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Handbook of Empirical Economics and Finance _16

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Tham khảo tài liệu 'handbook of empirical economics and finance _16', tài chính - ngân hàng, tài chính doanh nghiệp phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 446 Handbook of Empirical Economics and Finance wrong restrictions on the p parameters which in turn introduce bias and lead to bad MSE performance of the resulting MLEs. Fortunately this does not translate fully into bad MSE performance for the regression coefficients. The pretest estimator of the regression coefficients always performs better than the misspecified MLE and is recommended in practice. 15.4 Forecasts Using Panel Data with Spatial Error Correlation The literature on forecasting is rich with time series applications but this is not the case for spatial panel data applications. Exceptions are Baltagi and Li 2004 2006 with applications to forecasting sales of cigarette and liquor per capita for U.S. states over time. In order to explain how spatial autocorrelation may arise in the demand for cigarettes we note that cigarette prices vary among states primarily due to variation in state taxes on cigarettes. Border effect purchases not included in the cigarette demand equation can cause spatial autocorrelation among the disturbances. In forecasting sales of cigarettes the spatial autocorrelation due to neighboring states and the individual heterogeneity across states is taken explicitly into account. Baltagi and Li 2004 derive the best linear unbiased predictor for the random error component model with spatial correlation using a simple demand equation for cigarettes based on a panel of 46 states over the period 1963-1992. They compare the performance of several predictors of the states demand for cigarettes for 1 year and 5 years ahead. The estimators whose predictions are compared include OLS fixed effects ignoring spatial correlation fixed effects with spatial correlation random effects GLS estimator ignoring spatial correlation and random effects estimator accounting for the spatial correlation. Based on the RMSE criteria the fixed effects and the random effects spatial estimators gave the best out of sample forecast performance. Best linear unbiased .