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Random Effects And Spatial Autocorrelations With Equal Weights, Badi H. Baltagi
Random Effects And Spatial Autocorrelations With Equal Weights, Badi H. Baltagi
Center for Policy Research
This note considers a panel data regression model with spatial autoregressive disturbances and random effects where the weight matrix is normalized and has equal elements. This is motivated by Kelejian et al. (2005), who argue that such a weighting matrix, having blocks of equal elements, might be considered when units are equally distant within certain neighborhoods but unrelated between neighborhoods. We derive a simple weighted least squares transformation that obtains GLS on this model as a simple OLS. For the special case of a spatial panel model with no random effects, we obtain two sufficient conditions where GLS on this …
Estimating Heterogeneous Capacity And Capacity Utilization In A Multi-Species Fishery, Ronald G. Felthoven, William C. Horrace, Kurt E. Schnier
Estimating Heterogeneous Capacity And Capacity Utilization In A Multi-Species Fishery, Ronald G. Felthoven, William C. Horrace, Kurt E. Schnier
Center for Policy Research
We use a stochastic production frontier model to investigate the presence of heterogeneous production and its impact on fleet capacity and capacity utilization in a multi-species fishery. Furthermore, we propose a new fleet capacity estimate that incorporates complete information on the stochastic differences between each vessel-specific technical efficiency distribution. Results indicate that ignoring heterogeneity in production technologies within a multi-species fishery, as well as the complete distribution of a vessel's technical efficiency score, may yield erroneous fleet-wide production profiles and estimates of capacity. Furthermore, our new estimate of capacity enables out-of-sample production predictions predicated on either homogeneity or heterogeneity modeling …
Prediction In The Panel Data Model With Spatial Correlation: The Case Of Liquor, Badi H. Baltagi, Dong Li
Prediction In The Panel Data Model With Spatial Correlation: The Case Of Liquor, Badi H. Baltagi, Dong Li
Center for Policy Research
This paper considers the problem of prediction in a panel data regression model with spatial autocorrelation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965-1994. The spatial autocorrelation due to neighboring states and the individual heterogeneity across states is taken explicitly into account. We compare the performance of several predictors of the states demand for liquor for one year and five 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 …
Testing For Cointegrating Rank Via Model Selection: Evidence From 165 Data Sets, Badi H. Baltagi, Zijun Wang
Testing For Cointegrating Rank Via Model Selection: Evidence From 165 Data Sets, Badi H. Baltagi, Zijun Wang
Center for Policy Research
The model selection approach has been proposed as an alternative to the popular tests for cointegration such as the residual-based ADF test and the system-based trace test. Using information criteria, we conduct cointegration tests on 165 data sets used in published studies. The empirical results demonstrate the usefulness of the model selection approach for applied researchers.