TITLE:
Examine the Reliability of Econometrics Software: An Empirical Comparison of Time Series Modelling
AUTHORS:
Wickramasinghage M. A. Wickramasinghe, Parana P. A. W. Athukorala, Siththara G. J. Senarathne, Yapa P. R. D. Yapa
KEYWORDS:
Econometrics, Macroeconomic Determinants, Software Packages, Time Series Modelling
JOURNAL NAME:
Open Journal of Statistics,
Vol.13 No.1,
February
22,
2023
ABSTRACT: 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.