Analyzing Time Series And Panel Data Sets For Economic Performance Essay.

This task  concerns the preparation of two different datasets, that is a time series data set (e.g. stock and market returns, T-bill interest rates, etc) and a panel data set (e.g. macroeconomic variables for a large group of countries for several years). The datasets must be assembled by the student and cannot be represented by any known ready dataset created by others as a whole. The choice of datasets should represent student's interests.

The datasets must be presented in Stata as .dta file and each variable must be clearly labeled. Each dataset must include 7 variables at least. The total amount of data observations in each of the datasets is not less than 210. The Time series dataset cannot have missing observations. Make sure you then prepare two STATA files each of them for a specific data set (Time Series, and Panel Data sets).

Current assignment is about explaining the concepts of economics by adding statistical data to assess the overall performance of an entity. This assignment improves analytical skills of an individual in collecting reliable data from a relevant source. After collection of data from reliable source, the collected will analyse by a person on different parameters. Current report focuses on collecting two different kinds of data of time series and panel data series to draw a comparison among both the data series collected by an individual. Time series data will include various concepts such as stock market returns and Treasury bill rates. Interactive chart will show the declining or rising position of the external markets. It is empirical analysis report in which data series collected by the firm shows the overall performance of the firm in an external entity to take any decisions of investments on the produced results (World Bank data, 2017). Economic concepts use by an individual to test the performance of the overall economy. Data source of the current assignment is from World Bank website which will provide reliable and authentic information find on the website. Efficiency of the time series data can ascertain by applying regression analysis to record the overall trend of increasing or decreasing as the time series data is specifically used for forecasting purpose.

Time series data variables

1. Stock and market return
2. T bill interest rates
3. GDP
4. Population
5. Inflation
6. Poverty
7. Gross national income

Another data is related with the panel data series which is about macroeconomic variables collected for large samples of data. This kind of data has used simultaneously in statistics as well as in economics. In the panel data observations of different phenomena has considered by an individual. Panel data series emphasises on the macroeconomic variables such as Gross domestic product, unemployment rate and inflation rate that affects the large group of the business concern within a stipulated time period. Panel data series collected by an individual helps in tracking the overall performance of the firm by identifying all the issues to get rid of all of them in a given span of time.

This report has two parts one part is collection of data and another part is about research project based on one of the collected data. Research report is based on resolving the macroeconomic problem in the business by utilizing the collected data. Literature review is conducted on different objectives framed on the basis of the aims of the current research project.

To improve the decisions making in business with the help of time series data

• To determine the concepts of time series data
• To illustrate the role of time series data in improving business decisions
• To ascertain the business issues which demand the support of time series data
• Suggest some strategies to overcome the issues faced by an entity

• What is the concept of Time series data?
• Explain the role of time series data in improving business decisions?
• Identify business issues which demand the support of time series data?
• Recommend some strategies to overcome the issues faced by an entity.

Background

Present research study is all about explaining the business issues by using the time series data in which an individual will identify all the problems faced by them in an entity to eliminate the same by taking corrective action (Qiu, Ren, Suganthan and Amaratunga, 2017). A business environment is a mixtures of various elements that effects the external entity and do affect but the actions of all the players located in a similar industry or market segment. An enterprise owner plays a significant role in a business as they held responsible for boosting or suppressing their current earnings by making correct decisions in a business. Correct decisions is the basis secret behind the success of the venture through which individuals can boost up their earnings by taking decisions in the favour of all the stakeholders o the business concern.

Every business has flaws and strengths which need to be identify by the owner to get successful as analyzing its own weaknesses is the most important decisions taken by an entity (Deb, Zhang, Yang, Lee and Shah, 2017). Management must analyse their strength and weaknesses to grab all the external opportunities by eliminating all the threats takes places in the external business environment. Time series data is related with the time period as data collected by an entity owner according to specific time period. It includes various components such as Gross domestic product, inflation rate, increasing or decreasing population. By analyzing all the factors of time series data an entity owner can ascertain its overall performance within a given span of time as their motive is achieve the desired goals and the objectives of an entity (Nogi and et.al., 2017).  Time series analysis conducted by an individual to show the increasing or declining trend of the business performance of the business concern by focusing on all the important business areas in improving the overall performance of the business concern within a given span of time.

