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Predictive regressions

Predictive regressions
Predictive regressions
Predictive regressions are a widely used econometric environment for assessing the predictability of economic and financial variables using past values of one or more predictors. The nature of the applications considered by practitioners often involve the use of predictors that have highly persistent, smoothly varying dynamics as opposed to the much noisier nature of the variable being predicted. This imbalance tends to affect the accuracy of the estimates of the model parameters and the validity of inferences about them when one uses standard methods that do not explicitly recognize this and related complications. A growing literature aimed at introducing novel techniques specifically designed to produce accurate inferences in such environments ensued. The frequent use of these predictive regressions in applied work has also led practitioners to question the validity of viewing predictability within a linear setting that ignores the possibility that predictability may occasionally be switched off. This in turn has generated a new stream of research aiming at introducing regime-specific behavior within predictive regressions in order to explicitly capture phenomena such as episodic predictability.
predictability, Forecasting
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Gonzalo, Jesús
48015f9d-eef0-4ebd-8f2b-cbe7aa0cf667
Pitarakis, Jean-Yves
ee5519ae-9c0f-4d79-8a3a-c25db105bd51
Gonzalo, Jesús
48015f9d-eef0-4ebd-8f2b-cbe7aa0cf667

Pitarakis, Jean-Yves and Gonzalo, Jesús (2019) Predictive regressions. Oxford Research Encyclopedias (Economics and Finance). (doi:10.1093/acrefore/9780190625979.013.494).

Record type: Article

Abstract

Predictive regressions are a widely used econometric environment for assessing the predictability of economic and financial variables using past values of one or more predictors. The nature of the applications considered by practitioners often involve the use of predictors that have highly persistent, smoothly varying dynamics as opposed to the much noisier nature of the variable being predicted. This imbalance tends to affect the accuracy of the estimates of the model parameters and the validity of inferences about them when one uses standard methods that do not explicitly recognize this and related complications. A growing literature aimed at introducing novel techniques specifically designed to produce accurate inferences in such environments ensued. The frequent use of these predictive regressions in applied work has also led practitioners to question the validity of viewing predictability within a linear setting that ignores the possibility that predictability may occasionally be switched off. This in turn has generated a new stream of research aiming at introducing regime-specific behavior within predictive regressions in order to explicitly capture phenomena such as episodic predictability.

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More information

Published date: November 2019
Keywords: predictability, Forecasting

Identifiers

Local EPrints ID: 436248
URI: http://eprints.soton.ac.uk/id/eprint/436248
PURE UUID: 9e95f62a-ebc0-4683-a649-a290d328f4cc
ORCID for Jean-Yves Pitarakis: ORCID iD orcid.org/0000-0002-6305-7421

Catalogue record

Date deposited: 04 Dec 2019 17:30
Last modified: 16 May 2020 00:33

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