Search Results

Showing results 1 to 10 of approximately 94.

(refine search)
Jel Classification:C11 

Online Estimation of DSGE Models

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

DSGE forecasts of the lost recovery

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

Economic predictions with big data: the illusion of sparsity

We compare sparse and dense representations of predictive models in macroeconomics, microeconomics, and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse or dense model, but on a wide set of models. A clearer pattern of sparsity can only emerge when models of very low dimension are strongly favored a priori.
Staff Reports , Paper 847

Priors for the long run

We propose a class of prior distributions that discipline the long-run predictions of vector autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run are conjugate, and can thus be easily implemented using dummy observations and combined with other popular priors. In VARs with standard macroeconomic variables, a prior based on the long-run predictions of a wide class of theoretical models yields substantial improvements in the forecasting ...
Staff Reports , Paper 832

Real-time inflation forecasting in a changing world

This paper revisits the accuracy of inflation forecasting using activity and expectations variables. We apply Bayesian-model averaging across different regression specifications selected from a set of potential predictors that includes lagged values of inflation, a host of real activity data, term structure data, nominal data, and surveys. In this model average, we can entertain different channels of structural instability by incorporating stochastic breaks in the regression parameters of each individual specification within this average, allowing for breaks in the error variance of the ...
Staff Reports , Paper 388

Reexamining the consumption-wealth relationship: the role of model uncertainty

In their influential work on the consumption-wealth relationship, Lettau and Ludvigson found that while consumption responds to permanent changes in wealth in the expected manner, most changes in wealth are transitory with no effect on consumption. We investigate the robustness of these results to model uncertainty using Bayesian model averaging. We find that there is model uncertainty with regard to the number of cointegrating vectors, the form of deterministic components, lag length, and whether the cointegrating residuals affect consumption and income directly. Whether this uncertainty has ...
Staff Reports , Paper 202

Forecasting in large macroeconomic panels using Bayesian Model Averaging

This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms that simulate from the space defined by all possible models. We explain how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time ...
Staff Reports , Paper 163

Uncertainty about Trade Policy Uncertainty

We revisit in this note the macroeconomic impact of the recent rise in trade policy uncertainty. As in the literature, we do find that high trade policy uncertainty can adversely impact domestic and foreign economic activity. In addition, we identify an alternative business sentiment channel that is separate and distinct from the impact of trade policy uncertainty, which provides a complementary explanation of the recent developments in the U.S. and global economic activities. This sentiment channel also implies that subsiding trade policy uncertainty does not necessarily result in a recovery ...
Staff Reports , Paper 919

Working Paper
Clustered housing cycles

Using a panel of U.S. city-level building permits data, we estimate a Markov-switching model of housing cycles that allows cities to systematically deviate from the national housing cycle. These deviations 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 population growth or availability of credit) appear to be more important determinants of cyclical co-movements than similarities in factors affecting the ...
Working Papers , Paper 2013-021

Working Paper
Business Cycles Across Space and Time

We study the comovement of international business cycles in a time series clustering model with regime-switching. We extend the framework of Hamilton and Owyang (2012) to include time-varying transition probabilities to determine what drives similarities in business cycle turning points. We find four groups, or ?clusters?, of countries which experience idiosyncratic recessions relative to the global cycle. Additionally, we find the primary indicators of international recessions to be fluctuations in equity markets and geopolitical uncertainty. In out-of-sample forecasting exercises, we find ...
Working Papers , Paper 2019-10


FILTER BY Content Type


Clark, Todd E. 8 items

Herbst, Edward 8 items

Carriero, Andrea 6 items

Martínez-García, Enrique 6 items

Del Negro, Marco 5 items

Jensen, Mark J. 5 items

show more (127)

FILTER BY Jel Classification

C32 33 items

E32 22 items

C53 16 items

E52 16 items

C13 13 items

show more (71)

FILTER BY Keywords

Bayesian estimation 13 items

stochastic volatility 10 items

uncertainty 9 items

Forecasting 8 items

Bayesian Analysis 7 items

Bayesian methods 7 items

show more (300)