Early Warning Indicators And The Potential Role Of Income Inequality And Credit Booms In Financial Crises Essay.

1. What is the role of income equality in determining financial crisis?
2. How is aggregate expenditure able to signal early signs of economic downturn in a country?
3. What predictions of financial crisis are suggested with “boom and bust cycles”?
4. What is the impact of “current account imbalances in determining” financial crisis?
5. How well the changes in the deposit system and global imbalances able to predict financial crisis?
6. What is the role of budgetary crisis in determining financial crisis?

Literature Review

In modern economics financial crisis is seen as a recurring phenomenon. The crisis of “2007-2009” was a stark recall of the “financial crashes” which shook the world by surprise. The various types of the cause of the academic interest in the financial crises is considered with the historical events. The discourse of the study aims to find whether early warnings of the financial crises may be identified. The investigation of the research will rely on the previous work of the economists and policy makers in recognition of the crises. Additionally, the early warning indicators of the financial crisis is observed with the work of these “economists” and the policy makers with the prevention and development of the crises. Despite of the several theories, there has been no “consensus on whether the macroeconomic or the financial factors” has a significant role in the prediction of the financial crises (Wang and Hua 2014).

In general, inequality in the income is identified with the increasing attention of the “economic research in including the economic growth, political economy, schooling and saving of the behaviour”. The concept of income equality was depicted with an augmented emphasis before the crisis of “2007-2009 (as it was before the Great Depression)”. This concern has remained constant in the developed economies. As stated by Lee, Sameen and Cowling (2015), the potential role of driving the financial crisis has remained doubtful. Some of the other papers on the income equality is further able to focus on the increase in the inequality and the probability of the financial crises or impacts of credit booms. In other empirical research the income equality is assessed to be consistent with the ingredient in growth for the bank loans or the developmental activities during the financial crisis (Almamy, Aston and Ngwa 2016).

In terms of the financial aspect, credit boom was identified as the key contributor for the “financial crises in developed countries over the last 140 years”. A more recent study has focused on the mortgage lending for the households, which has increased to a significant level in the last century. The linking of the abnormal credit growth is recognised between 1970 and 2007. However, in this period the linkages in the abnormal credit growth is observed in only out of the three financial crises. Henceforth, credit boom by themselves are considered as an insufficient prerequisite for the financial crises. The study has been able to take a further step in understanding the relative role for the financial predictors and real roles driving the financial crises. The evaluation of the probability of the financial predictors is seen to be considered with the predictive “power of a broad set of potential” macroeconomic and financial factors over the next 100 years. Instead of imposing a strong priori restriction on the empirical model, the main emphasis is employed with general to specific model selection. This seen with the determination of the predictive relative factors. The research study is able to employ a “general to specific model selection process” for the determination of the appropriate predictors and the existing lags (Adebambo, Brockman and Yan 2015).

Methodology

The inclusion of the income “inequality and credit booms” along with other relevant economic factors are able to clarify the occurrence of the “financial crises” included with the collapses of “asset bubbles, deregulation, financial innovations, movements of real interest rates, deposit insurance schemes, growth of the monetary base, and current account”. The research study is also able to evaluate the potential interactions among the real and the financial factors which are seen to be evident with case of income inequality. Empirical research has provided a compelling evidence on the income equality with the primary drivers and the upsurge in the “household debt” in the “United States during 1980s and 1990s” (Doogar, Rowe and Sivadasan 2015). Some of the other empirical evidences is further able to show that inequality may increase the leverage among the middle-income and poor households due to the consumption smoothing brought by borrowing against future incomes. The connection of these studies with the relevant findings has stated on the credit boom literature implied with income equality which may be a result of the actual real “side cause of risk of financial instability”. This risk is directly attributable to the credit bubbles. In arguing of the rising nature of the inequality led by redeployment in the form of “subsidized housing finance” housing boom and subsequent crashes are taken into consideration for the discussion (Ahrend and Goujard 2014).

The important considerations of the paper are able to include the dataset of 14 developed countries over 1870-2008, which is provided by other empirical research by “Karolin Kirschenmann Department of Finance, Aalto University School of Business”. This study was published in April 29, 2015. The main research will be based on secondary sources along with qualitative analysis in terms of the depictions made in this research paper for the assessment of financial crisis. The differentiating aspect of the study has included the work in two significant behaviours. Firstly, the “predictive power needs to be” distributed among larger set of variables” and examining these potential predictors and lags in the joint model. Secondly, the study has employed a methodology for flexible and “general to specific model selection” between the various predictors “without imposing restrictive assumptions on the channels through which, income inequality impacts the risk of financial crises” (Persakis and Iatridis 2016).

The main purpose of the paper has considered the “long-time series” which has exploited to achieve the relation of the long-time series for determining the roles in different crises situations. The “long-time series” format is important for accomplishment of the out-of-sample forecasts. The main considerations for the long-term costs have gained the importance since 1980. During this time when financial liberalization began, the data indicators were based on the consideration of deregulation and availability of the comparative short time periods. The paper has also examined the robustness check between 1962-2008 which was available from the data on the size of the U.S. joint fund industry. This is identified as a major indicator of the factors leading of the financial innovation and deregulation which are available for shorter time periods.

Results

The discourse of the study has examined the in-sample robustness “checked for the 1962-2008 period”. The availability of the “data on the size of the US mutual fund industry” is considered as an indicator for the investment for the “innovative and riskier investment classes”. The results have been able to confirm the entire sample findings. Moreover, the results have depicted that a larger U.S. mutual fund industry has been depicted with a higher financial risk prior to the financial crisis.

The main objectives set for the research are enumerated below as follows:

• Contribution of macroeconomic factor such as income equality in financial crisis
• Studying the role of aggregate expenditure in terms of financial crisis
• Role of economic expansion during boom and bust cycles in predicting the financial crisis
• Global Imbalances contribution to financial crisis
• Impact of Budgetary crisis on financial crisis

The study is segregated into five chapters. The first chapter is able to introduce to the topic along with the research objectives, purpose, background and research questions. The second chapter has organized the associated literature and the way it is able to motivate the predictor variables in the data. The third section has outlined the methodology for conducting the research and the fourth section has presented the results. The fifth section has concluded the paper.

As stated by Borio (2014), aggregate expenditure is identified as the measure of the national income. This is seen to be defined as a present value for all the “finished goods and services present in the economy”. The consideration of the “aggregate expenditure” is considered with the “sum of the expenditures undertaken in the economy” and the factors considered in a particular time frame. This is further referred as the expenditure which are incurred on the “consumer goods, planned” investments and expenditures made by the government in the economy. The situation in the open economy is included with the difference among the imports and exports. The investment expenditure is also

The important considerations in the seminar paper has been able to link the systematic nature of the banking panics caused in the business cycles. The strongest signal of the recession identified with the decline in the investment expenditures. The nature of the investment is able to reflect the total level of the “aggregate demand” for the “capital goods in the economy”. Furthermore, the nature of the investments may be able to affect the likelihood of a crisis. The money available in the economy is seen to be invested in a productive manner rather driving the consumption. Therefore, the accounting of changes in the “real gross investments” is considered in the empirical analysis (Gilchrist et al. 2017).

