High dimensional stochastic regression with latent factors, endogeneity and nonlinearity

Chang, Jinyuan, Guo, Bin and Yao, Qiwei ORCID: 0000-0003-2065-8486 (2015) High dimensional stochastic regression with latent factors, endogeneity and nonlinearity. Journal of Econometrics, 189 (2). pp. 297-312. ISSN 0304-4076

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Abstract

We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors, a linear combination of some latent and serially correlated factors, and a vector white noise. We investigate the inference without imposing stationary conditions on the target multivariate time series, the regressors and the underlying factors. Furthermore we deal with the the endogeneity that there exist correlations between the observed regressors and the unobserved factors. We also consider the model with nonlinear regression term which can be approximated by a linear regression function with a large number of regressors. The convergence rates for the estimators of regression coefficients, the number of factors, factor loading space and factors are established under the settings when the dimension of time series and the number of regressors may both tend to infinity together with the sample size. The proposed method is illustrated with both simulated and real data examples.

Item Type:	Article
Official URL:	http://www.sciencedirect.com/science/article/pii/S...
Additional Information:	© 2015 Elsevier B.V.
Divisions:	Statistics
Subjects:	H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C32 - Time-Series Models C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C39 - Other
Date Deposited:	11 May 2015 09:24
Last Modified:	20 Nov 2024 22:57
Funders:	Center for Statistical Science at Peking University, Australian Research Council, Engineering and Physical Sciences Research Council
URI:	http://eprints.lse.ac.uk/id/eprint/61886

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