Concept of time series data

Chatfield, (2016) has asserted that time series data selected by an individual in the current research will give new direction to the entire research study that helps in collecting suitable and reliable data that meet all the criteria created by an individual. Time series data has used by an individual in predicting the future performance of the business as the decisions of investment has based on evaluating the gross domestic product of all the countries in the world (Leimbach, Kriegler, Roming and Schwanitz, 2017). Higher gross domestic product generated by the countries shows their efficiency as compared to different countries in the whole world.

Literature Review

Brockwell and Davis, (2016) states that time series analysis used by a person to present all the data points as different variables in showcasing the overall performance of the business concern within a given span of time. Line chart is the best suitable visual technique in presenting the increasing or decreasing trend of inflation which needs to be identified at the later stage to secure the position of the business in the external entity (Zolotoy, Frederickson and Lyon, 2017).

According to the study of Zucchini, MacDonald and Langrock, (2016) time series data play an integral role in improving the business decisions as in the current dynamic world it acts as a data mining tool. Data mining tool utilizes time series data in understanding the inflation patterns in assessing the overall trend of inflation in the external market segment (Talay, Akdeniz and Kirca, 2017). It is one of the analytical tool that consider gross domestic and gross national income that helps in analyzing the income and revenues as against the expenses incurred in a business to know the capability of firm in paying its debts by utilizing all their income in particular time period.

Tanaka, K., 2017) suggested that knowing about the population of all the countries in a world help in knowing the potential target market in which the investors will launch their business to boost up its earnings. Time series data acts as a business proposal that illustrates all the strengths, weaknesses, threats and opportunities.

From the point of view of Schmitt and Huang, (2016) uncertainty in business is inherent risk which will not be eliminated as it can be minimize by taking corrective actions before the occurrence of risks in an entity. Business uncertainties are recession, higher inflation rate, poverty, unemployment. All these variables are part of business uncertainty which can be predicted by an individual by taking the help of time series of data.  Decision making process can get successful by focussing on the strengths of the firm by keeping watch on all the external market changes as a faithful dog (Shmueli, and et. al., 2017). Personification has used to explain the qualities of dog which is required in increasing determined targets of the firm (NCD Risk Factor Collaboration, 2017). Another business issue is to overcome all the threats that are merger and acquisition risks faced by an entity due to sudden bankruptcy of the business (Chong and et. al., 2017).

Role of time series data in improving business decision

Research methodology plays an integral role in conducting a particular research study in which the researcher focuses on collecting data for an appropriate research study. It gives right direction to a study that helps in collecting relevant facts and figures that meets all the criteria’s of a particular research (Waljee and et.al., 2017). Research methodology acts a like a compass that gives right direction to the research study. Various approaches and research types helps an individual in collecting the best suitable data meets all the requirements of the business.

Research approach- Research approach is an important technique that helps in collecting authentic and reliable data after analyzing the nature of the research study (Ramirez Cohen and et. al., 2017). There are three different kinds of research approaches such as deductive, inductive research approaches in refining all the collected data by an individual. Inductive approach is suitable in that research kind in which hypothesis of the research is related to generalizing the terms from general to the specific nature of the overall study (Meshram and Prabhune, 2017). In this approach, verification is given more preferences by analyzing the overall data by using several parameters to tests the efficiency of the selected data by an individual in concluding the overall research (Pearson and Raphael, 2017). Inductive approach has applicable in the current research in which the researcher tries to generalize the research study and its hypothesis from general to specific to conclude the current research to accomplish the desired aims and the objectives framed by an enterprise within a given span of time.

