ECONOMETRIC MODELS AND ECONOMIC FORECASTS12

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This paper compares the forecasts of an estimated dynamic stochastic general equilibrium (DSGE) model with that of the Federal Reserve staff and reduced-form time-series models. The paper has three goals. First, much of the related literature has compared forecasts from DSGE models with simplereduced-form forecasting techniques: Our comparison with Federal Reserve staff forecasts provides a potentially more stringent test, given that previous research has shown the Federal Reserve staff forecast to be of high-quality relative to alternative methods.1 In addition, some of the research regarding DSGE models has found strong support for DSGE specifications using Bayesian measures of fit (such as posterior odds or marginal likelihoods);however, these measures can be dependent on the analyst's prior views and, as emphasized by Sims [2003], often appear too decisive. Given this concern, we focus on out-of-sample forecast performance.2 Finally, we examine forecast performance for both top-line macroeconomic variables--that is, the state of the labor market, growth of Gross Domestic Product, inflation, and the federal funds rate--and for detailed subcategoriesof aggregate expenditure--that is, consumption of nondurables and services and investment in consumer durables, residential capital, and business capital. This detailed focus is not common in DSGE models, which typically lump several of these categories into one broad category; however,policymakers have expressed interest in such details (see, for example, Kohn [2003]), and large macroeconometric models like the Federal Reserve's FRB/US model often produce forecasts at similar, or even more disaggregated levels.

Our DSGE model is the result of the Federal Reserve Board's project on Estimated, Dynamic, Optimization-based models; that is, the Edo model. This model contains a rich description of production, expenditure, labor supply, and pricing decisions for the economy of the United States. We havepresented detailed descriptions of the model's structure, our estimation strategy, and results in previous papers (see Edge et al. [2008] and Edge et al. [2007a]) and so we present only a brief summary of the model's structure in section 2. Fornow, we simply highlight that the model has been designed to address a broad range of policy questions, as emphasized in Edge et al. [2008]. For example, Gali and Gertler [2007] discuss two important contributions of DSGE models to monetarypolicy analysis: microeconomic foundations for economic dynamics merged with rational expectations for economic agents, and the role of fluctuations in natural rates of output and interest in policy determination. The Edo model has been used to analyze these issues, especially the latter, inEdge et al. [2008]. We have also investigated the fluctuations in the U.S. housing market, which have been considerable over the past decade, using the Edo model (see Edge et al. [2007b]). Importantly, we use the same model in this other research asin the forecasting analysis herein. While many academic investigations will consider specific models that are designed to address individual questions, the large number and broad range of questions that arise under significant time pressures within a policy institution require that the core modelsused for policy work be capable of spanning multiple questions. Indeed, Meyer [1997] emphasizes the multiple roles of macroeconomic models in policymaking and private-sector consulting work, of which forecasting is but one example.

Our period of analysis spans macroeconomic developments in the United States from mid-1996 to late-2004 (where the end-point is determined by the public availability of data for forecast evaluation at the time of this study). This period was chosen for two reasons. First, the Federal Reserve'sFRB/US model--a macroeconometric model specified with a neoclassical steady state and dynamic behavior designed to address the Lucas critique through consideration of the influence of expectations and other sources of dynamics--entered operation in mid-1996. As we aim to compare a cutting-edge DSGEmodel with existing practices at the Federal Reserve (and, to some extent, at other central banks), we focus on the period over which current practices have been employed. Second, the structure of our DSGE model--which, as discussed below, has two production sectors that experience "slow" and"fast" productivity growth--requires detailed data for estimation, and we have available the relevant "real-time" data since about mid-1996.

