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

Co-Integration, Causality and Export-Led Growth in Portugal - Article ExampleIn the current study firstly, the Augmented Dicky Fuller (ADF) test is apply for decision the order of integration between the two info series. Secondly, the Johansen maximum likelihood estimates argon used for testing co-integration. Thirdly, the standard Granger-type test is adapted by utilize lag residual of the co-integrating regression model. Lag length for Granger causality test is determined by minimizing the Akaikes Final Prediction Error (FPE). The data used in this study comprise annual secondary data of GDP and Exports values in Portugal between 1835 to1985 time period. The base year has been selected since 1914 for shrewd the real 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 Port ugal are co-integrated and causally related. Four lags of the dependent variable are used in this model. Based 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 idle hypothesis in favor of reverse causality. Thus economic growth has caused export growth in Portugal during 1835 to1985 time period.The first section of the paper describes the thought 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 assumed to be independently distributed and does not contain any long run correlations. Thus in the presence of autocorrelation OLS estimates are not associated with minimum variance. Nevertheless, autocorrelation is not considered as a fatal statistical issue in econometrics analyses.