Research type- There are two kinds of research such as qualitative as well as quantitative research type which an individual selects according to their convenience (Obenauer,  Quinn, Li and Joyner, 2017). Qualitative research is related with theoretical concepts used in a research in which the researcher collects fact and information to develop a theory that helps in overcoming all the issues faced by an individual.

 On the other hand, Quantitative research kind is about analyzing the collected data and numerical to take the best suitable decisions in the favour of an entity (Elhorst, 2017). Researcher tries to do justice with the nature of the research study as they held responsible for concluding wrong research as the current research will form basis in the future for authors to start their fresh research (Bacci, 2017). Improving the business decisions is both qualitative as well as quantitative research but in the current research, time series data sets are utilized to get rid of all the issues faced by an individual in an entity which will be resolved within a given span of time.

Identify issues in a business which requires the help of time series data

` Apart from qualitative and quantitative research type, there are two other research types such as primary research as well as secondary research conducted by an individual. Primary research is considered by an entity in case of small sample size along with a need to gather authentic and reliable set of data (Kiviet, Pleus and Poldermans, 2017). On another hand, Secondary research is that kind of research in which data has gathered by a person with the help of books and journals, news articles, magazines and internet as the biggest source of information. Internet is one of the important sources of information which provides different sets of data within a few seconds in the current technology world; an individual will gather large samples of data in less period of time.

2000
Mean	46.70694
Standard Error	6.665371
Median	14.54261
Mode	50.35253
Standard Deviation	66.65371
Sample Variance	4442.717
Kurtosis	3.421018
Skewness	1.926664
Range	289.5619
Minimum	0.012082
Maximum	289.574
Sum	4670.694
Count	100
Largest(1)	289.574
Smallest(1)	0.012082
Confidence Level (95.0%)	13.22554

2001
Mean	31.98447
Standard Error	4.775308
Median	9.607232
Mode	29.43288
Standard Deviation	49.85568
Sample Variance	2485.589
Kurtosis	4.616607
Skewness	2.18428
Range	238.0752
Minimum	0.018825
Maximum	238.0941
Sum	3486.307
Count	109
Largest(1)	238.0941
Smallest(1)	0.018825
Confidence Level (95.0%)	9.465489

2002
Mean	26.08939
Standard Error	3.684837
Median	9.574764
Mode	21.13843
Standard Deviation	37.21503
Sample Variance	1384.959
Kurtosis	3.115459
Skewness	1.866045
Range	162.9622
Minimum	0.012568
Maximum	162.9748
Sum	2661.118
Count	102
Largest(1)	162.9748
Smallest(1)	0.012568
Confidence Level(95.0%)	7.309726

2003
Mean	28.26442
Standard Error	3.447126
Median	14.26828
Mode	6.38863
Standard Deviation	35.49033
Sample Variance	1259.564
Kurtosis	2.69585
Skewness	1.664848
Range	169.0659
Minimum	0.002275
Maximum	169.0682
Sum	2996.028
Count	106
Largest(1)	169.0682
Smallest(1)	0.002275
Confidence Level(95.0%)	6.835014

2004
Mean	34.47542
Standard Error	4.075434
Median	19.59448
Mode	9.313726
Standard Deviation	43.13034
Sample Variance	1860.226
Kurtosis	4.979635
Skewness	1.966215
Range	238.7499
Minimum	0.001444
Maximum	238.7513
Sum	3861.247
Count	112
Largest(1)	238.7513
Smallest(1)	0.001444
Confidence Level(95.0%)	8.075744

2005
Mean	40.44834
Standard Error	5.476848
Median	18.59044
Mode	18.59044
Standard Deviation	56.12099
Sample Variance	3149.566
Kurtosis	8.163689
Skewness	2.511983
Range	335.9717
Minimum	0.000944
Maximum	335.9727
Sum	4247.075
Count	105
Largest(1)	335.9727
Smallest(1)	0.000944
Confidence Level(95.0%)	10.86079