As per Rey (2015), the bust and boom cycle is considered as the “process of economic expansion” and contraction which has occurred repeatedly. The “boom and bust cycle” is considered as the key characteristics for the capitalist economies. In case of a boom cycle, the economy is discerned with plenty of jobs and market can bring high return to the investors. The subsequent study in bust can include the consideration for economy shrinks. in such a situation people losing their source of income and investors lose money. The boom and the “bust cycle” acts as the key factor for the variations in the time and severity.

Conclusion

It needs to be further discerned that the main idea of the financial crisis is discerned in terms of the financial crises which is determined by credit boom and bust cycles. This has “long been stipulated in the literature”. In the recent studies there are several types of large credit booms which are related to the financial crises. The improved influence and simultaneous decrease in the lending standards has introduced several fragilities in the banking system and made it more vulnerable. The measurement for the “evolution of credit in each country” has been depicted with the changes in the real bank loans. In general, the bank loans are not scaled as per GDP, as such a scaled measure is able to include the several types of the considerations which is seen to be related to the proxy for the nature of the financial systems rather than including the proxy for the financial crisis in the sample developed in the economies (Reinhart, Reinhart and Trebesch 2016).

The various types of the similarities among the previous and recent crisis is able to include the asset price booms. The increase in the asset price can consider the increase in the lending as per higher collateral values, which has further increased the asset price. Once the spiral activity stops, the households and the firms are seen struggle in terms of the paying back the accumulated debt. This consideration is able to include the type of asset price boom and eventually, which is able to “threatens the stability of the financial system”. This consideration is detected in the US and various countries in Europe in the runup to the recent crisis (Ho et al. 2016). The “tech bubble in the end of 1990s and beginning of 2000s did not result in massive systematic financial crisis”. The result of the empirical research there had been several types of the other constraints which are seen to be related to the different type the other aspects of the financial system. This consideration is identified in U.S. and several European countries in the recent crisis. The “tech bubble in the end of 1990s and beginning of 2000s did not result in massive systemic financial crisis”. The empirical evidences have accounted for booms in the asset price and changes in the real value of the stock market indexes (Akins et al. 2016).

  As per the depictions made by Liu (2015), it needs to be understood that the various type the imbalances in the current account is identified with short-interest rates which may be contributed as per the development of the financial crises. The various types of the “capital inflows may lead to the stock market bubbles and excessive expansion in terms of the domestic credit and inflationary pressures”. The change in the real value of the current account is considered with the measures of the international capital flows which depicts the environment with lower interest rates and increasing mortgage lending and booms in the housing price which will ultimately lead instability to financial inconsistency. The increasing interest rate may hurt the balance of the bank’s balance sheet. This is done in case the banks cannot increase the banks are not able to quickly increase their rates for lending. In case the interest is passed to the borrowers, this would lead to increase in the “non-performing loans and the risk” in terms of the moral hazard on the borrower’s part. In the empirical analysis is able to account for the real “short-term interest rate” (Arthur, Tang and Lin 2015).

The recent studies in the literature has been developed as per the theories and arguments to show the various instances of income inequality which may be able to contribute to the financial “instability and increase the likelihood of the crisis through various channels”. This is considered with the emphasis on the various channels such as “credit and asset price booms or current account imbalances”. These channels will be able to emphasize on the asset and credit bubbles which might be able to asset in developing the actual results for the real cause.

There has been number of studies with the “body of literature” which has developed the concepts and influences on how the “income inequality” has contributed towards the financial instability. There has been significant nature of the increasing likelihood of the crisis through several channels. These aspects have been seen with “channels such as credit” and “asset price booms or current account imbalances”. The” channels are able to accentuate that the credit bubbles” and assets have been actually able to develop the results from the real causes (Sun and Wang 2015).

In several instances the increasing nature of the “inequality forced” by the US politicians is able to enact measures for improving the situation of “low and middle-income households” for avoiding and losing the voters.  The redistribution in terms of the subsidized housing finance was expedited. This provision is also seen to be inexpensive with the consideration of the “mortgage lending” together taken with the “concurrent deregulation of the financial sector”. This has intern led to housing boom and succeeding crashes (Proença, Laureano and Laureano 2014).

The assertion of a more direct link among the collective “debt levels” does not depend on on the specific political systems. In a “closed economy”, the crisis consideration is seen to emerge endogenously. This is seen because of “rising income inequality” due to low-income and middle-“income households” for avoiding “losing them as voters”. There is significant redeployment in form of the “social security payments or increased taxes”. The redistributions in terms of the “social security payments” and “increased taxes for the rich are impossible” solution in the US political environment and redeployment in form of the subsidized housing finance as expedited. The provision of the cheap lending for mortgage taken into consideration with concurrent deregulation of the financial sector observed with housing boom and subsequent crash (Armantier et al. 2015).

A more direct link is taken into consideration with the connection with the increasing debt level and income inequality which does not rely on the “specific political system”. The closed economy model has showed several crises which emerged endogenously because of increasing income inequality due to “income and middle-income households seeking” to preserve appropriate consumption must “borrow more as their real wages decrease”. The extended model in the “international environment” with the open economy is seen with the rising inequality and risk of financial crisis as it “endogenously leads to credit expansion, increased leverage and increased current account deficits” (Milne 2014).

In addition to this, there are several types of the other arguments which is able to show that the increasing nature of the discrepancy in the income equality is able to be depicted in terms of depressed aggregate demand. This is seen to be induced with central banks in maintaining lower interest rates and contributed with gathering of private debt. The assistance from the increasing inequality is able to search for the higher form of the yields taken from the investments and driving the asset bubbles. The increase in the “non-performing loans” after the “burst of the asset bubble” which is exposed with the banking sector to the risk of a run (Persakis and Iatridis 2015).

The additional variables are seen with the prediction of the financial crisis in the previous studies. In addition to this, central banks are seen to use the additional variables which is shown with the help of the prediction of the “financial crises” in the previous studies. Firstly, the central banks are able to steer the aggregate the credit through the inclusion of the monetary aggregates. The control of the potential impact on the monetary aggregate on the likelihood of the financial “crisis using the change” in the broad money. Secondly, the administration debt is seen to be relevant to the “financial sector”. The government “short of funds may postpone” the measures aimed at strengthening balance sheet of the bank. However, in case the government is prepared for support the “country’s banking sector despite of the budgetary problems”. The problem may not be able trust such an endeavour which may turn the trigger a bank run (Ozturk and Sozdemir 2015).

