UNIFIED INTERVAL ESTIMATION FOR RANDOM COEFFICIENT AUTOREGRESSIVE MODELS. (7th February 2014)

• Record Type: Journal Article
• Authors: Hill, Jonathan; Peng, Liang
• Is Part Of: Journal of time series analysis. Volume 35:Number 3(2014:May)
• Page Start: 282
• Page End: 297
• Publication Date: 2014-02-07
• Subjects: Time-series analysis -- Periodicals; 519.232
• DOI: 10.1111/jtsa.12064
• Languages: English
• ISSNs: 0143-9782

Abstract

<abstract abstract-type="main" id="jtsa12064-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="jtsa12064-para-0001">The consistency of the quasi‐maximum likelihood estimator for random coefficient autoregressive models requires that the coefficient be a non‐degenerate random variable. In this article, we propose empirical likelihood methods based on weighted‐score equations to construct a confidence interval for the coefficient. We do not need to distinguish whether the coefficient is random or deterministic and whether the process is stationary or non‐stationary, and we present two classes of equations depending on whether a constant trend is included in the model. A simulation study confirms the good finite‐sample behaviour of our resulting empirical likelihood‐based confidence intervals. We also apply our methods to study US macroeconomic data.</p> </abstract>