2006
Mean	49.80447
Standard Error	6.331244
Median	33.0037
Mode	37.8808
Standard Deviation	66.10013
Sample Variance	4369.227
Kurtosis	10.53218
Skewness	2.777697
Range	390.3877
Minimum	0.001823
Maximum	390.3895
Sum	5428.688
Count	109
Largest(1)	390.3895
Smallest(1)	0.001823
Confidence Level(95.0%)	12.54962

2007
Mean	73.37965
Standard Error	10.67922
Median	44.53974
Mode	84.94874
Standard Deviation	109.9494
Sample Variance	12088.86
Kurtosis	38.71543
Skewness	5.173535
Range	952.6665
Minimum	0.000868
Maximum	952.6673
Sum	7778.243
Count	106
Largest(1)	952.6673
Smallest(1)	0.000868
Confidence Level(95.0%)	21.17493

2008
Mean	55.48338
Standard Error	8.568667
Median	31.28782
Mode	70.85264
Standard Deviation	88.21983
Sample Variance	7782.738
Kurtosis	30.30293
Skewness	4.666386
Range	715.1446
Minimum	0.021012
Maximum	715.1656
Sum	5881.238
Count	106
Largest(1)	715.1656
Smallest(1)	0.021012
Confidence Level(95.0%)	16.99008

2009
Mean	51.14634
Standard Error	7.663489
Median	25.95278
Mode	127.2036
Standard Deviation	78.52739
Sample Variance	6166.551
Kurtosis	34.64407
Skewness	4.895314
Range	660.1611
Minimum	0.102544
Maximum	660.2636
Sum	5370.366
Count	105
Largest(1)	660.2636
Smallest(1)	0.102544
Confidence Level(95.0%)	15.19699

2010
Mean	44.94058
Standard Error	7.129424
Median	20.6917
Mode	112.3923
Standard Deviation	75.45073
Sample Variance	5692.812
Kurtosis	37.40396
Skewness	5.124801
Range	650.5347
Minimum	0.098838
Maximum	650.6336
Sum	5033.345
Count	112
Largest(1)	650.6336
Smallest(1)	0.098838
Confidence Level(95.0%)	14.12743

2011
Mean	37.83564
Standard Error	6.517599
Median	13.89362
Mode	44.7955
Standard Deviation	69.28303
Sample Variance	4800.138
Kurtosis	33.91451
Skewness	4.996984
Range	578.1323
Minimum	0.087145
Maximum	578.2195
Sum	4275.428
Count	113
Largest(1)	578.2195
Smallest(1)	0.087145
Confidence Level(95.0%)	12.91379

2012
Mean	30.83999
Standard Error	4.818191
Median	12.50313
Mode	52.84531
Standard Deviation	51.21807
Sample Variance	2623.291
Kurtosis	27.54439
Skewness	4.422598
Range	409.9084
Minimum	0.071363
Maximum	409.9798
Sum	3484.919
Count	113
Largest(1)	409.9798
Smallest(1)	0.071363
Confidence Level(95.0%)	9.546627

2013
Mean	33.17782
Standard Error	5.301274
Median	14.80938
Mode	#N/A
Standard Deviation	55.85238
Sample Variance	3119.489
Kurtosis	31.12516
Skewness	4.675606
Range	459.4531
Minimum	0.111314
Maximum	459.5644
Sum	3682.738
Count	111
Largest(1)	459.5644
Smallest(1)	0.111314
Confidence Level(95.0%)	10.50588

2014
Mean	40.86299
Standard Error	6.256119
Median	17.11328
Mode	28.9828
Standard Deviation	63.1837
Sample Variance	3992.18
Kurtosis	27.18103
Skewness	4.32623
Range	497.8169
Minimum	0.00084
Maximum	497.8177
Sum	4168.025
Count	102
Largest(1)	497.8177
Smallest(1)	0.00084
Confidence Level(95.0%)	12.41046