Thirdly, it needs to be seen that the deposit insurance is related to the preventing the depositors from “threatening the stability of the financial system”. The presence of the deposit system is considered with the introduction of the various factors linked with the deposit insurance thereby introducing a moral hazard to the bank managers. There may be several instances when the deposit insurance is designed and introduced for the introduction of moral hazard for the bank managers and “knowing that the deposit scheme” will be able to pay the depositors in case the risky investments go bad. In addition to this, there may instances when the deposit insurance may be facing disadvantages which are associated to the moral hazard on the bankers. The existence of the deposit insurance may be considered with the likelihood of the financial crises despite of the intended stabilizing effect. In several empirical researches the consideration of the deposit insurance is seen to be bead on “binary variable” which “equals one in all years which a country has in an active deposit insurance scheme” (Saghi-Zedek and Tarazi 2014).

This situation is considered as the situation when some countries are having more assets in compare to the other countries. In general, the different types of the imbalances pertaining to the current account deficits are associated to the which has zero value for the “inflows and outflows of the capital” which may be negated by each other. In case the current account is consistent with the deficits for the current periods then it will lead to in-equilibrium. As per the definition the current account and the foreign assets of the country must become zero, this will lead to other countries develop indebted with other nations. In recent years, the various types of the global imbalance are able to show the various type the factors related to the different types of the concerns related to rest of the world. in addition to this, the United States is identified with the various types of the long-term benefits which are seen to be linked to the advanced economies (Görg and Spaliara 2014).

The conceptualization of the global imbalances is not new. There have been several periods which is able to relate the different type the considerations for the global imbalances had originated during the “years 1870 to 1914 (a former era of financial globalisation)”. The “massive flows in the capital flew from the core countries” of “Western Europe to the countries of recent settlement overseas (especially the Americas and Australasia). Current account surpluses run by Britain, Germany, France and the Netherlands reached approximately 9% of GDP, while for the destination of the flows. (Argentina, Australia and Canada) the deficit exceeded 5%”(Bhagat and Bolton 2014).

As stated by Filip and Raffournier (2014), A more technical definition pertaining to global imbalance refers to the inconsistencies in the "external positions of systemically important economies that reflect distortions or entail risks for the global economy". The external position is further linked to the flows in the current account which are directly linked to the various types of the other factors such as current account flows and net foreign assets of the countries. This is taken into consideration with the “changes in prices of those assets and liabilities are zero”. This consideration is identified to be based on the various types of the factors which are directly related to the accumulated sum of the past net current account flows (Young 2014). The systematic important in the economies are seen with different types of the consideration related to the “economic blocks running the imbalances which are relevant to the world market operations”. These examples are included in terms of China, Euro area and United States. The reflection of the distortions is taken into consideration with the various factors which linked with the external imbalance. This is able to signify external imbalance do not originate from market distortions and would fall under the category of the market distortion (Waelti 2015).

The research study has complimented the collection of primary source of data from 14 developed countries between 1870 to 2008. Some of the data are included with countries like “United States, Canada, Germany, France, Denmark, Italy, Japan, Netherlands, Norway, Spain and Switzerland”. The dependent variables from the previous studies are considered with the financial crisis episodes and combination of the various other relevant datasets. The inclusion of the dataset is taken into consideration with financial crisis observations. The main form of the observed dependent variable Yit was taken into consideration with value one (Yit=1), in case there was any incidence of financial crisis in a country. In this case “i” is the country and “t” is the country (Kirschenmann, Malinen and Nyberg 2014).

The financial crisis is defined with the consideration of the various experiences taken from the country’s banking sector. This is identified with sharp increase in the default rates accompanies with the large losses of the “capital leading” to the “government interventions” from “bankruptcy or forced mergers of financial institutions”. The main assumption of the crisis is considered in the year when the country was facing the financial crisis.

The data collected from the previous research studies have followed cluster probability sampling method. In this technique the independent variables such as “real bank loans, top 1% income earning group, gross r. investments, current account, money inflows, government debt, r. stocks, s.t. real interest and deposit insurance” are selected from the various countries. The application statistical tools are implemented for analysis and classified them into separate groups. Some of these groups include “summary statistics of the predictive variables”, “In-sample results, full sample period 1870-2008”, “In-sample results, loan growth and income inequality, full sample period 1870-2008”, “In-sample results with several predictors, full sample period 1870-2008”, “In-sample results, post-WWII period 1950-2008”, “In-sample results with several predictors, period 1950–2008”, “In-sample results with selected predictors, period 1962–2008”, “Out-of-sample AUROCs for the sample period 1980–2008” and “Out-of-sample AUROCs for different sample periods” (De Haan 2017).

The main research approach selected for the study is seen with a qualitative research study based on secondary sources. This approach will act the re-analysis of the quantitative data already collected in previous research conduct on this topic. However, this will also consider some of the unique factors for the financial crises such as contribution of macroeconomic factor such as income equality in financial crisis, aggregate expenditure impact on financial crisis, role of economic expansion during boom and bust cycles, Global Imbalances and Impact of Budgetary crisis on financial crisis. The results obtained from the previous studies are able to relate the aforementioned factors which were responsible for the crisis.

The study has included the analysis from previous studies conducted to measure whether financial risk is based on real factors or financial factor, conducted by “Karolin Kirschenmann, Tuomas Malinen and Henri Nyberg”. Some of the other sources of secondary analysis are taken from the study on the “impact of deposit insurance” effect on banking risk written by “Anginer, Demirguc-Kunt and Zhu”. To link the impact of income inequality with financial crisis the study on "Economic crises and inequality" written by Atkinson and Morelli has been duly referred for the predictions made in this study.

The main datasets selected for the previous research study were based on sources such as real bank loans, debts by the government and the indexes relating to the stock market. Moreover, the considerations of the data are seen to be based on the information obtained from the current account deficits and investments suggested from the “World Bank World Development Indicators,  on real GDP per capita from the Maddison Database of the Groningen Growth and Development Center. The data on the introduction of deposit insurance is taken from the World Bank Deposit Insurance Database” (Doogar, Rowe and Sivadasan 2015).

The consideration of the variable with the “top 1% income” share is considered from the top “income database” on a study conducted by Alvaredo. The use of “top 1% income share” is further seen to be based on the varied nature of the other requirements which are considered for the share of two reasons. The first step is taken with calculating the synthetic indexes relating to accurate country specific information. This is observed with the indexes which may be unreliable due to the existence of the various types of anomalies and inconsistencies which are likely to be country dependent. In addition to this, the top income shares measure is constructed with the use of same methodology and raw material for each country. The second top 1% of the income share is depicted with the consideration of the different evaluation which is seen to be based on the availability of data for a “time span of approximately 10 years” in most of the sample countries. On the other hand, the consideration of the synthetic indexes is seen to cover the past 40 years which often includes the “missing observations” for several years. It is further identified that the financial crisis is identified to be infrequent in nature especially in the developed countries. The use of synthetic index would be further able to restrict the analysis to a considerable level which may lead to “incomplete conclusion”s. The focus on the study is given with the comprehensive picture of the roles which are different from the financial and the real factors observed in the run-up to the different crises (Luchtenberg and Vu 2015).

