Random Subspace Local Projections

Viet Hoang Dinh, Didier Nibbering and Benjamin Wong

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over regressions estimated over different combinations of subsets of these controls. We document three key results: (i) Our approach can successfully recover the impulse response function in a Monte Carlo exercise where we simulate data from a real business cycle model with fiscal foresight. (ii) Our results suggest that random subspace methods are more accurate than factor models if the underlying large data set has a factor structure similar to typical macroeconomic data sets such as FRED-MD. (iii) Our approach leads to differences in the estimated impulse response functions relative to standard methods when applied to two widely-studied empirical applications.

Keywords: Local Projections; Random Subspace; Impulse Response Functions; Large Data Sets (search for similar items in EconPapers)
JEL-codes: C22 E32 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2023-07
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
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Citations: View citations in EconPapers (1)

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https://cama.crawford.anu.edu.au/sites/default/fil ... h_nibbering_wong.pdf

Related works:
Working Paper: Random Subspace Local Projections (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2023-34

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