2015
Mean	72.46158
Standard Error	12.48705
Median	20.01736
Mode	303.9564
Standard Deviation	110.2827
Sample Variance	12162.27
Kurtosis	10.43873
Skewness	2.71995
Range	668.4335
Minimum	0.165294
Maximum	668.5988
Sum	5652.003
Count	78
Largest(1)	668.5988
Smallest(1)	0.165294
Confidence Level(95.0%)	24.8649

2016
Mean	53.11924
Standard Error	8.05879
Median	20.36108
Mode	17.47536
Standard Deviation	70.71559
Sample Variance	5000.695
Kurtosis	8.739052
Skewness	2.425797
Range	420.9002
Minimum	0.117478
Maximum	421.0177
Sum	4090.181
Count	77
Largest(1)	421.0177
Smallest(1)	0.117478
Confidence Level(95.0%)	16.05047

A stock market return of all the countries in whole world has tested by using one of the famous statistical tool techniques. Descriptive statistics has used in analyzing the stock market return data which covers all the tools of central tendency, dispersion, measures of skewness (Bou and Satorra, 2017). It includes various factors such as mean, median, mode, variance, standard deviation, kurtosis, range of the data points and standard error. Mean shows the consistency of the data points by sowing the overall average of stock market return ranging from 2000-2016 that is showing the data response of 17 years. Mean of the data points of the above data is fluctuating in nature as initially its declining, then increasing and again got stable and then increases.

Standard deviation shows the deviation takes places in a collected set of data about all the stock market returns of all the countries exists in the whole world (Moral-Benito, Allison and Williams, 2017). Higher standard deviation shows the closeness of the data points with the mean of the data and lower standard deviation shows no closeness with the data. In the current case, there is lower standard deviation as the standard deviation is declining from 2000-2016 showing deficiency of the overall mean of overall stock market (McCarthy, Fader and Hardie, 2017).

SUMMARY
Groups	Count	Sum	Average	Variance
1.87E+09	244	2.48E+14	1.02E+12	1.58E+25
1.92E+09	244	2.47E+14	1.01E+12	1.56E+25
ANOVA
Source of Variation	SS	df	MS	F
Between Groups	9.5152E+20	1	9.52E+20	6.06E-05
Within Groups	7.6367E+27	486	1.57E+25
Total	7.6367E+27	487

SUMMARY
Groups	Count	Sum	Average	Variance
1.94E+09	248	2.57E+14	1.04E+12	1.67E+25
2.02E+09	248	2.91E+14	1.17E+12	2.11E+25
ANOVA
Source of Variation	SS	df	MS	F
Between Groups	2.2785E+24	1	2.28E+24	0.120581
Within Groups	9.3347E+27	494	1.89E+25
Total	9.337E+27	495

SUMMARY
Groups	Count	Sum	Average	Variance
2.23E+09	249	3.3E+14	1.33E+12	2.64E+25
2.33E+09	249	3.61E+14	1.45E+12	3.02E+25
ANOVA
Source of Variation	SS	df	MS	F
Between Groups	1.9563E+24	1	1.96E+24	0.069111
Within Groups	1.404E+28	496	2.83E+25
Total	1.4042E+28	497

SUMMARY
Groups	Count	Sum	Average	Variance
2.42E+09	250	3.96E+14	1.59E+12	3.46E+25
2.62E+09	250	4.54E+14	1.82E+12	4.32E+25
ANOVA
Source of Variation	SS	df	MS	F
Between Groups	6.6914E+24	1	6.69E+24	0.171997
Within Groups	1.9374E+28	498	3.89E+25
Total	1.9381E+28	499

SUMMARY
Groups	Count	Sum	Average	Variance
2.79E+09	249	5.06E+14	2.03E+12	5.12E+25
2.5E+09	248	4.8E+14	1.94E+12	4.59E+25
ANOVA
Source of Variation	SS	df	MS	F	P-value	F crit
Between Groups	1.1818E+24	1	1.18E+24	0.024347	0.876069	3.860313
Within Groups	2.4028E+28	495	4.85E+25
Total	2.4029E+28	496