The first table has depicted the number of observations which are available for each of the predictor variables and emphasize on the fact that the panel is unbalanced. In addition to this, the different types of the issues from the empirical research is considered with the study of the subsample periods which shows more yield balanced panels. The Table 1 is able to depict the summary of statistics for all the predictive variables.

The implementation of the main statistical model is described with the fixed effect panel with the logit model and selection process using throughput the research paper. The dependent variable is binary and it is recognized with the “binary response” model rather than the panel model designed for the “continuous dependent variable”. The “latter models” are further depicted with the problems relating to the “binary-dependent variables”. For instance, the “financial crisis probabilities” may not necessarily have been taken into consideration with the unit interval. The use of this model has suggested the country with “fixed effects” to control all the “time invariant heterogeneity” factors at the country levels. This type of the model is depicted with additional advantages which are derived from variations within the country wise crises predictions, thereby eliminating the potential bias which stems from the data reporting standards across these regions.

The important data is also seen to be observed in terms of the various types of the other facets which are considered with the experience by the country’s banking sector. It is understood that the sharp increases in the default rates are accompanied with the losses in the capital pertaining to the interventions made by the government. Some of the other considerations are also recognized with the forced mergers of the financial institutions and bankruptcy related to the details of the crisis data.

Dependent Variable

• Financial Crisis

Independent Variable

• Real bank loans
• Top 1% income earning group
• Gross r. investments,
• Current account
• Money inflows
• Government debt
• stocks
• t. real interest and deposit insurance
• Income Inequality
• Budgetary Crisis
• Global imbalances

This study will analyse the statistical interpretation done in previous research studies, which has showed the likelihood of events with the application of logit model and “McFadden's pseudo-R squared”. This logistic regression model is fitted into the study to evaluate the findings for the values “which maximize the likelihood of the data which have been observed”. The logit model follows the prediction made for P(Y=1) for the individual subjects. This would require “P(Y=1)≈1 for those subjects who did have Y=1, and P(Y=1)≈0 for those subjects who had Y=0”. In this case the probability of “Y=1when P(Y=1) ≈1 is almost 1, and similarly the probability of seeing Y=0 when P(Y=1) ≈0 is almost 1”. This will be signifying the likelihood for each observation close to 1.

In addition to this, “The area under the receiver operator characteristic (AUROC)” has stated on the “summary statistic for the goodness of a predictor in a binary classification task”. This prediction illustrated that the predictor will rank a randomly chosen positive instance higher than a negatively chosen random event.

The important form of the assumptions of the study has included the different type of the instances which are seen to be associated to the beginning of the crisis (Y=1) to the time when the country falls into the crisis, otherwise 0. In other words, this expression was denoted as

Yit = 1 ------------------------------------------------------------------------------------------------------(1)

It needs to be further noted that this value will be 1 only if “there was a financial crisis in country i at time t”. In other situations, this value was considered as 0.

The initial model has explained the main consideration which is associated to the use of selection approach and multivariate analysis. The employment of the selected methodology has allowed for a more “flexible general-to-specific model selection” between the predictors without the imposition of the restrictive assumptions on the channels which are impacted from the risk of financial crises. The research study has been also able to allow for the examination of the larger sets of variables and potential predictors in the joint models.

The inclusion of logit model is considered with the fixed effect from the “panel logit model conditional on the information set at time t-1 (denoted by Ft-1) including, e.g., the relevant predictive variables, Yit has a Bernoulli distribution”:

 “Yit|Ft-1 ~ B(pit); i = 1…. ,N,        t = 1,…..,T” ---------------------------------------- (2)

This signified Et~1(-) and Pt~1(-) for denoting the “conditional expectation and conditional probability” given with conditional expectation for the information set of Ft-1. The conditional probability is respectively considered with Yit taking the value 1. The financial crisis is seen at the time t in country i may be stated as:

Pit = P t-1(Yit = 1) = Et-1(Yit) = ^(πit) ---------------------------------------------------------- (3)

In this equation πit was the linear function of the variables was assumed with the information set Ft-1 and ^ (-) was considered as the logistic cumulative distribution function.

^(πit) = exp (πit)/ (1+exp (πit)) ------------------------------------------------------------------- (4)

The assumption of the linear function (πit) was taken from the Schularik and Taylor which denoted

(πit) = Wi+b1(L)x1it+…. +bK(L)x Kit---------------------------------------------------------(5)

In this particular case bj(L)x jit = bj1x ji, t-1+…..+ bjpxji,t-p where j=1…. K, the country specific vector Wi comprises the deterministic terms such as “country-specific dummy variables”. This has showed the “possible heterogeneity” among the countries. The model (5) is seen with the use of lag-polynomials for the different predictors having the predictors having the form:

bj(L) = bj1L +….+bjpL p, j=1,…..,K,------------------------------------------------------------(6)

In this case L is considered as the usual lag in the operator. In other words, the research is able to clearly allow for the opportunity of the predictive power of the different predictors which are distributed among several lags. The use of same lag of length p in the equation (6) for all the predictors is considered only for “notational convenience and can be easily relaxed in the practice”. “The notable polynomial (6) began with the lag (i.e. these lags are seen to be included in the predictors as included in (5))”.

The logit model is able to conveniently estimate the “maximum likelihood (ML)” methodology. The “conditional probabilities” were constructed in (3), which allowed for the likelihood function to “obtain the ML” approximations by the use of numerical methods. In this particular method the numerical “cross-sectional units” N is considerably small, “whereas the length of the time series T is considered to be relatively long”. Due to the consideration of this model ML estimator may be understood as per “quasi maximum” likelihood estimator in usual way. Henceforth, the accounting of possible misspecification may be used with robust standard errors for the estimation of the coefficients all along the study.

As the available data is seen to be “highly unbalanced”, there needs to be special consideration paid for the selection in the entire analysis. In this particular case, the “predictive variables” included in the model is depicted with the “number of observations” which is seen to differ in various types model specifications. Due to this, the usual information criterion is based on the model selection procedure which are not directly applicable. Nevertheless, the model selection process is seen to be taken into consideration with the unbalanced panel. The previous criteria for the prediction of the literature is able to consider the different variables which are seen to be available over the various time spans (Haven et al. 2016).

