Abstract
The problems of how to evaluate and compare the quality of models formed from panel data are discussed. Using the lessons learnt from the valuation of time series models using post-sample forecasting a variety of tests are suggested using several out-of-sample parts of a panel data set, but un-used regions and time periods. Emphasis is on comparing models and two real-data examples are provided - a transition model of land use in the Amazon region and a production model using Jorgenson's input-output database. Specific consideration is given to data quality and the effect of outliers and to policy model evaluation.