Consistent Estimation, Variable Selection, and Forecasting in FAVAR Models

35 Pages Posted: 17 Jan 2022 Last revised: 8 Feb 2022

See all articles by John C. Chao

John C. Chao

University of Maryland

Norman R. Swanson

Rutgers University - Department of Economics; Rutgers, The State University of New Jersey - Department of Economics

Date Written: February 7, 2022

Abstract

In the context of latent factor models that are widely used in economics, a common assumption made is one of factor pervasiveness, which implies that all available predictor or informative variables in a dataset, with the possible exception of a negligible number of them, load significantly on the underlying factors. In this paper, we analyze the more likely scenario where there is significant underlying heterogeneity in the sense that some of the variables load significantly on the underlying factors, while others are irrelevant, in the sense that they do not share any common dynamic structure with each other or with the relevant variables in the data set. We show that, even in such a setting, consistent factor estimation can be achieved if one pre-screens the variables and successfully prunes out the irrelevant ones. To do so, we introduce a new variable selection procedure that, with probability approaching one, correctly distinguishes between relevant and irrelevant variables. We study this problem within a factor-augmented VAR (FAVAR) framework, and show that by using variables selected via our pre-screening procedure to estimate the underlying factors and then inserting these factor estimates into -step ahead forecasting equations implied by the FAVAR model, we can obtain consistent estimates of the conditional mean function of said equations. In particular, our methodology allows the conditional mean function of a factor augmented forecast equation to be consistently estimable in a wide range of situations, including cases where violation of factor pervasiveness is such that consistent estimation is precluded in the absence of variable pre-screening.

Keywords: factor analysis, factor augmented vector autoregression, forecasting, moderate deviation asymptotics, principal components, self-normalization, variable selection

JEL Classification: C32, C33, C38, C52, C53, C55

Suggested Citation

Chao, John C. and Swanson, Norman Rasmus and Swanson, Norman Rasmus, Consistent Estimation, Variable Selection, and Forecasting in FAVAR Models (February 7, 2022). Available at SSRN: https://ssrn.com/abstract=4010249 or http://dx.doi.org/10.2139/ssrn.4010249

John C. Chao

University of Maryland ( email )

Department of Economics
College Park, MD 20742
United States
301-405-1579 (Phone)
301-408-3542 (Fax)

Norman Rasmus Swanson (Contact Author)

Rutgers, The State University of New Jersey - Department of Economics ( email )

75 Hamilton Street
New Brunswick, NJ 08901
United States
848-932-7432 (Phone)

HOME PAGE: http://econweb.rutgers.edu/nswanson/

Rutgers University - Department of Economics ( email )

NJ
United States

HOME PAGE: http://econweb.rutgers.edu/nswanson/

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