Microeconometric dynamic panel data methods: Model specification and selection issues

Abstract

A motivated strategy is presented to find step by step an adequate model specification and a matching set of instrumental variables by applying the programming tools provided by the Stata package Xtabond2. The aim is to implement generalized method of moment techniques such that useful and reasonably accurate inferences can be extracted from an observational panel data set on a single microeconometric structural presumably dynamic behavioral relationship. In the suggested specification search three comprehensive heavily interconnected goals are pursued: (i) to include all the relevant appropriately transformed possibly lagged regressors, as well as any interactions between these, if it is required to relax the otherwise very strict homogeneity restrictions on the dynamic impacts of the explanatories in standard linear panel data models; (ii) to correctly classify all regressors as either endogenous, predetermined or exogenous, as well as being either effect-stationary or effect-nonstationary, implying which internal variables could represent valid and relatively strong instruments; (iii) to enhance the accuracy of inference in finite samples by omitting redundant regressors and by profitably reducing the space spanned by the full set of available internal instruments. For the various tests which trigger the decisions to be made in the sequential selection process the relevant considerations are spelled out to interpret the magnitude of p-values. Also the complexities to estimate and interpret the ultimately established dynamic impacts are explained. Finally the developed strategy is applied to a classic data set and is shown to yield new insights.