SUMMARY
Groups	Count	Sum	Average	Variance
2.47E+09	248	5.34E+14	2.15E+12	5.39E+25
2.58E+09	248	6E+14	2.42E+12	6.6E+25
ANOVA
Source of Variation	SS	df	MS	F	P-value	F crit
Between Groups	8.7584E+24	1	8.76E+24	0.146182	0.702375	3.860351
Within Groups	2.9597E+28	494	5.99E+25
Total	2.9606E+28	495

SUMMARY
Groups	Count	Sum	Average	Variance
	245	6.15E+14	2.51E+12	6.92E+25
	246	6.38E+14	2.59E+12	7.3E+25
ANOVA
Source of Variation	SS	df	MS	F	P-value	F crit
Between Groups	8.1575E+23	1	8.16E+23	0.011471	0.914751	3.860545
Within Groups	3.4774E+28	489	7.11E+25
Total	3.4775E+28	490

SUMMARY
Groups	Count	Sum	Average	Variance
	241	6.55E+14	2.72E+12	7.83E+25
	238	6.16E+14	2.59E+12	7.09E+25
	225	6.18E+14	2.75E+12	7.68E+25
ANOVA
Source of Variation	SS	df	MS	F	P-value	F crit
Between Groups	3.4152E+24	2	1.71E+24	0.022671	0.977585	3.008571
Within Groups	5.2801E+28	701	7.53E+25
Total	5.2805E+28	703

In one way ANOVA, p value shows the efficiency of the data in which p value between the range of 0 and 1 shows the acceptance or rejection of hypothesis used in a particular research. P value less than 0.05 is rejected as a null hypothesis and p value higher than 0.05 is not null hypothesis (Sturm, Goldstein, Huntington and Douglas, 2017). Current data has higher p value than 0.05 that shows that the entire hypothesis used in the research are not null.

Point	62149	Rank	Percent
257	5284886348	1	100.00%
101	4310509136	2	99.60%
138	4287532629	3	99.20%
154	3964846386	4	98.80%
100	3468363155	5	98.40%
247	2039466775	6	98.00%
60	2031055831	7	97.60%
137	1925379611	8	97.30%
140	1848804300	9	96.90%
61	1819288343	10	96.50%
59	1602333626	11	96.10%
228	1582008622	12	95.70%
38	1135185000	13	95.30%
202	1132832536	14	94.60%
238	1132832536	14	94.60%
179	1069095267	16	94.20%
93	997353719	17	93.80%
196	964601905	18	93.40%
107	870133480	19	93.00%
63	842848473	20	92.60%
102	842145981	21	92.30%
105	559246165	22	91.90%
215	511410066	23	91.10%
239	511410066	23	91.10%
213	511340559	25	90.70%
133	510827576	26	90.30%
71	478005307	27	90.00%
132	445044474	28	89.60%
234	430014112	29	89.20%
229	428318228	30	88.80%
126	422914642	31	88.40%
189	417158756	32	88.00%
62	390207446	33	87.60%
96	355762200	34	87.30%
134	322686243	35	86.90%
66	311539698	36	86.50%
103	282899816	37	86.10%
168	277473326	38	85.70%
72	262862202	39	85.30%
151	255989130	40	85.00%
249	249623000	41	84.60%
159	227903820	42	84.20%
236	225925572	43	83.80%
5	224735446	44	83.40%
104	181436821	45	83.00%
27	149352145	46	82.60%
200	148292000	47	82.30%
117	123537000	48	81.90%
34	110745760	49	81.50%
182	107678614	50	81.10%
18	106188642	51	80.70%
172	95269988	52	80.30%
152	85357874	53	80.00%
53	79433029	54	79.60%
255	66016700	55	79.20%
185	61947348	56	78.80%
75	58512808	57	78.40%
65	57412215	58	78.00%
79	57247586	59	77.60%
114	56719240	60	77.30%
231	56582821	61	76.90%
110	56226185	62	76.50%
242	53921699	63	76.10%
246	51892000	64	75.70%
70	48086516	65	75.30%
124	42869283	66	75.00%
158	40626250	67	74.60%
68	38867322	68	74.20%
188	38110782	69	73.80%
261	36793490	70	73.40%
41	34614581	71	73.00%
43	34271565	72	72.60%
7	32729739	73	72.30%
33	27791000	74	71.90%
58	25912367	75	71.50%
244	25459604	76	71.10%
216	25181708	77	70.70%

