Approximate factor models with restrictions on the loadings may be interesting both for structural analysis (simpler structures are easier to interpret) and forecasting (parsimonious models typically deliver superior forecasting performances). However, the issue is largely unexplored. In particular, no currently available test is entirely suitable for the empirically important case of non-stationary data. Building on the intuition that de-factoring the data under a correct set of restrictions will lower the number of factors, a bootstrap test based on the comparison of the number of factors selected for the raw and de-factored data is proposed. The test is shown analytically to be asymptotically valid and by simulation to have good small sample properties.

Evaluating restricted Common Factor models for non-stationary data / Iorio, Francesca Di; Fachin, Stefano. - In: ECONOMETRICS AND STATISTICS. - ISSN 2452-3062. - (2020), pp. 1-12. [10.1016/j.ecosta.2020.10.004]

Evaluating restricted Common Factor models for non-stationary data

Fachin, Stefano
Secondo
2020

Abstract

Approximate factor models with restrictions on the loadings may be interesting both for structural analysis (simpler structures are easier to interpret) and forecasting (parsimonious models typically deliver superior forecasting performances). However, the issue is largely unexplored. In particular, no currently available test is entirely suitable for the empirically important case of non-stationary data. Building on the intuition that de-factoring the data under a correct set of restrictions will lower the number of factors, a bootstrap test based on the comparison of the number of factors selected for the raw and de-factored data is proposed. The test is shown analytically to be asymptotically valid and by simulation to have good small sample properties.
2020
Non-stationary factor model; restricted factor models; stationary bootstrap
01 Pubblicazione su rivista::01a Articolo in rivista
Evaluating restricted Common Factor models for non-stationary data / Iorio, Francesca Di; Fachin, Stefano. - In: ECONOMETRICS AND STATISTICS. - ISSN 2452-3062. - (2020), pp. 1-12. [10.1016/j.ecosta.2020.10.004]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1460771
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