Prediction with Pooled Cross-Section and Time-Series Data: Two Case Studies.
Abstract
When estimating models with pooled cross-section and time-series data (e.g. estimating demand equations for all 50 states) one has to decide whether or not to pool the data. The usual procedure is to first test for the overall homogeneity (equality) of the coefficients. If this hypothesis is not rejected, then a single equation is estimated with pooled data. If the hypothesis is rejected, further hypothesis testing may be necessary. For example, if the model contains more than one coefficient the equality constraint may be rejected for only a subset of the coefficients. In this case the data is pooled and dummy variables are used with the subset of coefficients for which the equality constraint does not hold. There are at least three problems with this procedure of pooling (or not pooling) after some preliminary tests of significance. First, as noted in Maddala, it raises problems about the inference from the pooled model. Second, there is the related question of what significance level to use when deciding whether or not to pool. Third, the choice of estimates to select from is quite limited. That is, one must pick either the pooled or the non-pooled estimate, even if these two estimates are very different. The problems suggest that an alternative (or hybrid) method of handling pooled cross-section and time-series data is needed. The purpose of this paper is to propose such a method.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 1982
- Accession Number
- ADA112503
Entities
People
- Robert C. Vogel
- Robert P. Trost
Organizations
- Center for Naval Analyses