Search Results
Working Paper
A Unified Framework to Estimate Macroeconomic Stars
This paper develops a semi-structural model to jointly estimate “stars” — long-run levels of output (its growth rate), the unemployment rate, the real interest rate, productivity growth, price inflation, and wage inflation. It features links between survey expectations and stars, time-variation in macroeconomic relationships, and stochastic volatility. Survey data help discipline stars’ estimates and have been crucial in estimating a high-dimensional model since the pandemic. The model has desirable real-time properties, competitive forecasting performance, and superior fit to the ...
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Fitting observed inflation expectations
This paper provides evidence on the extent to which inflation expectations generated by a standard Christiano et al. (2005)/Smets and Wouters (2003)?type DSGE model are in line with what is observed in the data. We consider three variants of this model that differ in terms of the behavior of, and the public?s information on, the central banks? inflation target, allegedly a key determinant of inflation expectations. We find that: 1) time-variation in the inflation target is needed to capture the evolution of expectations during the post-Volcker period; 2) the variant where agents have ...
Working Paper
A Unified Framework to Estimate Macroeconomic Stars
We develop a flexible semi-structural time-series model to estimate jointly several macroeconomic "stars" -- i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, price inflation, and wage inflation. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on inflation and interest rate modeling, we explicitly model the links between long-run survey expectations ...
Working Paper
Analyzing data revisions with a dynamic stochastic general equilibrium model
We use a structural dynamic stochastic general equilibrium model to investigate how initial data releases of key macroeconomic aggregates are related to final revised versions and how identified aggregate shocks influence data revisions. The analysis sheds light on how well preliminary data approximate final data and on how policy makers might condition their view of the preliminary data when formulating policy actions. The results suggest that monetary policy shocks and multifactor productivity shocks lead to predictable revisions to the initial release data on output growth and inflation.
Working Paper
A Unified Framework to Estimate Macroeconomic Stars
We develop a flexible semi-structural time-series model to estimate jointly several macroeconomic "stars" — i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, the price inflation, and wage inflation. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on inflation and interest rate modeling, we explicitly model the links between long-run survey ...
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Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)
This paper discusses prior elicitation for the parameters of dynamic stochastic general equilibrium (DSGE) models and provides a method for constructing prior distributions for a subset of these parameters from beliefs about the moments of the endogenous variables. The empirical application studies the role of price and wage rigidities in a New Keynesian DSGE model and finds that standard macro time series cannot discriminate among theories that differ in the quantitative importance of nominal frictions.
Working Paper
CardSim: A Bayesian Simulator for Payment Card Fraud Detection Research
Payment fraud has been high in recent years, and as criminals gain access to capability-enhancing generative AI tools, there is a growing need for innovative fraud detection research. However, the pace, diversity, and reproducibility of such research are inhibited by the dearth of publicly available payment transaction data. A few payment simulation methodologies have been developed to help narrow the payment transaction data gap without compromising important data privacy and security expectations. While these simulation approaches have enabled research advancements, more work is needed to ...
Working Paper
Scenario-based Quantile Connectedness of the U.S. Interbank Liquidity Risk Network
We characterize the U.S. interbank liquidity risk network based on a supervisory dataset, using a scenario-based quantile network connectedness approach. In terms of methodology, we consider a quantile vector autoregressive model with unobserved heterogeneity and propose a Bayesian nuclear norm estimation method. A common factor structure is employed to deal with unobserved heterogeneity that may exhibit endogeneity within the network. Then we develop a scenario-based quantile network connectedness framework by accommodating various economic scenarios, through a scenario-based moving average ...
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Rare shocks, great recessions
We estimate a DSGE model where rare large shocks can occur, by replacing the commonly used Gaussian assumption with a Student?s t distribution. Results from the Smets and Wouters (2007) model estimated on the usual set of macroeconomic time series over the 1964-2011 period indicate that 1) the Student?s t specification is strongly favored by the data even when we allow for low-frequency variation in the volatility of the shocks and 2) the estimated degrees of freedom are quite low for several shocks that drive U.S. business cycles, implying an important role for rare large shocks. This result ...
Working Paper
Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy
This paper constructs hybrid forecasts that combine both short- and long-term conditioning information from external surveys with forecasts from a standard fixed-coefficient vector autoregression (VAR) model. Specifically, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of ...