The “model selection” employed in the study was separated into two parts. The first part has examined the predictive variables which needs to be included in this model. Secondly, the determination of the individual variables needs to be taken into consideration with the varying assumption with the use of analytical power. In general practice, the optimal lag length p is considered with the use of predictive power. However, the lengths p for the diverse predictors are unknown. The main assumption of the upper bound such as pmax is considered with following successive “general to specific model selection method”, which are essentially seen with the use of same procedure proposed by the researchers mainly for “vector autoregressive” model.

The maintaining the lag structure and the predictive model is taken into consideration with the parsimonious possibility for the individual variables. This was included with “lags up to six (i.e., pmax = 6)”. The estimation of the parameters is seen to be taken into consideration with the t-ratio related to the longest lag coefficient which was less than 1.65. It needs to be further considered that the procedures for the t-ratios needs to be maintained for the lengthiest lags which are larger than the assigned threshold.

The “predictive performance” model is able to include the evaluation of the “well-known” goodness of the fit measures. The inclusion of the “dependent variables” is seen with the various substitute procedures which are roughly analogous with the coefficient determination R² applied in the linear models. One such alternative for the R² is considered with the measurement

Pseudo- R²= 1-LU/LC--------------------------------------------------------------------------------- (7)

This expression has considered LU as the determined value of the projected unconstrained loglikelihood function and LC is seen as the “constrained counterpart” in the model containing only a constant term. This form in (7) is able to ensure that the values 1 and 0 is able to correspond with the “perfect fit” and “No fit” which is seen with the intermediate values and considers roughly the same interpretations which are used in the linear models as per R².

Some of the other criteria for evaluation is considered in receiving operating characteristics “(ROC) curve”. The “ROC curve” “methodology” is considered to be common in the evaluation of the criteria for binary estimates and the outcomes in other sciences. The inclusion of the recent examples from the economic and the financial application. The evaluation of the ability of the model is able to state on the financial crisis found with “logit model are P>C” for some of the threshold value c and vice versa. The ROC curve has described the possible combinations of the inclusion of the true positive rate TPR (c )=P and the false positive rate is considered with the F PR( c) space described with the classification of the capability of the model. In this application the range of financial crisis period is seen to be taken into consideration with the single threshold C is complex. Henceforth, the believe of the ‘ROC methodology” is seen to be more sensible than the method concentrating with the results based on the cut-off c (Lehkonen 2015).

The area in the “ROC curve (AUROC)” was used to illustrate each model’s ability to “distinguish between signals for financial crises yit = 1 and normal periods yit = 0”. The ROC curve defines all likely amalgamations of the true positive rate” TPR(c) = P(ˆyit = 1|yit = 1) and the “false positive rate FPR(c) = P(ˆyit = 1|yit = 0)” “that arise as one varies the probability threshold c”. The threshold c can vary from 0 to 1; the “ROC curve is traced out in TPR(c)&FPR(c)” space describing the classification ability of the model”.

The summarization of the classification of the ability in the given model is considered with the AUROC model which has been duly stated in summary statistic. The value of the AUROC=0.5 relates to the coin toss. This shows that “the model has no predictive power at all”. In contrast the value 1 will be able to signify the perfect fit. The higher form of the value is able to indicate the increased classification ability.

The results from the empirical research was able to signify the objectives of the study which is were included in the analytical influence for the financial crisis period. The full sample results are considered with the panel of countries at the time span of the data considered from 1870 to 2008. This has been able to serve as the benchmark for the maximal amount of information.  The subsequent analysis of the shorter sample periods was considered with the robustness check with the balanced panels. The study has included the robustness check with the inclusion of more balanced panels. The next consideration is done with the out-of-sample forecasting experiment for the different types of the robustness in the results. This is done to gain a deeper insight in the predictive power of the various facets which may impact overtime (Persakis and Iatridis 2015).

The estimated model is contained in the country-fixed effect with focus on the control for the time variant heterogeneity in the country level and focus on the investigation within the country variation. In addition to this, we have   not been able to include the analysis within the country variation. The time-fixed effects are also not taken inti consideration in the dependent variables with actual changes in the values. The financial crises are rather considered to be rather uncommon in the developed economies (Hwang 2014).

The first estimation of the fixed-effect of the logit model is taken into account with the use of “predictive variable” at a time. The selection of the “optimal lag length” of p for each variable is used with the variable using “sequential testing approach”. The predictor for each table is displayed in terms of the “optimal lag length and the values pertaining to pseudo-R2 and the AUROC”.

The AUROC statistics are obtained above 0.5 for all the predictors indicated in the models which may be distinguished in the crisis and “non-crisis periods”. This is reliable with the univariate results confirming with the increase in the overall credit and acts as an important predictor for the warning signs of the “financial crises”. Some of the other studies are further able to emphasize on the singular role of the credit bubbles and results suggested with univariate analysis. The important consideration which seen to be taken into consideration for the financial crisis appeared with a reasonable level in light of the Gorton’s measures with approximately” one-third of the crises” which took place “between 1970 and 2007” and the credit boom which was seen during the run-ups (Adebambo, Brockman and Yan 2015).

The next step was considered with the examination of the effects which led to the joint inclusion of the predictors income of the model. For instance, the financial variables are consideration with the financial variables which may present the real factors predictions. The second table to the study has been able to discuss about the different type the factors which are responsible for income inequality and bank loans. The table 3 report has considered the full sample period. The Column 1 and column 2 is able to include the models for the real bank loans and this is considered with the top 1% of the income predictors separately. These models can replicate the various types of the considerations which are considered as per the actual estimated coefficients included in the lags and comparison facilitated with the models comprising the forecasting factors (column 3) (Waelti 2015).

The “column 3 of the Table 3” has depicted that the “variables” are seen to be noteworthy nature of the predictors which are estimated in the period of financial crisis in a “joint model”. The separate tests for the predictive power is defined with the lags in the variables and these are further seen “significant at 1% level like the univariate results reported in column 1 and 2. The values considered in the pseudo R2 and the AUROC” are taken into account with the larger specifications from the single variable models. The “income inequality” has additional predictive power over the bank loans (Bordo and Meissner 2016).

In the “column 4 of Table 3”, we are able to interact with the real bank loans which are take into account in terms of income inequality and growth in loan reinforced with the drivers of crises. In this study the reporting is mainly done as per the first lag and interaction term as the other lags are not able to meaningfully predict the crisis. The interaction is seen  positive and this has implied that the growth in the bank loans are more probable to “lead to a crisis” when the income equality is high and vice versa (Askari and Mirakhor 2015).

The evidence from the income inequality is able to focus on the various type to relevant factors which are taken into account with the role of the real bank loans which is seen to be limited. The estimations have been further able to signify that table 4 remained qualitatively unchanged. In case the bank loans are considered to be excluded from the model. The results are taken into consideration which are in contrast with the evidences used in the dataset. In this study, it was stated that the contrast on the variety of the predictors were based on the joint model associated to the general to specific model selection (Bucher-Koenen and Ziegelmeyer 2014).

