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Factor Analysis Regression for Predictive Modeling with High-Dimensional Data

Author

Listed:
  • Randy Carter

    (State University of New York at Buffalo)

  • Netsanet Michael

    (The Boeing Company)

Abstract

Factor analysis regression (FAR) of $$y _i$$ y i on $${{\varvec{x}}}_i=(x _{1i},x _{2i},\ldots ,x _{pi})$$ x i = ( x 1 i , x 2 i , … , x pi ) , i = 1,2,...,n, has been studied only in the low-dimensional case $$(p n )$$ ( p > n ) . In this paper, we develop a high-dimensional version of FAR based on a computationally efficient method of factor extraction. We compare the performance of our high-dimensional FAR with partial least squares regression (PLSR) and principal component regression (PCR) under three underlying correlation structures: arbitrary correlation, factor model correlation structure, and when y is independent of x. Under each structure, we generated Monte Carlo training samples of sizes $$n

Suggested Citation

  • Randy Carter & Netsanet Michael, 2022. "Factor Analysis Regression for Predictive Modeling with High-Dimensional Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 115-132, September.
    • Handle: RePEc:spr:jqecon:v:20:y:2022:i:1:d:10.1007_s40953-022-00322-x
    DOI: 10.1007/s40953-022-00322-x
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    References listed on IDEAS

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