Co-Integration, Causality and Export-Led Growth in Portugal Article

Co-Integration, Causality and Export-Led Growth in Portugal - Article ExampleIn the current fill depressionly, the Augmented Dicky Fuller (ADF) test is used for finding the order of integration between the devil data series. Secondly, the Johansen maximum likelihood estimates are used for testing co-integration. Thirdly, the standard Granger-type test is adapted by using lag residual of the co-integrating regression model. Lag length for Granger causality test is goaded by minimizing the Akaikes Final Prediction Error (FPE). The data used in this study curb annual secondary data of GDP and Exports values in Portugal between 1835 to1985 time period. The base form has been selected since 1914 for calculating the original prices.Statistical estimates of ADF test showed that log GDP and log Exports are 1(1) while the first difference of the level variables is 1(0). The trace statistics of the Johansen maximum likelihood is used to conclude that real GDP and real Exports values of Portugal are co-integrated and causally related. Four lags of the dependent variable are used in this model. base on FPE criteria Granger causality test structure is determined as m=3, n=2, q=3, and r=4. Accordingly, the Wald test statistics of the Granger causality test rejected the null hypothesis in favor of drive away causality. Thus economic growth has caused export growth in Portugal during 1835 to1985 time period.The first section of the radical describes the concept of autocorrelation in relation to conventional research studies of applied economics. Autocorrelation or serial correlation is a common condition found in time series data. In OLS estimation residual is fictitious to be independently distributed and does not contain any long run correlations. Thus in the posture of autocorrelation OLS estimates are not associated with minimum variance. Nevertheless, autocorrelation is not considered as a fatal statistical issue in econometrics analyses.