The table 4 stated on the different type of the evaluation on the inequality in income which are having the predictive power associated to the growth in the loan. The probability of the expectancy of the financial crises was accounted with the “current account deficits”. The negative “first lag” of the real stock has indicated that, once there is an” asset price boom” the probability of the financial crisis has increased. In addition to the monitoring process of the loan-term evolution of the stock prices, some of the sources of data are considered to be useful for the policy makers for the prediction of financial crises. The significant effect of the “short-term interest rates” is accounted with the growth in the credit and increase in the likelihood of the direct predictive power (Carbó-Valverde, Rodríguez-Fernández and Udell 2016).

The full sample results predicted that increasing “income inequality” is associated to the probability of the financial crisis. The findings are constant with the anecdotal indication from the two forecasts of “crisis in the US, the Great Depression” and a more recent crisis, were preceded by “high income inequality”. The results have been able to consider the “predictive power” in the models clearly distributed in different predictors and their lags. The results are also able to support the academic literature which shows “income inequality” is one of the factors for causing financial crises and “rejects the suggestion that income inequality works solely through credit booms” as discussed by Klein (2015) and Görg and Spaliara (2014).

The study conducted by Marco Terrones and Enrique Mendoza revealed the characteristics of “credit booms” in the “industrial and emerging economies”. The predictions made as per the macro data depicted a systematic connection between the credit “booms and economic expansions, rising asset prices, real appreciations and widening external deficits”. The micro data on the other hand has been able to depict a robust association of the “credit booms” and leverage ratio, banking fragility and firm values. It has been further discerned that the credit booms are larger in the emerging economies. This trend is evident in the non-tradable sector. In most cases “the emerging markets crises are associated with credit booms; and credit booms in emerging economies are often preceded by large capital inflows but not by financial reforms or productivity gains”.

This is able to show the robustness in the two tests. Firstly, the study is able to replicate the previous analysis of the WW2 sample for examining the “predictive power” of the “real and financial variables” which are dependent on the “sample period”. The “post WW2” period has considered the importance of the robustness among the "two eras of financial capitalism".

Some of the other predictors are further able to identify that the factors affecting the top 1% income share is only available in shorter time. This panel is therefore considered to be “much more balanced in the post WW2 analysis” and compared with the effects between the other variables. Secondly, the two further channels are able to state on the effect of the inequality and its impact on the financial crises. The housing price booms have been able to depicted with increased income of the household income and risk-taking potential. The gauges for both the “channels” are obtainable for a shorter time span than those which are employed in the primary analysis. The robustness factors are considered with shortest constraint from 1962 to 2008 (Persakis and Iatridis 2016).

The only concern in the different type of the previous results are seen to be based on the 1% income share which are having the superiority of the variables from the “sample period of different variables”. The table 5 is able to current the models including one predictor at a time post WW2. The total number of observations are seen to be much closer to another variable. The results have been able to reveal that the inequality in the income is best considered with “pseudo R2 and the AUROC”. By the consideration of other variables real bank loans act as useful predictors although there may be several other variables having higher predictive power (Milne 2014).

The Table 6 has estimated the consequences for 1950-2008 having the various predictors in the model. Selection of the sample studies are further able to highlight the importance of the stepwise model which has considered the appropriate model selection. The robustness in the study has been further able to include the different types of the consideration which are seen to be based on the discoveries for the full sample analysis. The primary changes are able to consider the existence of the deposit insurance schemes with the predictive power of WW2 sample. On the other hand, the short-term considerations for the interest rates. The introduction of the “deposit insurance” increases the chances of the “outbreak of a financial crisis” which is indicated with the inherent moral hazards and problems which may “interfere with the intended stabilizing effects of the deposit insurance” (Lee, Sameen and Cowling 2015).

The results have been also able to consider that the “income inequality” is seen as a contributing factor in the financial crises and these are above the “credit growth, current account deficits, real interest rates and stock price booms”. The section 2 of the report is able to suggests on the inequality which is considered with the expansion of the crisis through the housing price booms and increase in the investment. This is considered with the increasing nature of the risky assets backed by the “high-income households” (Saghi-Zedek and Tarazi 2014). The testing of the effects of the “housing price booms” were considered from price data taken from the Bank for “International settlement” for the time of “1970-2006”. The accounting of the investments is further seen to be taken into consideration with the evaluations of the asset classes and the use of the data from the size of US mutual fund industry. This is taken into account with the total assets held in the “mutual as a share of CRSP market capitalization”.  The estimation from the restricted form of model is considered from the beginning of the variables which are depicted to show the variables through which the “income inequality” is expected to affect the likelihood of the crises. This model included the consideration of the various factors taken from the “real bank loans, housing prices and size of the US mutual funds” (Akins et al. 2016).

The study conducted by Alan Taylor was related to a “larger project with Maurice Obstfeld (forthcoming) on the historical evolution of international capital mobility”.  The depictions made as per this study stated that the recent globalizations have focused on the progression of the “international capital mobility” for the long run. The issues were seen to be examined using the “time series analysis” of the current account dynamics for fifteen countries in 1850. The inter war period has emerged as an era for low mobility in capital. The predictions made as per the savings and investments has been further able to suggest on the investment dynamics which had been conducive in terms of making sense of the regularly identified high correlation of savings and investment as per the historical data.

The results extracted from table 7 shows when we are able to control the various channels “through which income inequality may affect the likelihood of the financial crises”. The consideration of these results has been depicted with different channels such as “credit and asset” “price booms” or the “current account imbalances”. These channels emphasized on the different type the considerations which are associated to the “asset and credit bubbles might actually develop because of real causes” (Filip and Raffournier 2014).

Several types of the other arguments suggest that the impacts of the other studies may enact the procedures to improve the situation of low to “middle income households” and avoid losing the voters. The redistribution of such form of the social security for the increased taxes for the rich may be impossible to US political environment and redistributed in form of the “subsidized housing finance” which was expedited. This provision is further seen to be “inexpensive mortgage lending” “together with the concurrent deregulation of the financial sector” and this in turn led to subsequent boom and housing and subsequent crash.

In addition to this, a more direct link among the “income inequality” and the increasing nature of the debt is able to suggest about the different types of the factors which are responsible for specific political system. The closed economy model crises have emerged because of rising income inequality and the incidences leading to the real wage decrease. This is seen as the extension of the varied type of factors which are associated to depict the rising inequality and risk of financial crises as it has endogenously led to the credit expansion. The “increased leverage and current account deficits” are some of the other considerations which needs to be considered (Ho et al. 2016).

