Researchers must understand that naively relying on the reliability of
statistical software packages may result in suboptimal, biased, or erroneous
results, which affects applied economic theory and the conclusions and policy
recommendations drawn from it. To create confidence in a result, several
software packages should be applied to the same estimation problem. This study
examines the results of three software packages (EViews, R, and Stata) in the
analysis of time-series econometric data. The time-series data analysis which
presents the determinants of macroeconomic growth of Sri Lanka from 1978 to
2020 has been used. The study focuses on testing for stationarity,
cointegration, and significant relationships among the variables. The Augmented
Dickey-Fuller and Phillips Perron tests were employed in this study to test for
stationarity, while the Johansen cointegration test was utilized to test for
cointegration. The study employs the vector error correction model to assess
the short-run and long-term dynamics of the variables in an attempt to
determine the relationship between them. Finally, the Granger Causality test is
employed in order to examine the linear causation between the concerned
variables. The study revealed that the results produced by three software
packages for the same dataset and the same lag order vary significantly. This
implies that time series econometrics results are sensitive to the software
that is used by the researchers while providing different policy implications
even for the same dataset. The present study highlights the necessity of
further analysis to investigate the impact of software packages in time series
analysis of economic scenarios.
References
[1]
Chen, E.E. and Wojcik, S.P. (2016) A Practical Guide to Big Data Research in Psychology. Psychological Methods, 21, 458-474. https://doi.org/10.1037/met0000111
[2]
Ciftci, S.K., Karadag, E. and Akdal, P. (2014) Instruction of Statistics via Computer-Based Tools: Effects on Statistics’ Anxiety, Attitude, and Achievement. Journal of Educational Computing Research, 50, 119-133. https://doi.org/10.2190/EC.50.1.f
[3]
McCulloch, R S. (2017) Learning Outcomes in a Laboratory Environment vs. Classroom for Statistics Instruction: An Alternative Approach Using Statistical Software. International Journal of Higher Education, 6, 131-142.
https://doi.org/10.5430/ijhe.v6n5p131
[4]
Sosa, G.W., Berger, D.E., Saw, A.T. and Mary, J.C. (2011) Effectiveness of Computer-Assisted Instruction in Statistics: A Meta-Analysis. Review of Educational Research, 81, 97-128. https://doi.org/10.3102/0034654310378174
[5]
McCullough, B.D. and Vinod, H.D. (1999) The Numerical Reliability of Econometric Software. Journal of Economic Literature, 37, 633-665.
https://doi.org/10.1257/jel.37.2.633
[6]
Altman, M. and McDonald, M.P. (2001) Choosing Reliable Statistical Software. PS: Political Science & Politics, 34, 681-687.
https://doi.org/10.1017/S1049096501001093
[7]
McCullough, B.D. and Wilson, B. (2005) On the Accuracy of Statistical Procedures in Microsoft Excel 2003. Computational Statistics & Data Analysis, 49, 1244-1252.
https://doi.org/10.1016/j.csda.2004.06.016
[8]
Sawitzki, G. (1994) Report on the Numerical Reliability of Data Analysis Systems. Computational Statistics & Data Analysis, 18, 289-301.
https://doi.org/10.1016/0167-9473(94)90177-5
[9]
McCullough, B.D. (1999) Assessing the Reliability of Statistical Software: Part II. The American Statistician, 53, 149-159.
https://doi.org/10.1080/00031305.1999.10474450
[10]
McCullough, B.D. (1999) Econometric Software Reliability: EViews, LIMDEP, SHAZAM and TSP. Journal of Applied Econometrics, 14, 191-202.
https://doi.org/10.1002/(SICI)1099-1255(199903/04)14:2<191::AID-JAE524>3.0.CO;2-K
[11]
McCullough, B.D. and Heiser, D.A. (2008) On the Accuracy of Statistical Procedures in Microsoft Excel 2007. Computational Statistics & Data Analysis, 52, 4570-4578.
https://doi.org/10.1016/j.csda.2008.03.004
[12]
L’Ecuyer, P. and Simard, R. (2007) TestU01: AC Library for Empirical Testing of Random Number Generators. ACM Transactions on Mathematical Software (TOMS), 33, Article No. 22. https://doi.org/10.1145/1268776.1268777
[13]
McCullough, B.D. and Wilson, B. (2002) On the accuracy of statistical procedures in Microsoft Excel 2000 and Excel XP. Computational Statistics & Data Analysis, 40, 713-721. https://doi.org/10.1016/S0167-9473(02)00095-6
[14]
Ng, S. and Perron, P. (2001) LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power. Econometrica, 69, 1519-1554.
https://doi.org/10.1111/1468-0262.00256
[15]
McCullough, B.D. (2004) Wilkinson’s Tests and Econometric Software. Journal of Economic and Social Measurement, 29, 261-270.
https://doi.org/10.3233/JEM-2004-0199
[16]
Keeling, K.B. and Pavur, R.J. (2007) A Comparative Study of the Reliability of Nine Statistical Software Packages. Computational Statistics & Data Analysis, 51, 3811-3831.
https://doi.org/10.1016/j.csda.2006.02.013
[17]
Johansen, S. and Juselius, K. (1990) Maximum Likelihood Estimation and Inference on Cointegration—With Applications to the Demand for Money. Oxford Bulletin of Economics and Statistics, 52, 169-210.
https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x
[18]
Dickey, D.A., Jansen, D.W. and Thornton, D.L. (1994) A Primer on Cointegration with an Application to Money and Income. In: Rao, B.B., Ed., Cointegration, Palgrave Macmillan, London, 9-45. https://doi.org/10.1007/978-1-349-23529-2_2
[19]
Shibata, R. (1976) Selection of the Order of an Autoregressive Model by Akaike’s Information Criterion. Biometrika, 63, 117-126.
https://doi.org/10.1093/biomet/63.1.117
[20]
Engle, R.F. and Granger, C.W.J. (1987) Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55, 251-276.
https://doi.org/10.2307/1913236
[21]
Marquez, J. (1995) Time Series Analysis: James D. Hamilton, 1994, (Princeton University Press, Princeton, NJ), 799 pp., US $55.00, ISBN 0-691-04289-6. International Journal of Forecasting, 11, 494-495. https://doi.org/10.1016/0169-2070(95)90035-7
[22]
Granger, C.W.J. (1969) Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica: Journal of the Econometric Society, 37, 424-438. https://doi.org/10.2307/1912791
