Predictive Regression with p-Lags and Order-q Autoregressive Predictors

This paper considers predictive regressions, where y<sub>t </sub> is predicted by all p lags of x, here with x being autoregressive of order q, PR(p,q). The literature considers model properties in the cases where p=q. We demonstrate that the current augmented regression method can still reduce the bias in predictive coefficients, but its efficiency depends on correctly specifying both p and q. We propose an estimation framework for the predictive regression, PR(p,q), with a data-driven auto-selection of p and q to achieve the best bias reduction in predictive coefficients. The corresponding hypothesis testing procedure is also derived. The efficiency of the proposed method is demonstrated with simulations. Empirical applications to equity premium prediction illustrate the substantial difference between the estimates of our method and those obtained by the common predictive regressions with p=q

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Year of publication:	2020
Authors:	Jayetileke, Harshanie L.
Other Persons:	Wang, You-Gan (contributor) ; Zhu, Min (contributor)
Publisher:	[2020]: [S.l.] : SSRN
Subject:	Regressionsanalyse \| Regression analysis \| Prognoseverfahren \| Forecasting model \| Schätztheorie \| Estimation theory \| Kapitaleinkommen \| Capital income \| Prognose \| Forecast \| Autokorrelation \| Autocorrelation

Extent:	1 Online-Ressource (29 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments April 10, 2020 erstellt
Other identifiers:	10.2139/ssrn.3597761 [DOI]
Classification:	C14 - Semiparametric and Nonparametric Methods ; G12 - Asset Pricing
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10012834477