	4.044021	2.883604	3.315775	3.657377	2.529938	3.395625
4.044021	1
2.883604	0.959544	1
3.315775	0.503776	0.514774	1
3.657377	0.216641	0.284309	0.823185	1
2.529938	0.160879	0.236169	0.756845	0.975362	1
3.395625	0.158245	0.255915	0.732155	0.969487	0.967799	1

	3.608711	5.391203	8.957732	-2.13637	2.077739
3.608711	1
5.391203	0.997519	1
8.957732	0.40146	0.56893	1
-2.13637	0.32165	0.340525	0.456136	1
2.077739	0.007049	-0.02466	0.339567	0.579468	1

	4.374596	0.571756	-2.37226	0.420998	0.476485	-0.89302
4.374596	1
0.571756	0.798068	1
-2.37226	0.542334	0.651214	1
0.420998	0.490281	0.559552	0.831273	1
0.476485	0.490099	0.45981	0.571401	0.819391	1
-0.89302	0.565809	0.521478	-0.01957	0.089902	0.673276	1

Inflation is a biggest negative aspect that crashes overall earrings of the business as this can be controlled by keeping track on the entire inflation rates among all the countries. Trend of inflation is decreasing among all the countries (Wang and Byrd, 2017).

Changes play an integral role in every business as change has dual phase as it is positive as well as negative for the business concern. An entity is required to keep track on its overall performance by identifying all the factors that may affects the performance of the firm in the external business environment (Hsiao, 2014). Aim of the current research emphasises on two important things such as use of time series data as well as improving the business decisions which is beneficial as well as not appropriate for the firm. A wise decisions of the business helps in grabbing large number of business opportunities by improving the current performance of an enterprise. Role of this research targets large number of the external audiences to participate in utilizing various statistical tools in achieving the desired aims and the objectives in the external business environment (Mayda, 2010). Time series analysis has used by an enterprise owner in ascertaining all their weaknesses that helps in getting rid of all the threats by compensating them with the current strengths of the business. Time series data shows the overall trend of all the variables such as gross domestic product, inflation, gross national income, poverty, population, Treasury bill interest rates and stock market returns. All these variables are considered by the firm as these shows both positive as well as negative aspects for an entity.

Basic limitation of the current research study is not to collect authentic data as only one source of website has chosen in the current assignment. Overall data of the research has collected from World Bank website (Baltagi, 2008). Data of different variable’s data has collected from all the countries arranged in an alphabetical order starting from A to Z. Weakness in collecting the data is that some of data for different variable is not available for several countries in the research which will deflate an entity’s desired aims and objectives. Due to lack of data available in some of the countries, the overall trend of the current research cannot be tracked. Another limitation of the research study is due to lack of expertise of the researcher in applying statistical tools. Standard measure of analyzing the data has two options such as excel analysis and SPSS analysis which will be used by a researcher according to their expertise (Petersen, 2009). These two different techniques will generate different results which create further confusion for an individual to take decisions based on the generated output as the input remains the same but differentiation lies in the processing of all the inputs into the systems.

Conclusion And Recommendation 

It can summarised from the above study that time series data used by the firm helps in overcoming the biggest issue of business uncertainty. Higher inflation burden imposed on an entity collapses various business markets can be controlled by using forecasting tools under time series data.

It is suggested to an entity to use excel data miner that helps in removing all the impurities lies in the data which helps in achieving all the desired market aims and targets of the firm. R language is another data mining tool in analyzing the overall business performance.

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