Quantile Autoregressive Distributed Lag Model with an Application to House Price Returns*
| Published date | 01 April 2013 |
| Author | Gabriel Montes‐Rojas,Antonio F. Galvao JR.,Sung Y. Park |
| Date | 01 April 2013 |
| DOI | http://doi.org/10.1111/j.1468-0084.2011.00683.x |
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©Blackwell Publishing Ltd and the Department of Economics, University of Oxford 2011. Published by Blackwell Publishing Ltd,
9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 75, 2 (2013) 0305-9049
doi: 10.1111/j.1468-0084.2011.00683.x
Quantile Autoregressive Distributed Lag Model with
an Application to House Price ReturnsÅ
Antonio F. Galvao JR.,†Gabriel Montes-Rojas‡ and
Sung Y. Park§
†Department of Economics, University of Wisconsin-Milwaukee and University of Iowa, Iowa City,
IA 52242, USA (e-mail: antonio-galvao@uiowa.edu)
‡Department of Economics, City University London, London EC1V 0HB, UK
(e-mail: gabriel.montes-rojas.1@city.ac.uk)
§Department of Economics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
(e-mail: sungpark@cuhk.edu.hk)
Abstract
This article studies quantile regression in an autoregressive dynamic framework with exo-
genous stationary covariates. We demonstrate the potential of the quantile autoregressive
distributed lag model with an application to house price returns in the United Kingdom.
The results show that house price returns present a heterogeneous autoregressive behaviour
across the quantiles. Real GDP growth and interest rates also have an asymmetric impact
on house prices variations.
I. Introduction
Asymmetric dynamic responses are common in the time series empirical literature. For
instance, Beaudry and Koop (1993) show that positive shocks to the US GDP are more
persistent than negative shocks. Poterba (1991) and Capozza et al. (2002) among others,
present evidence on the asymmetric responses of house prices to income shocks. The occur-
rence of these asymmetries call into question the usefulness of models with time invariant
structures as means of modelling such series. Quantile regression (QR) is a statistical
method for estimating models of conditional quantile functions, which offers a systematic
strategy for examining how covariates influence the location, scale and shape of the entire
response distribution, therefore exposing a variety of heterogeneity in response dynam-
ics. Koenker and Xiao (2006) introduced quantile autoregression (QAR) models in which
the autoregressive coefficients can be expressed as monotone functions of a single, scalar
random variable. QAR models are becoming increasingly popular, and there is a growing
ÅThe authors would like to express their appreciation to Christopher Adam, two anonymous referees, Dan Bern-
hardt, Odilon Camara, Roger Koenker, Luiz Lima, Simone Manganelli and the participants of seminars at University
of Wisconsin-Milwaukeeand the 2008 XMU-HUB Workshop on Economics and Financial Econometrics for helpful
comments and discussions. All the remaining errors are ours.
JEL Classification numbers: C14, C32.
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