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by Marco Del Negro, Bianca De Paoli, Stefano Eusepi, Marc Giannoni, Argia Sbordone, and Andrea Tambalotti – Liberty Street Economics, Federal Reserve Bank of New York
This is the first post in a five-part series which examines the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (FRBNY DSGE) model – a structural model used by Bank researchers to understand the workings of the U.S. economy and provide economic forecasts.The Federal Reserve Bank of New York (FRBNY) has built a DSGE model as part of its efforts to forecast the U.S. economy. On Liberty Street Economics, we are publishing a weeklong series to provide some background on the model and its use for policy analysis and forecasting, as well as its forecasting performance. In this post, we briefly discuss what DSGE models are, explain their usefulness as a forecasting tool, and preview the forthcoming pieces in this series.
The term DSGE, which stands for dynamic stochastic general equilibrium, encompasses a very broad class of macro models, from the standard real business cycle (RBC) model of Nobel prizewinners Kydland and Prescott to New Keynesian monetary models like the one of Christiano, Eichenbaum, and Evans. What distinguishes these models is that rules describing how economic agents behave are obtained by solving intertemporal optimization problems, given assumptions about the underlying environment, including the prevailing fiscal and monetary policy regime. One of the benefits of DSGE models is that they can deliver a lens for understanding the economy’s behavior. The third post in this series will show an example of this role with a discussion of the forces behind the Great Recession and the following slow recovery.
DSGE models are also quite abstract representations of reality, however, which in the past severely limited their empirical appeal and forecasting performance. This started to change with work by Schorfheideand Smets and Wouters. First, they popularized estimation (especiallyBayesian estimation) of these models, with parameters chosen in a way that increased the ability of these models to describe the time series behavior of economic variables. Second, these models were enriched with both endogenous and exogenous propagation mechanisms that allowed them to better capture patterns in the data. For this reason, estimated DSGE models are increasingly used within the Federal Reserve System (the Board of Governors and the Reserve Banks of Chicago andPhiladelphia have versions) and by central banks around the world (including the New Area-Wide Model developed at the European Central Bank, and models at the Norges Bank and the Sveriges Riksbank). The FRBNY DSGE model is a medium-scale model in the tradition ofChristiano, Eichenbaum, and Evans and Smets and Wouters that also includes credit frictions as in the financial accelerator model developed byBernanke, Gertler, and Gilchrist and further investigated by Christiano, Motto, and Rostagno. The second post in this series elaborates on what DSGE models are and discusses the features of the FRBNY model.
Perhaps some progress was made in the past twenty years toward empirical fit, but is it enough to give forecasts from DSGE models any credence? Aren’t there many critics out there (here is one) telling us these models are a failure? As it happens, not many people seem to have actually checked the extent to which these model forecasts are off the mark. Del Negro and Schorfheide do undertake such an exercise in a chapter of the recent Handbook of Economic Forecasting. Their analysis compares the real-time forecast accuracy of DSGE models that were available prior to the Great Recession (such as the Smets and Wouters model) to that of the Blue Chip consensus forecasts, using a period that includes the Great Recession. They find that, for nowcasting (forecasting current quarter variables) and short-run forecasting, DSGE models are at a disadvantage compared with professional forecasts. Over the medium- and long-run terms, however, DSGE model forecasts for both output and inflation become competitive with—if not superior to—professional forecasts. They also find that including timely information from financial markets such as credit spreads can dramatically improve the models’ forecasts, especially in the Great Recession period.
These results are based on what forecasters call “pseudo-out-of-sample” forecasts. These are not truly “real time” forecasts, because they were not produced at the time. (To our knowledge, there is little record of truly real time DSGE forecasts for the United States, partly because these models were only developed in the mid-2000s.) For this reason, in the fourth post of this series, we report forecasts produced in real time using the FRBNY DSGE model since 2010. These forecasts have been included in internal New York Fed documents, but were not previously made public. Although the sample is admittedly short, these forecasts show that while consensus forecasts were predicting a relatively rapid recovery from the Great Recession, the DSGE model was correctly forecasting a more sluggish recovery.
The last post in the series shows the current FRBNY DSGE forecasts for output growth and inflation and discusses the main economic forces driving the predictions. Bear in mind that these forecasts are not the official New York Fed staff forecasts; the DSGE model is only one of many tools employed for prediction and policy analysis at the Bank.
DSGE models in general and the FRBNY model in particular have huge margins for improvement. The list of flaws is long, ranging from the lack of heterogeneity (the models assume a representative household) to the crude representation of financial markets (the models have no term premia). Nevertheless, we are sticking our necks out and showing our forecasts, not because we think we have a “good” model of the economy, but because we want to have a public record of the model’s successes and failures. In doing so, we can learn from both our past performance and readers’ criticism. The model is a work in progress. Hopefully, it can be improved over time, guided by current economic and policy questions and benefiting from developments in economic theory and econometric tools.
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.
About the Authors
Marco Del Negro is an assistant vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.
Bianca De Paoli is a senior economist in the Bank’s Research and Statistics Group.
Stefano Eusepi is an officer in the Group.
Marc Giannoni is an assistant vice president in the Group.
Argia Sbordone is a vice president in the Group.
Andrea Tambalotti is an officer in the Group.
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