EconStor >
Federal Reserve Bank of New York >
Staff Reports, Federal Reserve Bank of New York >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/60703
  
Title:A flexible approach to parametric inference in nonlinear time series models PDF Logo
Authors:Koop, Gary
Potter, Simon
Issue Date:2007
Series/Report no.:Staff Report, Federal Reserve Bank of New York 285
Abstract:Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible parametric model that accommodates virtually any of these specifications—and does so in a simple way that allows for straightforward Bayesian inference. The basic idea underlying our model is that it adds two concepts to a standard state space framework. These ideas are ordering and distance. By ordering the data in different ways, we can accommodate a wide range of nonlinear time series models. By allowing the state equation variances to depend on the distance between observations, the parameters can evolve in a wide variety of ways, allowing for models that exhibit abrupt change as well as those that permit a gradual evolution of parameters. We show how our model will (approximately) nest almost every popular model in the regime-switching and structural break literatures. Bayesian econometric methods for inference in this model are developed. Because we stay within a state space framework, these methods are relatively straightforward and draw on the existing literature. We use artificial data to show the advantages of our approach and then provide two empirical illustrations involving the modeling of real GDP growth.
Subjects:Bayesian, structural break, threshold autoregression, regime switching, state space model
JEL:C11
C22
E17
Document Type:Working Paper
Appears in Collections:Staff Reports, Federal Reserve Bank of New York

Files in This Item:
File Description SizeFormat
54010891X.pdf399 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:http://hdl.handle.net/10419/60703

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.