Statistics

CoIntegration Causality and ExportLed Growth in Portugal

In the current study firstly, the Augmented Dicky Fuller (ADF) test is used for finding the order of integration between the two 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 determined by minimizing the Akaike’s 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 calculating 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 Portugal 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 null 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 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 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.

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