Literature Review on Climate Change and Aedes aegypti

The provided text appears to be a collection of references related to a literature review on the topic of using ecological niche models to predict the spread of _Aedes aegypti_ mosquitoes in the context of climate change. The list includes diverse sources covering various aspects like epidemiology, statistical modeling, data analysis, and finance. This suggests the review might delve into how different disciplines contribute to understanding disease risk projections in a changing climate.

ECONOMICS-STATA

TABLE OF CONTENTS
INTRODUCTION......................................................................................................................1
AIM............................................................................................................................................2
OBJECTIVES............................................................................................................................2
RESEARCH QUESTIONS........................................................................................................2
BACKGROUND........................................................................................................................2
LITERATURE REVIEW...........................................................................................................3
Concept of time series data.....................................................................................................3
Role of time series data in improving business decision........................................................3
Identify issues in a business which requires the help of time series data...............................4
RESEARCH METHODOLOGY...............................................................................................4
DATA ANALYSIS....................................................................................................................6
SIGNIFICANCE OF STUDY..................................................................................................20
LIMITATION OF RESEARCH..............................................................................................20
CONCLUSION AND RECOMMENDATION.......................................................................21
REFERENCES.........................................................................................................................22

INTRODUCTION
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
1

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.
AIM
To improve the decisions making in business with the help of time series data
OBJECTIVES
 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
RESEARCH QUESTIONS
 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.
2

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.
LITERATURE REVIEW
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.
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).
Role of time series data in improving business decision
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
3

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.
Identify issues in a business which requires the help of time series data
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).
RESEARCH METHODOLOGY
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.
4

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.
` 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
5

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.
EMPIRICAL RESULTS
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
6

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
7

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
8

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
9

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
10

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
11

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
12

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
13

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 P-value F crit
Between Groups 9.5152E+20 1 9.52E+20 6.06E-05 0.993794 3.860663
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 P-value F crit
Between Groups 2.2785E+24 1 2.28E+24 0.120581 0.728553 3.860351
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 P-value F crit
Between Groups 1.9563E+24 1 1.96E+24 0.069111 0.792745 3.860275
Within Groups 1.404E+28 496 2.83E+25
14

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 P-value F crit
Between Groups 6.6914E+24 1 6.69E+24 0.171997 0.678521 3.860199
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
15

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%
16

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%
17

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
18

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
Aruba Ecuador Lebanon Portugal Vanuatu
-100
0
100
200
300
400
500
600
Inflation
2000
2001
2002
2003
2004
2005
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).
SIGNIFICANCE OF STUDY
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
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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.
LIMITATION OF RESEARCH
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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