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Jel Classification:C53 

Exploiting the monthly data flow in structural forecasting

This paper develops a framework that allows us to combine the tools provided by structural models for economic interpretation and policy analysis with those of reduced-form models designed for nowcasting. We show how to map a quarterly dynamic stochastic general equilibrium (DSGE) model into a higher frequency (monthly) version that maintains the same economic restrictions. Moreover, we show how to augment the monthly DSGE with auxiliary data that can enhance the analysis and the predictive accuracy in now-casting and forecasting. Our empirical results show that both the monthly version of ...
Staff Reports , Paper 751

Revisiting useful approaches to data-rich macroeconomic forecasting

This paper analyzes the properties of a number of data-rich methods that are widely used in macroeconomic forecasting, in particular principal components (PC) and Bayesian regressions, as well as a lesser-known alternative, partial least squares (PLS) regression. In the latter method, linear, orthogonal combinations of a large number of predictor variables are constructed such that the covariance between a target variable and these common components is maximized. Existing studies have focused on modelling the target variable as a function of a finite set of unobserved common factors that ...
Staff Reports , Paper 327

Dynamic prediction pools: an investigation of financial frictions and forecasting performance

We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call dynamic pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, for output growth and inflation in the period 1992 to 2011. We find strong evidence of time variation in the pool?s weights, reflecting the fact that the DSGE model with financial frictions produces superior forecasts in periods of financial distress but doesn?t perform as well in tranquil periods. The dynamic ...
Staff Reports , Paper 695

Recent changes in the U.S. business cycle

The U.S. business cycle expansion that started in March 1991 is the longest on record. This paper uses statistical techniques to examine whether this expansion is a onetime unique event or whether its length is a result of a change in the stability of the U.S. economy. Bayesian methods are used to estimate a common factor model that allows for structural breaks in the dynamics of a wide range of macroeconomic variables. We find strong evidence that a reduction in volatility is common to the series examined. Further, the reduction in volatility implies that future expansions will be ...
Staff Reports , Paper 126

Heterogeneous inflation expectations and learning

Using the panel component of the Michigan Survey of Consumers, we estimate a learning model of inflation expectations, allowing for heterogeneous use of both private information and lifetime inflation experience. ?Life-experience inflation? has a significant impact on individual expectations, but only for one-year-ahead inflation. Public information is substantially more relevant for longer-horizon expectations. Even controlling for life-experience inflation and public information, idiosyncratic information explains a nontrivial proportion of the inflation forecasts of agents. We find that ...
Staff Reports , Paper 536

What predicts U.S. recessions?

We reassess the predictability of U.S. recessions at horizons from three months to two years ahead for a large number of previously proposed leading-indicator variables. We employ an efficient probit estimator for partially missing data and assess relative model performance based on the receiver operating characteristic (ROC) curve. While the Treasury term spread has the highest predictive power at horizons four to six quarters ahead, adding lagged observations of the term spread significantly improves the predictability of recessions at shorter horizons. Moreover, balances in broker-dealer ...
Staff Reports , Paper 691

The FRBNY DSGE model

The goal of this paper is to present the dynamic stochastic general equilibrium (DSGE) model developed and used at the Federal Reserve Bank of New York. The paper describes how the model works, how it is estimated, how it rationalizes past history, including the Great Recession, and how it is used for forecasting and policy analysis.
Staff Reports , Paper 647

Discussion Paper
Monitoring Real Activity in Real Time: The Weekly Economic Index

Economists are well-practiced at assessing real activity based on familiar aggregate time series, like the unemployment rate, industrial production, or GDP growth. However, these series represent monthly or quarterly averages of economic conditions, and are only available at a considerable lag, after the month or quarter ends. When the economy hits sudden headwinds, like the COVID-19 pandemic, conditions can evolve rapidly. How can we monitor the high-frequency evolution of the economy in “real time”?
Liberty Street Economics , Paper 20200330b

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

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


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McCracken, Michael W. 13 items

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