The New York Fed Staff Underlying Inflation Gauge (UIG)
A measure of underlying inflation that uses all relevant information, is available in real time, and forecasts inflation better than traditional underlying inflation measures?such as core inflation measures?would greatly benefit monetary policymakers, market participants, and the public. This article presents the New York Fed Staff Underlying Inflation Gauge (UIG) for the consumer price index and the personal consumption expenditures deflator. Using a dynamic factor model approach, the UIG is derived from a broad data set that extends beyond price series to include a wide range of nominal, ...
How Easy Is It to Forecast Commodity Prices?
Over the last decade, unprecedented spikes and drops in commodity prices have been a recurrent source of concern to both policymakers and the general public. Given all the recent attention, have economists and analysts made any progress in their ability to predict movements in commodity prices? In this post, we find there is no easy answer. We consider different strategies to forecast near-term commodity price inflation, but find that no particular approach is systematically more accurate and robust. Additionally, the results warn against interpreting current forecasts of commodity prices ...
Labor Force Exits Are Complicating Unemployment Rate Forecasts
What will the unemployment rate be in 2013? Even if you were certain how much the U.S. economy (gross domestic product, or GDP) would grow over the next year or two, it would still be difficult to forecast the unemployment rate over that period. The link between GDP growth and unemployment is complex in part because it depends on how many people decide to work or look for work?that is, the labor force participation rate. In this post, we discuss the recent steep decline in the labor force participation rate and explain how uncertainty regarding the future path of that variable contributes to ...
Forecasting the Great Recession: DSGE vs. Blue Chip
Dynamic stochastic general equilibrium (DSGE) models have been trashed, bashed, and abused during the Great Recession and after. One of the many reasons for the bashing was the models? alleged inability to forecast the recession itself. Oddly enough, there?s little evidence on the forecasting performance of DSGE models during this turbulent period. In the paper ?DSGE Model-Based Forecasting,? prepared for Elsevier?s Handbook of Economic Forecasting, two of us (Del Negro and Schorfheide), with the help of the third (Herbst), provide some of this evidence. This post shares some of our results.
The FRBNY DSGE Model Forecast
The U.S. economy has been in a gradual but slow recovery. Will the future be more of the same? This post presents the current forecasts from the Federal Reserve Bank of New York?s (FRBNY) DSGE model, described in our earlier ?Bird?s Eye View? post, and discusses the driving forces behind the forecasts. Find the code used for estimating the model and producing all the charts in this blog series here. (We should reiterate that these are not the official New York Fed staff forecasts, but only an input to the overall forecasting process at the Bank.)
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 ...
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 ...
Housing demand and community choice: an empirical analysis
Housing demand reflects the household's simultaneous choice of neighborhood, whether to own or rent the dwelling, and the quantity of housing services demanded. Existing literature emphasizes the final two factors, but overlooks the choice of community. This paper develops an econometric model that incorporates all three components, and then estimates this model using a sample of households in Tampa, Florida. Incorporating community choice increases the price elasticity of demand and reduces the differential between white and comparable nonwhite households. The results are robust to the ...
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 ...
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 ...