Search Results
Working Paper
Dividend Momentum and Stock Return Predictability: A Bayesian Approach
A long tradition in macro finance studies the joint dynamics of aggregate stock returns and dividends using vector autoregressions (VARs), imposing the cross-equation restrictions implied by the Campbell-Shiller (CS) identity to sharpen inference. We take a Bayesian perspective and develop methods to draw from any posterior distribution of a VAR that encodes a priori skepticism about large amounts of return predictability while imposing the CS restrictions. In doing so, we show how a common empirical practice of omitting dividend growth from the system amounts to imposing the extra ...
Working Paper
A narrative approach to a fiscal DSGE model
This version: March 28, 2016 First version: February 2014 {{p}} Structural DSGE models are used both for analyzing policy and the sources of business cycles. Conclusions based on full structural models are, however, potentially affected by misspecification. A competing method is to use partially identified VARs based on narrative shocks. This paper asks whether both approaches agree. First, I show that, theoretically, the narrative VAR approach is valid in a class of DSGE models with Taylor-type policy rules. Second, I quantify whether the two approaches also agree empirically, that is, ...
Working Paper
Addressing COVID-19 Outliers in BVARs with Stochastic Volatility
Incoming data in 2020 posed sizable challenges for the use of VARs in economic analysis: Enormous movements in a number of series have had strong effects on parameters and forecasts constructed with standard VAR methods. We propose the use of VAR models with time-varying volatility that include a treatment of the COVID extremes as outlier observations. Typical VARs with time-varying volatility assume changes in uncertainty to be highly persistent. Instead, we adopt an outlier-adjusted stochastic volatility (SV) model for VAR residuals that combines transitory and persistent changes in ...
Working Paper
Reconciling VAR-based Forecasts with Survey Forecasts
This paper proposes a novel Bayesian approach to jointly model realized data and survey forecasts of the same variable in a vector autoregression (VAR). In particular, our method imposes a prior distribution on the consistency between the forecast implied by the VAR and the survey forecast for the same variable. When the prior is placed on unconditional forecasts from the VAR, the prior shapes the posterior of the reduced-form VAR coefficients. When the prior is placed on conditional forecasts (specifically, impulse responses), the prior shapes the posterior of the structural VAR ...
Working Paper
Forecasting Economic Activity with Mixed Frequency Bayesian VARs
Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate a large number of mixed frequency indicators into forecasts of economic activity. This paper evaluates the forecast performance of MF-BVARs relative to surveys of professional forecasters and investigates the influence of certain specification choices on this performance. We leverage a novel real-time dataset to conduct an out-of-sample forecasting exercise for U.S. real gross domestic product (GDP). MF-BVARs are shown to provide an attractive alternative to surveys of professional forecasters for ...
Working Paper
Measuring Uncertainty and Its Impact on the Economy
We propose a new framework for measuring uncertainty and its effects on the economy, based on a large VAR model with errors whose stochastic volatility is driven by two common unobservable factors, representing aggregate macroeconomic and financial uncertainty. The uncertainty measures can also influence the levels of the variables so that, contrary to most existing measures, ours reflect changes in both the conditional mean and volatility of the variables, and their impact on the economy can be assessed within the same framework. Moreover, identification of the uncertainty shocks is ...
Working Paper
FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications
We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor market, production, and housing variables. We conduct two sets of real-time forecasting exercises. The first forecasts state-level labor-market variables using five different models and different levels of industrially-disaggregated data. The second forecasts a national-level variable exploiting the cross-section of state data. The state-forecasting experiments ...
Working Paper
Can Forecast Errors Predict Financial Crises? Exploring the Properties of a New Multivariate Credit Gap
Yes, they can. I propose a new method to detect credit booms and busts from multivariate systems -- monetary Bayesian vector autoregressions. When observed credit is systematically higher than credit forecasts justified by real economic activity variables, a positive credit gap emerges. The methodology is tested for 31 advanced and emerging market economies. The resulting credit gaps fit historical evidence well and detect turning points earlier, outperforming the credit-to-GDP gaps in signaling financial crises, especially at longer horizons. The results survive in real time and can shed ...
Working Paper
Have Standard VARs Remained Stable since the Crisis?
Small or medium-scale VARs are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism. This requires the VAR parameters to be stable over the evaluation and forecast sample, or to explicitly consider parameter time variation. The earlier literature focused on whether there were sizable parameter changes in the early 1980s, in either the conditional mean or variance parameters, and in the subsequent period till the beginning of the new century. In this paper we conduct a similar analysis but focus on the effects of the recent crisis. Using a ...
Working Paper
Addressing COVID-19 Outliers in BVARs with Stochastic Volatility
The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that ...