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DSGE forecasts of the lost recovery
Cai, Michael; Giannoni, Marc; Del Negro, Marco; Gupta, Abhi; Moszkowski, Erica; Li, Pearl (2018-03-01)
The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the real real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using ...
Staff Reports , Paper 844

Working Paper
Constrained Discretion and Central Bank Transparency
Melosi, Leonardo; Bianchi, Francesco (2016-10-16)
We develop and estimate a general equilibrium model to quantitatively assess the effects and welfare implications of central bank transparency. Monetary policy can deviate from active inflation stabilization and agents conduct Bayesian learning about the nature of these deviations. Under constrained discretion, only short deviations occur, agents? uncertainty about the macroeconomy remains contained, and welfare is high. However, if a deviation persists, uncertainty accelerates and welfare declines. Announcing the future policy course raises uncertainty in the short run by revealing that ...
Working Paper Series , Paper WP-2016-15

Report
Online Estimation of DSGE Models
Herbst, Edward; Schorfheide, Frank; Cai, Michael; Matlin, Ethan; Del Negro, Marco; Sarfati, Reca (2019-08-01)
This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for ?online? estimation, and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts of DSGE models with and without financial frictions and document the benefits of conditioning DSGE model forecasts on nowcasts ...
Staff Reports , Paper 893

Working Paper
High-Dimensional DSGE Models: Pointers on Prior, Estimation, Comparison, and Prediction∗
Shin, Minchul; Tan, Fei; Chib, Siddhartha (2020-09-15)
Working Papers , Paper 20-35

Working Paper
Clustered Housing Cycles
Rubio, Margarita; Owyang, Michael T.; Hernandez-Murillo, Ruben (2015-10-22)
Using a panel of U.S. city-level building permits data, we estimate a Markov-switching model of housing cycles that allows for idiosyncratic departures from a national housing cycle. These departures occur for clusters of cities that experience simultaneous housing contractions. We find that cities do not form housing regions in the traditional geographic sense. Instead, similarities in factors affecting the demand for housing (such as average winter temperature and the unemployment rate) appear to be more important determinants of cyclical comovements than similarities in factors affecting ...
Working Papers (Old Series) , Paper 1524

Working Paper
Measuring Uncertainty and Its Impact on the Economy
Clark, Todd E.; Carriero, Andrea; Massimiliano, Marcellino (2016-10-14)
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 Papers (Old Series) , Paper 1622

Working Paper
Bayesian estimation of NOEM models: identification and inference in small samples
Vilan, Diego; Wynne, Mark A.; Martinez-Garcia, Enrique (2012)
This paper studies the (potential) weak identification of these relationships in the context of a fully specified structural model using Bayesian estimation techniques. We trace the problems to sample size, rather than misspecification bias. We conclude that standard macroeconomic time series with a coverage of less than forty years are subject to potentially serious identification issues, and also to model selection errors. We recommend estimation with simulated data prior to bringing the model to the actual data as a way of detecting parameters that are susceptible to weak identification in ...
Globalization Institute Working Papers , Paper 105

Working Paper
Bayesian Inference and Prediction of a Multiple-Change-Point Panel Model with Nonparametric Priors
Fisher, Mark; Jensen, Mark J. (2018-02-01)
Change point models using hierarchical priors share in the information of each regime when estimating the parameter values of a regime. Because of this sharing, hierarchical priors have been very successful when estimating the parameter values of short-lived regimes and predicting the out-of-sample behavior of the regime parameters. However, the hierarchical priors have been parametric. Their parametric nature leads to global shrinkage that biases the estimates of the parameter coefficient of extraordinary regimes toward the value of the average regime. To overcome this shrinkage, we model ...
FRB Atlanta Working Paper , Paper 2018-2

Working Paper
Measuring International Uncertainty : The Case of Korea
Zhong, Molin; Shin, Minchul; Zhang, Boyuan; Lee, Dong Jin (2017-06-20)
We leverage a data rich environment to construct and study a measure of macroeconomic uncertainty for the Korean economy. We provide several stylized facts about uncertainty in Korea from 1991M10-2016M5. We compare and contrast this measure of uncertainty with two other popular uncertainty proxies, financial and policy uncertainty proxies, as well as the U.S. measure constructed by Jurado et. al. (2015).
Finance and Economics Discussion Series , Paper 2017-066

Working Paper
Capital-Task Complementarity and the Decline of the U.S. Labor Share of Income
Orak, Musa (2017-03)
This paper provides evidence that shifts in the occupational composition of the U.S. workforce are the most important factor explaining the trend decline in the labor share over the past four decades. Estimates suggest that while there is unitary elasticity between equipment capital and non-routine tasks, equipment capital and routine tasks are highly substitutable. Through the lenses of a general equilibrium model with occupational choice and the estimated production technology, I document that the fall in relative price of equipment capital alone can explain 72 percent of the observed ...
International Finance Discussion Papers , Paper 1200

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