The different types of the other arguments are able to present the arguments which have led to depressed aggregate demand introduced with the central bank in maintaining of the low interest rates. This factor is suggested with central bank in maintaining low rate of interests and factors “contributing to the accumulation of private debt”. Simultaneously the benefits are described as per the increasing nature of the inequality search which taken into consideration from the high yield investment driving asset bubbles. The increasing nature of the non-performing bubbles is further based on the different types of the speculation of the risk-taking ability due to the consumption opportunities. These considerations are seen to be benefitted from the increasing nature of the inequality and risk-taking occurrences which are more likely (Reinhart and Rogoff 2014).

The completion of the pool, of data is seen to be taken into consideration with additional variables which can predict the “financial crises” in the previous studies. The “central banks” are able to steer the “aggregate credit” with “monetary aggregates”. The controlling of the potential impact is considered with the use of financial crisis with changes in the broad money. Secondly, the government debt is supported in terms of the banking sector despite of the existing budgetary problems. The common public may be facing several hurdles in trusting such an endeavour.

Table 9 results are able to offer the interesting insights. Firstly, none of the lags in the “real bank loan” are “statistically significant to the in-sample model” and the selectin of the data which are seen to be brought in the data of 1979. Therefore, it needs to be discerned that the “real bank loans” are seen to be omitted from the output of the sample forecasts from 1980-2008. Secondly, the top 1% if the oncome share is seen to be discerned with the main form of the evaluations based on the “predictive power” in all the respective time periods “(AUROC>0.5)”. Some of the other factors such as broad money and government debt have not shown any predictive power which the policy makers need to take into consideration with more awareness. This particular finding is able to suggest on the universally “strong predictors” of the crises which the “economists and the policy makers” needs to be pay more attention based on the range of factors. Thirdly, the predictive power of the variables is seen to increase with a more recent focus between “1990 and 2008”. Additionally, more variables have little or no “predictive power when the entire post WW2 period in the column 2 of the table 9 for the considered forecasting period”. Fourthly, dividing the recent decades into shorter time periods will be comprised with shorter time periods from 1999 to 2000 and from 2000 to 2008 (column 5). The main emphasis has been able to focus on the shorter samples of the period which may lead to incomplete conclusions (Boz and Mendoza 2014).

 A prominent example for the misleading conclusions are regarded with the drivers of financial crises which may be seen with shorter period is government debt. The variables were based on good forecasting power during “1990-2000 in column 4”, this may be explained with the “government debt crisis” which took place in Italy in the in the “beginning of 1990s”. Despite of this, “government debt” is not having any forecasting power in 2000-2008 period. The real bank loans are having the highest forecasting power in “1990s”. This is considered to be consistent with the “bursts of the credit and housing bubbles in Japan, Sweden and the U.K”.

  The out of sample forecasting data are underpinned with the importance of focussing on the real and financial variable for the assessment of the various forecasting period helpful in understanding the financial crises.

The estimations in “section 4.1 and 4.2” is able to suggest on the possibility for obtaining the statistically significant predictive power for the “financial crisis” in various developed countries. The primary interest is considered with the macroeconomic variables which are seen useful for predicting the crisis in general. The next consideration needs to be taken into consideration with the recent crisis periods.

The depictions made in the forecast for the financial crisis period is considered with rolling recreational analysis during 1980 to 2008. The model is able to estimate the data from the commencement of the sample time T with the use of information set FT. This done to construct probability one year in advance for the observation such as “Yit, i=1 ,…, N”. This procedure is considered to be repeated in individual year in the end of sample. This analysis led to amore more accurate contrast of the analytical ability of the variables as there is no future data included in the information set when estimating with the parameter model. This consideration is seen to act as the check for robustness against any potential over lifting of the logit models which are have been understood as per full sample prediction and subsample predictions (Armantier et al. 2015).

The table 8 of the report is able to report the results gathered from forecasting of the research data. The “out of sample AUROC” is used as the main criterion for assessment of the “forecasting performance” due to lack of pseudo-R2 measure. Therefore, the AUROC criteria and the associated tests are designed originally as per the in-sample predictions, on the other hand the out-sample analysis is seen to be predicting the power of the variables. Additionally, the column 1 of table 8 depicts the AUROC for the entire sample of information available for the 14 countries in the entire period of observation. On the other hand, the second column is able to focus on the “common sample” which consists of the observation used form all the relevant models (Proença, Laureano and Laureano 2014).

The initial seven rows are considered as the predictor variable. This result is able to demonstrate the growth in the loans which is taken with the “best out-of-sample, independent of the estimation sample”.

The associated study led by Schularick discussed on the “Credit Booms Gone Bust: Monetary Policy, Leverage Cycles and Financial Crises, 1870-2008”. As per the depictions made in this study the crisis of 2008-09 had focused attention on the “credit fluctuations, financial crises, and policy responses.” The interpretations as per this study the behaviour of “money, credit and macroeconomic factors” in the long run is “based on newly constructed historical dataset” from “12 developed countries over the years 1870– 2008, utilizing the data to study rare events associated with financial crisis episodes”. The various types of the evidences presented from the study had been able to discuss on the “leverage in the financial sector to increase strongly in the second half of the twentieth century”. This was depicted with decoupling of “money and credit aggregates”. In addition to this, the decline in the safe assets on the bank loans were increasing in nature. The response of the monetary policy to the financial crisis has been depicted to be more aggressive post-1945. However, the output costs for these policies has remained large. The important predictions as per this study has been also able to suggest that credit growth is strong predictor of financial crises. This is suggested with the fact that such crises are “credit booms gone wrong” and that policymakers ignore credit at their peril. The latter part of the study has been able to clearly suggest that it is only in the long run when the comparative data can be assembled.

Some of the previous research studies has been also able to suggest that the rising inequality within the advanced economies which are in contrast with the reduced inequality among the countries. Based on these interpretations, one trend in global income inequality was depicted with accelerating the global income whereas “another, opposing trend, appeared to stall”. Among the different countries, the inequality continued to reduce for decades (Christophers 2016).

Figure 1: Global Income inequality within country and between country

 (Source: Ft.com. 2018)

The country wise risk of income inequality is depicted with growth in advanced economies which suffered more deep and protracted collapse. It was further depicted that the emerging economies has a shallower fall in the economic growth during the crisis period. The overall estimation stated that emerging economies was experienced with a shallower or briefer economic growth at the time of crisis. This trend was followed in the developed countries which originated decades earlier (Bhagat and Bolton 2014).

Figure 2: Country wise risk of income inequality

(Source: Ft.com. 2018)

In addition to this the predictions made as per the Michael Kumhof in the study of income inequality and current account imbalances has identified that in case of higher income inequality and financial liberalization the conventional explanatory variables predictions are based on the larger “current account deficits” in a “cross section” of the advanced economics. The was based on a DSGE model in which the investors income share increases at the expenses of the workers and the workers “respond by obtaining loans from the domestic and foreign investors”.

