Modeling Nonlinear Autoregressive Distributed Lag Models: A New Approach

Abstract

It is a common practice in econometrics that estimation is carried out in terms of the reduced form parameters and the structural form parameters are retrieved using the functional relationship between structural form parameters and the reduced form parameters. The reduced form of many useful economic models is a nonlinear distributed lag model (NLADL) with error structure which may have autocorrelation. In addition, the relationship between the reduced form and the structural parameters is often nonlinear and in a ratio form. In such situations existing sampling theory estimation procedures result in estimators for the structural parameters which do not have finite moments and do not possess optimal sampling properties. As an alternative, we propose a two step Bayesian estimation method. The Bayesian method has great potential and allows us to obtain the posterior probability density functions of all parameters of interest. In particular, its application for the analysis of adaptive expectation partial adjustment models for which the reduced form is a NLADL model, has been found extremely useful. An application to Nerlove’s supply response function supports the proposed methodology.