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Semiparametric ultra-high dimensional model averaging of nonlinear dynamic time series

Semiparametric ultra-high dimensional model averaging of nonlinear dynamic time series
Semiparametric ultra-high dimensional model averaging of nonlinear dynamic time series
We propose two semiparametric model averaging schemes for nonlinear dynamic time series regression models with a very large number of covariates including exogenous regressors and auto-regressive lags. Our objective is to obtain more accurate estimates and forecasts of time series by using a large number of conditioning variables in a nonparametric way. In the first scheme, we introduce a Kernel Sure Independence Screening (KSIS) technique to screen out the regressors whose marginal regression (or auto-regression) functions do not make a significant contribution to estimating the joint multivariate regression function; we then propose a semiparametric penalized method of Model Averaging MArginal Regression (MAMAR) for the regressors and auto-regressors that survive the screening procedure, to further select the regressors that have significant effects on estimating the multivariate regression function and predicting the future values of the response variable. In the second scheme, we impose an approximate factor modelling structure on the ultra-high dimensional exogenous regressors and use the principal component analysis to estimate the latent common factors; we then apply the penalized MAMAR method to select the estimated common factors and the lags of the response variable that are significant. In each of the two schemes, we construct the optimal combination of the significant marginal regression and auto-regression functions. Asymptotic properties for these two schemes are derived under some regularity conditions. Numerical studies including both simulation and an empirical application to forecasting inflation are given to illustrate the proposed methodology.
Kernel smoother, penalized MAMAR, principal component analysis, semiparametric approximation, sure independence screening, ultra-high dimensional time series.
0162-1459
Chen, Jia
3b32661d-16b8-46ed-9fee-8cbacd390119
Li, Degui
e341f702-23cd-4c1a-91a8-3b7aa3dfda15
Linton, Oliver
36fa04bf-d781-45bd-bd7f-c28f68e5df6e
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Chen, Jia
3b32661d-16b8-46ed-9fee-8cbacd390119
Li, Degui
e341f702-23cd-4c1a-91a8-3b7aa3dfda15
Linton, Oliver
36fa04bf-d781-45bd-bd7f-c28f68e5df6e
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95

Chen, Jia, Li, Degui, Linton, Oliver and Lu, Zudi (2017) Semiparametric ultra-high dimensional model averaging of nonlinear dynamic time series. Journal of the American Statistical Association. (doi:10.1080/01621459.2017.1302339).

Record type: Article

Abstract

We propose two semiparametric model averaging schemes for nonlinear dynamic time series regression models with a very large number of covariates including exogenous regressors and auto-regressive lags. Our objective is to obtain more accurate estimates and forecasts of time series by using a large number of conditioning variables in a nonparametric way. In the first scheme, we introduce a Kernel Sure Independence Screening (KSIS) technique to screen out the regressors whose marginal regression (or auto-regression) functions do not make a significant contribution to estimating the joint multivariate regression function; we then propose a semiparametric penalized method of Model Averaging MArginal Regression (MAMAR) for the regressors and auto-regressors that survive the screening procedure, to further select the regressors that have significant effects on estimating the multivariate regression function and predicting the future values of the response variable. In the second scheme, we impose an approximate factor modelling structure on the ultra-high dimensional exogenous regressors and use the principal component analysis to estimate the latent common factors; we then apply the penalized MAMAR method to select the estimated common factors and the lags of the response variable that are significant. In each of the two schemes, we construct the optimal combination of the significant marginal regression and auto-regression functions. Asymptotic properties for these two schemes are derived under some regularity conditions. Numerical studies including both simulation and an empirical application to forecasting inflation are given to illustrate the proposed methodology.

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CLLL-JASA02062016R-14JAN2017 - Accepted Manuscript
Restricted to Repository staff only until 14 May 2018.
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Text
CLLL-JASA02062016R-supp - Accepted Manuscript
Restricted to Repository staff only until 14 May 2018.
Request a copy

More information

Accepted/In Press date: 14 February 2017
e-pub ahead of print date: 3 April 2017
Keywords: Kernel smoother, penalized MAMAR, principal component analysis, semiparametric approximation, sure independence screening, ultra-high dimensional time series.
Organisations: Statistics

Identifiers

Local EPrints ID: 411652
URI: https://eprints.soton.ac.uk/id/eprint/411652
ISSN: 0162-1459
PURE UUID: fde3f3fb-c145-4f5d-899e-7998844f972a
ORCID for Zudi Lu: ORCID iD orcid.org/0000-0003-0893-832X

Catalogue record

Date deposited: 21 Jun 2017 16:33
Last modified: 16 Jul 2019 18:22

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