Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2014-08-11
Number: 14-103/III
Author-Name: Francisco Blasques
Author-Name: Siem Jan Koopman
Author-Name: André Lucas
Author-Workplace-Name: VU University Amsterdam, the Netherlands
Title: Optimal Formulations for Nonlinear Autoregressive Processes
Abstract: We develop optimal formulations for nonlinear autoregressive models by representing them as linear autoregressive models with time-varying temporal dependence coefficients. We propose a parameter updating scheme based on the score of the predictive likelihood function at each time point. The resulting time-varying autoregressive model is formulated as a nonlinear autoregressive model and is compared with threshold and smooth-transition autoregressive models. We establish the information theoretic optimality of the score driven nonlinear autoregressive process and the asymptotic theory for maximum likelihood parameter estimation. The performance of our model in extracting the time-varying or the nonlinear dependence for finite samples is studied in a Monte Carlo exercise. In our empirical study we present the in-sample and out-of-sample performances of our model for a weekly time series of unemployment insurance claims.
Classification-JEL: C13, C22, C32
Keywords: Asymptotic theory; Dynamic models, Observation driven time series models; Smooth-transition model; Time-Varying Parameters; Treshold autoregressive model
File-Url: https://papers.tinbergen.nl/14103.pdf
File-Format: application/pdf
File-Size: 5537677 bytes
Handle: RePEc:tin:wpaper:20140103