Picture of offices in the City of London

Open Access research that is better understanding work in the global economy...

Strathprints makes available scholarly Open Access content by researchers in the Department of Work, Employment & Organisation based within Strathclyde Business School.

Better understanding the nature of work and labour within the globalised political economy is a focus of the 'Work, Labour & Globalisation Research Group'. This involves researching the effects of new forms of labour, its transnational character and the gendered aspects of contemporary migration. A Scottish perspective is provided by the Scottish Centre for Employment Research (SCER). But the research specialisms of the Department of Work, Employment & Organisation go beyond this to also include front-line service work, leadership, the implications of new technologies at work, regulation of employment relations and workplace innovation.

Explore the Open Access research of the Department of Work, Employment & Organisation. Or explore all of Strathclyde's Open Access research...

Large time-varying parameter VARs

Koop, Gary and Korobilis, Dimitris (2013) Large time-varying parameter VARs. Journal of Econometrics, 177 (2). pp. 185-198. ISSN 0304-4076

[img]
Preview
Text (koop-korobilis-JE2013-large-time-varying-parameter-VARs)
koop_korobilis_JE2013_large_time_varying_parameter_VARs.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (398kB) | Preview

Abstract

In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVP-VAR so that its dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a smaller TVP-VAR at others. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach.