IDENTIFYING LONG-RUN RISKS: A BAYESIAN MIXED-FREQUENCY APPROACH

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 cashflow (consumption, output, dividends) growth that feature measurement errors, timevarying volatilities, and mixed-frequency observations....

Full description

Saved in:
Bibliographic Details
Published in:Econometrica 2018-03, Vol.86 (2), p.617-654
Main Authors: Schorfheide, Frank, Song, Dongho, Yaron, Amir
Format: Article
Language:English
Subjects:
Asset pricing
Bayesian analysis
Bayesian inference
Cash flow
Consumption
consumption dynamics
long‐run risks
Measurement
measurement errors
mixed frequency observations
Multivariate analysis
nonlinear state‐space model
particle MCMC
Prices
Risk factors
stochastic volatility
Tracking
Volatility
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 cashflow (consumption, output, dividends) growth that feature measurement errors, timevarying 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.
ISSN:0012-9682
1468-0262
DOI:10.3982/ECTA14308