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Working Paper
Real-time forecasting with a mixed-frequency VAR
This paper develops a vector autoregression (VAR) for macroeconomic time series which are observed at mixed frequencies ? quarterly and monthly. The mixed-frequency VAR is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. Using a real-time data set, we generate and evaluate forecasts from the mixed-frequency VAR and compare them to forecasts from a VAR that is estimated based on data time-aggregated to quarterly frequency. We document how information that becomes available within the quarter improves the forecasts in real time.
Working Paper
A Bayesian evaluation of alternative models of trend inflation
With the concept of trend inflation now widely understood as to be important as a measure of the public's perception of the inflation goal of the central bank and important to the accuracy of longer-term inflation forecasts, this paper uses Bayesian methods to assess alternative models of trend inflation. Reflecting models common in reduced-form inflation modeling and forecasting, we specify a range of models of inflation, including: AR with constant trend; AR with trend equal to last period's inflation rate; local level model; AR with random walk trend; AR with trend equal to the long-run ...
Working Paper
Identifying long-run risks: a bayesian mixed-frequency approach
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 ...
Working Paper
A Bayesian evaluation of alternative models of trend inflation
The concept of trend inflation is important in making accurate inflation forecasts. However, there is little consensus on how the trend in inflation should be modeled. While some studies suggest a survey-based measure of long-run inflation expectations as a good empirical proxy for trend inflation, others have argued for a statistical exercise of decomposing inflation data into trend and cycle components. In this paper, we assess alternative models of trend inflation based on the accuracy of medium-term inflation forecasts. To incorporate recent evidence on the time-varying macroeconomic ...
Working Paper
It’s not just for inflation: The usefulness of the median CPI in BVAR forecasting
In this paper we investigate the forecasting performance of the median CPI in a variety of Bayesian VARs (BVARs) that are often used for monetary policy. Until now, the use of trimmed-mean price statistics in forecasting inflation has often been relegated to simple univariate or ?Philips-Curve? approaches, thus limiting their usefulness in applications that require consistent forecasts of multiple macro variables. We find that inclusion of an extreme trimmed-mean measure?the median CPI?significantly improves the forecasts of both headline and core CPI. across our wide-ranging set of BVARs. ...
Working Paper
Sequential Monte Carlo sampling for DSGE models
We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models, wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using three examples consisting of an artificial state-space model, the Smets and Wouters (2007) model, and Schmitt-Grohe and Uribe's (2012) news shock model we show that the SMC algorithm is better suited for multi-modal and irregular posterior distributions than the widely-used random walk Metropolis-Hastings algorithm. Unlike standard Markov chain Monte Carlo ...
Report
Belief updating among college students: evidence from experimental variation in information
We investigate how college students form and update their beliefs about future earnings using a unique ?information? experiment. We provide college students true information about the population distribution of earnings and observe how this information causes respondents to update their beliefs about their own future earnings. We show that college students are substantially misinformed about population earnings and logically revise their self-beliefs in response to the information we provide, with larger revisions when the information is more specific and is good news. We classify the ...
Working Paper
Managing self-confidence: theory and experimental evidence
Evidence from social psychology suggests that agents process information about their own ability in a biased manner. This evidence has motivated exciting research in behavioral economics, but also garnered critics who point out that it is potentially consistent with standard Bayesian updating. We implement a direct experimental test. We study a large sample of 656 undergraduate students, tracking the evolution of their beliefs about their own relative performance on an IQ test as they receive noisy feedback from a known data-generating process. Our design lets us repeatedly measure the ...
Working Paper
Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility
This paper develops a method for producing current-quarter forecasts of GDP growth with a (possibly large) range of available within-the-quarter monthly observations of economic indicators, such as employment and industrial production, and financial indicators, such as stock prices and interest rates. In light of existing evidence of time variation in the variances of shocks to GDP, we consider versions of the model with both constant variances and stochastic volatility. We also evaluate models with either constant or time-varying regression coefficients. We use Bayesian methods to estimate ...
Report
Estimating the cross-sectional distribution of price stickiness from aggregate data
We estimate a multisector sticky-price model for the U.S. economy in which the degree of price stickiness is allowed to vary across sectors. For this purpose, we use a specification that allows us to extract information about the underlying cross-sectional distribution from aggregate data. Identification is possible because sectors play different roles in determining the response of aggregate variables to shocks at different frequencies: Sectors where prices are stickier are relatively more important in determining the low-frequency response. Estimating the model using only aggregate data on ...