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Dynamic Vector Mode Regression

Kemp, Gordon C.R., Parente, Paulo M.D.C. and Santos Silva, Joao (2019) Dynamic Vector Mode Regression Journal of Business & Economic Statistics.

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Abstract

We study the semi-parametric estimation of the conditional mode of a random vector that has a continuous conditional joint density with a well-defined global mode. A novel full-system estimator is proposed and its asymptotic properties are studied. We specifically consider the estimation of vector autoregressive conditional mode models and of systems of linear simultaneous equations defined by mode restrictions. The proposed estimator is easy to implement and simulations suggest that it is reasonably behaved in finite samples. An empirical example illustrates the application of the proposed methods, including its use to obtain multi-step forecasts and to construct impulse response functions.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > School of Economics
Authors :
NameEmailORCID
Kemp, Gordon C.R.
Parente, Paulo M.D.C.
Santos Silva, Joaojmcss@surrey.ac.uk
Date : 1 February 2019
DOI : 10.1080/07350015.2018.1562935
Copyright Disclaimer : This is an Accepted Manuscript of an article to be published by Taylor & Francis in Journal of Business & Economic Statistics, and will be available online: https://www.tandfonline.com/toc/ubes20/current
Uncontrolled Keywords : Impulse response functions, Multivariate conditional mode, Robust regression, Simultaneous equations, Vector autoregression.
Depositing User : Melanie Hughes
Date Deposited : 04 Dec 2018 12:17
Last Modified : 05 Apr 2019 09:31
URI: http://epubs.surrey.ac.uk/id/eprint/849975

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