Identifying Long-Run Risks: A Bayesian Mixed-Frequency Approach

53 Pages Posted: 29 Sep 2013

See all articles by Frank Schorfheide

Frank Schorfheide

University of Pennsylvania - Department of Economics; Centre for Economic Policy Research (CEPR); National Bureau of Economic Research (NBER); University of Pennsylvania - The Penn Institute for Economic Research (PIER)

Dongho Song

Johns Hopkins University - Carey Business School

Amir Yaron

University of Pennsylvania -- Wharton School of Business; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: September 25, 2013

Abstract

We develop a nonlinear state-space model that captures the joint dynamics of consumption, dividend growth, and asset returns. Building on Bansal and Yaron (2004), our model consists of an economy containing a common predictable component for consumption and dividend growth and multiple stochastic volatility processes. The estimation is based on annual consumption data from 1929 to 1959, monthly consumption data after 1959, and monthly asset return data throughout. We maximize the span of the sample to recover the predictable component and use high-frequency data, whenever available, to efficiently identify the volatility processes. Our Bayesian estimation provides strong evidence for a small predictable component in consumption growth (even if asset return data are omitted from the estimation). Three independent volatility processes capture different frequency dynamics; our measurement error specification implies that consumption is measured much more precisely at an annual than monthly frequency; and the estimated model is able to capture key asset-pricing facts of the data.

Keywords: Consumption (Economics), Bayesian Estimates

Suggested Citation

Schorfheide, Frank and Song, Dongho and Yaron, Amir, Identifying Long-Run Risks: A Bayesian Mixed-Frequency Approach (September 25, 2013). FRB of Philadelphia Working Paper No. 13-39, Available at SSRN: https://ssrn.com/abstract=2332045 or http://dx.doi.org/10.2139/ssrn.2332045

Frank Schorfheide (Contact Author)

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
133 South 36th Street
Philadelphia, PA 19104-6297
United States

HOME PAGE: http://www.econ.upenn.edu/~schorf

Centre for Economic Policy Research (CEPR)

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United Kingdom

National Bureau of Economic Research (NBER) ( email )

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University of Pennsylvania - The Penn Institute for Economic Research (PIER) ( email )

Philadelphia, PA
United States

Dongho Song

Johns Hopkins University - Carey Business School ( email )

Baltimore, MD 20036-1984
United States

Amir Yaron

University of Pennsylvania -- Wharton School of Business ( email )

The Wharton School
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Philadelphia, PA 19104
United States
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215-898-6200 (Fax)

National Bureau of Economic Research (NBER) ( email )

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