Another study depicted that the financial crises of 2008 were viewed with several financial disruptions spread from the financial markets to the economy. The great depression was witnessed with the aggregate expenditure model. This model led to the understanding of the factors which led to the reduction of the economic activity in the early 1930s. The inclusion of aggregate expenditure model acts as the starting model for the GDP, which acts as the measurement of total production and total spending. The model has considered “real GDP = planned spending” this further equal to “autonomous spending + marginal propensity to spend × real GDP” (Almamy, Aston and Ngwa 2016).

It needs to be further depicted that the autonomous spending is considered as the intercept for the planned spending line. The main lessons drawn from this research was able to depict that the aggregate expenditure and core social spending led to greater risk of global recession and this increases the chances of weakening public finances.  As per the study conducted by “Jim Brumby and Marijn Verhoeven” on “Public Expenditure after the Global Financial Crisis”, many government agencies are seen to be likely coming under increasing pressure to lessen the financial requirements. In this situation the real temptation is seen on cutting the spending on investment as the first resort as this is easier than increasing the general tax rate or reducing the social expenditure. In case there is pressure on public finances, the Government may not only postpone the investment in new infrastructure but also reduce the spending to minimize the over social spending (Saylordotorg.github.io. 2018).

The financial crisis brought by the current account imbalances is studied with previous research conducted by “R Barrell, E P Davis, D Karim and I Liadze”. This study has been able to signify that the degree of the “global imbalances” as per the run-up of the subprime crisis was the test for the impact on the “current account balance” on the probability of the banking crisis in OECD since 1980. In most of the cases the variables set for the research neglected the early warning despite of the prominence in the theory and the case studies associated to the crisis. It has been discerned that the current account liquidity and variations in the house price are able to signify the patterns preceding the subprime crisis (Arthur, Tang and Lin 2015). The exercise of the research illustrated that the “patterns preceding the subprime crisis” in several ways were not unprecedented and the model included here may have helped the authorities to appropriately forecast the crisis and thereby neccessary regulatory measures.  This situation was notably evident for U.S. being the epicentre of subprime crisis. The prediction of the crises in the U.K. is also seen to be befitted using this model. The study computed the “degree of tightening of the regulation” which would have been needed to minimize the probability of the crisis down to one in every 18 years. The findings have revealed that there was a rise of “2 percentage points” in the capital adequacy to around 6% in the liquidity or alternatively 1.6% rise in both.  Reducing the probability to 1 crisis in 100 years is required to maintain a much more substantial regulatory liquidity. However, this is depicted as a very challenging to the banking system and would adversely impact on the “real economy” by widening of the bank margins. This type of policy should not be ruled out given the high cost of the banking crisis (Niesr.ac.uk. 2018).

A study on “The Impact of The Financial and Economic Crisis on Debt Sustainability in Developing Countries” has emphasized on the need for maintaining domestic and international level policies to generate the economic growth in the developing countries. This is seen to be conducive in maintaining the sustainability and meeting the MDGs. As per the depictions made in the study the short-term actions are aimed to minimize the impacts of the financial crises and the long-term measure are implemented to increase the robustness of the global economy and reducing the global imbalances. The study has been further able to depict that at times of financial crises the low-income countries in general have smaller margins to compensate the external shocks. The immediate short-term measure to be adopted during a crisis is mainly seen among the leader for providing the low-income companies to respond to the external shocks (Unctad.org. 2018).

Several predictions made from the secondary analysis has been able to predict that in the recent times practitioners are converging to the notion of debt crisis which are associated to both debt composition and debt level. The significant interaction among the domestic public and external debt has also played a vital role in signalling the early warning signs for financial crisis. Moreover, the International policies will be able to assist safer debt structure which is related to make the countries more resilient to the external shocks (Liu 2015).

Some of the other studies have been able to suggest that on an average deposit insurance has the possibility of exacerbating the moral hazard problems related to bank lending. As discussed by Anginer, Demirguc-Kunt and Zhu (2014), in the institutionally under developed economies the bank has the propensity to “exploit the availability” of the “insured deposits” and this increases their risk. This tendency also leads to make the financial system more prone to the crisis. The impact of the deposit insurance during crisis was studied using sample of “4,109 publicly traded banks in 96 countries”. The paper examined the effect of “deposit insurance on bank risk and systemic stability separately for the crisis period from 2007 to 2009” and 2004 to 2005. The main findings were able to state “generous financial safety” nets increased the “bank risk” and reduced the systematic steadiness during in the countries possessing coverage for deposit insurance. It needs to be discerned that based on the consideration of the “earlier literature” the overall impression of the deposit insurance on the depiction of financial crisis remains negative. This is due to the fact of the “destabilizing effect during normal times, which is greater in the magnitude in compared to the stabilizing the effect at times of global turbulence”. The overall findings of the study are able to imply “moral hazard effect” of deposit insurance “dominates in good times” while the “stabilization effect” of “deposit insurance dominates in turbulent times”.  As the generous safety net policies may lead to instability the deposit insurance sample is considered to be negative (Moro 2014).

Conclusion

The several depictions made in the study has related the financial and macroeconomic variables required in the prediction of the financial with the use of dataset taken from 14 developed countries from 1870 to 2008. It needs to be further understood that the inclusion of general to specific model was commenced with the large array of the macroeconomic and financial predictors. This has also included the lags and which are distributed among the different variables.

It may be discerned that the run up to the crisis has led to considerable amount of the predictive power which are associated to credit booms. The introduction of the income inequality has further brought in several range predictors for the crises. The research is able to complement the previous research and found that the study is able to introduce the income inequality emphasized by Schularick and Taylor. The main understanding from the discourse of the evaluations has further focused on the income inequality which may be considered conducive in paying attention to the various strands of “in-sample estimations”, which is considered as the universal predictor for the out-of-sample predictions. The various findings from the out of sample forecasts have been also able to emphasize on the short subperiods yields which are varying in a considerable manner for the several factors. This includes “government debt” which may lead to unfinished estimations for depicting the usefulness of certain predictor.

The complete measurements from the secondary sources have been able to suggest that prediction of financial crisis is still a challenging task. Despite of the factors such as credit boom have been held responsible for aftermath of the crisis, they cannot be considered as the only reason. The factors leading to financial crises can be ranging from several factors which includes “private banknotes during the Panic of 1819 to the runs on repo, commercial paper, and primer broker balances during the crisis of 2007-2009”. Henceforth, more number of the research papers needs to be warranted for understanding of the various types of the predictive aspects such as “bank credit, external imbalances, financial innovation (securitization), asset price booms, and income inequality”. The future studies associated to the research may be further linked to the predictive performance of the different factors as per the characteristics of the financial systems at different times. The overall results have been able to “imply that the role of income inequality” during the financial crisis needs to be dealt with more attention. In case the income inequality is having the destabilizing effect then the present “trend of increasing inequality” can set for further disorder in financial aspect

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