High dimensional stochastic regression with latent factors, endogeneity and nonlinearity

Jinyuan Chang, Bin Guo and Qiwei Yao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: α-mixing; dimension reduction; instrument variables; nonstationarity; time series (search for similar items in EconPapers)
JEL-codes: C13 C32 C39 (search for similar items in EconPapers)
Date: 2015-12-19
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (16)

Published in Journal of Econometrics, 19, December, 2015, 189(2), pp. 297-312. ISSN: 0304-4076

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:61886

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