Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/107817
Authors: 
Blasques, Francisco
Koopman, Siem Jan
Lucas, André
Year of Publication: 
2014
Series/Report no.: 
Tinbergen Institute Discussion Paper 14-103/III
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.
Subjects: 
Asymptotic theory
Dynamic models
Observation driven time series models
Smooth-transition model
Time-Varying Parameters
Treshold autoregressive model
JEL: 
C13
C22
C32
Document Type: 
Working Paper

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