Identifying Long‐Run Risks: A Bayesian Mixed‐Frequency Approach. Issue 2 (31st March 2018)

• Record Type: Journal Article
• Authors: Schorfheide, Frank; Song, Dongho; Yaron, Amir
• Is Part Of: Econometrica. Volume 86:Issue 2(2018)
• Page Start: 617
• Page End: 654
• Publication Date: 2018-03-31
• Subjects: Asset pricing -- Bayesian inference -- consumption dynamics -- long‐run risks -- measurement errors -- mixed frequency observations -- nonlinear state‐space model -- particle MCMC -- stochastic volatility; Econometrics -- Periodicals; Economics, Mathematical -- Periodicals; Economics -- Periodicals; Économétrie -- Périodiques; Mathématiques économiques -- Périodiques; Économie politique -- Périodiques; 330.05
• DOI: 10.3982/ECTA14308
• Languages: English
• ISSNs: 0012-9682

Abstract

Abstract : We document that consumption growth rates are far from i.i.d. and have a highly persistent component. First, we estimate univariate and multivariate models of cash‐flow (consumption, output, dividends) growth that feature measurement errors, time‐varying volatilities, and mixed‐frequency observations. Monthly consumption data are important for identifying the stochastic volatility process; yet the data are contaminated, which makes the inclusion of measurement errors essential for identifying the predictable component. Second, we develop a novel state‐space model for cash flows and asset prices that imposes the pricing restrictions of a representative‐agent endowment economy with recursive preferences. To estimate this model, we use a particle MCMC approach that exploits the conditional linear structure of the approximate equilibrium. Once asset return data are included in the estimation, we find even stronger evidence for the persistent component and are able to identify three volatility processes: the one for the predictable cash‐flow component is crucial for asset pricing, whereas the other two are important for tracking the data. Our model generates asset prices that are largely consistent with the data in terms of sample moments and predictability features. The state‐space approach allows us to track over time the evolution of the predictable component, the volatility processes, the decomposition of the equity premium into risk factors, and the variance decomposition of asset prices.