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Jel Classification:C11 

A Bayesian Approach to Inference on Probabilistic Surveys

We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation are consistent with the noisy rational expectations hypothesis. We find that in contrast to theory, for horizons close to two years, there is no relationship whatsoever between subjective uncertainty and forecast accuracy for output growth density projections, both across forecasters and over time, and only a mild relationship for inflation projections. As the horizon ...
Staff Reports , Paper 1025

Working Paper
Nowcasting Business Cycles: a Bayesian Approach to Dynamic Heterogeneous Factor Models

We develop a framework for measuring and monitoring business cycles in real time. Following a long tradition in macroeconometrics, inference is based on a variety of indicators of economic activity, treated as imperfect measures of an underlying index of business cycle conditions. We extend existing approaches by permitting for heterogenous lead-lag patterns of the various indicators along the business cycles. The framework is well suited for high-frequency monitoring of current economic conditions in real time - nowcasting - since inference can be conducted in presence of mixed frequency ...
Finance and Economics Discussion Series , Paper 2015-66

Working Paper
A New Way to Quantify the Effect of Uncertainty

This paper develops a new way to quantify the effect of uncertainty and other higher-order moments. First, we estimate a nonlinear model using Bayesian methods with data on uncertainty, in addition to common macro time series. This key step allows us to decompose the exogenous and endogenous sources of uncertainty, calculate the effect of volatility following the cost of business cycles literature, and generate data-driven policy functions for any higherorder moment. Second, we use the Euler equation to analytically decompose consumption into several terms--expected consumption, the ex-ante ...
Working Papers , Paper 1705

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

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
Significance Bands for Local Projections

An impulse response function describes the dynamic evolution of an outcome variable following a stimulus or treatment. A common hypothesis of interest is whether the treatment affects the outcome. We show that this hypothesis is best assessed using significance bands rather than relying on commonly displayed confidence bands. Under the null hypothesis, we show that significance bands are trivial to construct with standard statistical software using the LM principle, and should be reported as a matter of routine when displaying impulse responses graphically.
Working Paper Series , Paper 2023-15

Working Paper
Fitting a distribution to survey data for the half-life of deviations from PPP

This note presents a nonparametric Bayesian approach to fitting a distribution to the survey data provided in Kilian and Zha (2002) regarding the prior for the half-life of deviations from purchasing power parity (PPP). A point mass at infinity is included. The unknown density is represented as an average of shape-restricted Bernstein polynomials, each of which has been skewed according to a preliminary parametric fit. A sparsity prior is adopted for regularization.
FRB Atlanta Working Paper , Paper 2015-15

Working Paper
Monetary Policy and Macroeconomic Stability Revisited

A large literature has established that the Fed? change from a passive to an active policy response to inflation led to US macroeconomic stability after the Great Inflation of the 1970s. This paper revisits the literature?s view by estimating a generalized New Keynesian model using a full-information Bayesian method that allows for equilibrium indeterminacy and adopts a sequential Monte Carlo algorithm. The model empirically outperforms canonical New Keynesian models that confirm the literature?s view. Our estimated model shows an active policy response to inflation even during the Great ...
Working Papers , Paper 19-14

Working Paper
A Novel MIMIC-Style Model of European Bank Technical Efficiency and Productivity Growth

Using Bayesian Monte Carlo methods, we augment a stochastic distance function measure of bank efficiency and productivity growth with indicators of capitalization, return and risk. Our novel Multiple Indicator-Multiple Cause (MIMIC) style model generates more precise estimates of policy relevant parameters such as returns to scale, technical inefficiency and productivity growth. We find considerable variation in the performance of EU-15 banks over the period 2008 to 2015. For the vast majority of banks, productivity growth – the sum of efficiency and technical changes – is negative, ...
Working Papers , Paper 2012

A 14-Variable Mixed-Frequency VAR Model

This paper describes recent modifications to the mixed-frequency model vector autoregression (MF-VAR) constructed by Schorfheide and Song (2012). The changes to the model are restricted solely to the set of variables included in the model; all other aspects of the model remain unchanged. Forecast evaluations are conducted to gauge the accuracy of the revised model to standard benchmarks and the original model.
Staff Report , Paper 493


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