Structural-Break Models under Mis-specification: Implications for Forecasting

47 Pages Posted: 23 Apr 2013 Last revised: 20 Jun 2015

See all articles by Bonsoo Koo

Bonsoo Koo

Monash Business School

Myung Hwan Seo

Seoul National University - School of Economics

Date Written: April 18, 2013

Abstract

This paper revisits the least squares estimator of the linear regression with a structural break. We view the model as an approximation to the true data generating process whose exact nature is unknown but perhaps changing over time either continuously or with some jumps. This view is widely held in the forecasting literature and under this view, the time series dependence property of all the observed variables is unstable as well. We establish that the rate of convergence of the estimator to a properly defined limit is much slower than the standard super consistent rate, even slower than the square root of the sample size T and as slow as the cube root of T. We also provide an asymptotic distribution of the estimator and that of the Gaussian quasi likelihood ratio statistic for a certain class of true data generating process. We relate our finding to current forecast combination methods and bagging and propose a new averaging scheme. The performance of various contemporary forecasting methods is compared to ours using a number of macroeconomic data.

Keywords: structural break, forecasting, mis-specification, cube-root asymptotics, bagging

JEL Classification: C13, C22, C53

Suggested Citation

Koo, Bonsoo and Seo, Myung Hwan, Structural-Break Models under Mis-specification: Implications for Forecasting (April 18, 2013). Available at SSRN: https://ssrn.com/abstract=2253679 or http://dx.doi.org/10.2139/ssrn.2253679

Bonsoo Koo (Contact Author)

Monash Business School ( email )

Wellington Road
Clayton, Victoria 3168
Australia
+61 3 9905 0547 (Phone)
+61 3 9905 5474 (Fax)

Myung Hwan Seo

Seoul National University - School of Economics ( email )

San 56-1, Silim-dong, Kwanak-ku
Seoul 151-742

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