Before turning to our analysis, we would like to highlight several pieces of related research. Smets and Wouters [2007] demonstrated that a richly-specified DSGE model could fit the U.S. macroeconomic data well and provide out-of-sample forecasts that arecompetitive or superior to reduced-form vector-autoregressions. We build on their work in several ways. First, our model contains a more detailed description of sectoral production and household/business expenditure decisions--which, as noted earlier, appears to be a prerequisite for apolicy-relevant model. Second, we measure all economic variables in a manner more consistent with the official statistics published by the U.S. Bureau of Economic Analysis (the statistics that form the basis of policy deliberations and public discussion of economic fluctuations), whereas incontrast, Smets and Wouters [2007] make adjustments to published figures on consumption and investment in order to match the relative price restrictions implied by their one-sector model. Finally, and most importantly, we examine out-of-sample forecast performanceusing real-time data and compare our DSGE model's forecast performance with Federal Reserve staff forecasts and models, thereby pushing further on the question of whether DSGE models can give policy-relevant forecast information.

Other relevant research includes Lees et al. [2007], who compare the forecast performance of the Reserve Bank of New Zealand's official forecasts with those from a vector-autoregressive model informed by priors from a DSGE model as suggested in Del Negro and Schorfheide [2004]. Our analysis shares the idea of comparing forecasts to staff forecasts at a central bank; such a comparison seems especially likely to illuminate the relevance of such techniques for policy work. However, we focus on forecasts from a DSGEmodel rather than those informed by a DSGE prior. The latter approach is something of a "black-box", as the connection of the DSGE structure to the resulting forecast is tenuous (and asymptotically completely absent, as the data dominate the prior). Moreover, our reliance on a DSGE model directlyallows us to make economically interesting inferences regarding the aspects of the model that contribute to its successes and failures. Finally, Lees et al. [2007] examine a very small set of variables--specifically, output, inflation, and the policy interestrate. Our experience with larger models like FRB/US at the Federal Reserve suggests that such small systems are simply not up to the challenge of addressing the types of questions demanded of models at large central banks (as we discuss in Edge et al. [2008]).

Adolfson et al. [2006] and Christoffel et al. [2007] examine out-of-sample forecast performance for DSGE models of the Euro area. Their investigations are very similar to ours in directly considering a fairly large DSGE model.However, the focus of each of these pieces of research is on technical aspects of model evaluation. We eschew this approach and instead attempt to identify the economic sources of the successes and failures of our model. Also, neither of these studies uses real-time data, nor do they compareforecast performance to an alternative model employed at a central bank or official staff forecasts. As discussed, we focus on real-time data and compare forecast performance to the FRB/US model and Federal Reserve Greenbook forecasts. Overall, we view both Adolfsonet al. [2006] and Christoffel et al. [2007] as complementary to our analysis, but feel that the explicit comparison to "real-world" central bank practices is especially valuable.

The paper is organized as follows. Section 2 provides an overview of the Edo model. Section 3 discusses the estimation and evaluation of both the Edo model as well as the alternative forecasting models used in the paper's analysis. Section 4 introduces the alternative forecaststhat the paper considers: We focus on our DSGE model (Edo) forecasts, the Federal Reserve Board's staff projections, including those from the FRB/US model, and the forecasts from autoregressions and vector autoregessions. We also discuss our real-time data in the fourth section. Section 5presents the comparison between Edo and time-series models. Section 6 examines the Federal Reserve forecasts and subsample results that illustrate important economic successes and failures of our model. We discuss amendments to our DSGE model that address some of these failures and henceprovide an example of the type of lesson for structural modelers that can be gleaned from forecast exercises. Section 7 concludes and points to directions for future research.

Research on policy applications of dynamic, stochastic, general-equilibrium (DSGE) models has exploded in the last five years. On the policy front, the GEM project at the International Monetary Fund (see Bayoumi et al. [2004]) and the SIGMA project at the Federal Reserve(see Erceg et al. [2006]) have provided examples of richly-specified models with firm microeconomic foundations that can be applied to policy questions. However, even these rich models have not had the detail on domestic economic developments, such as specifications ofhighly disaggregated expenditure decisions, to address the range of questions typically analyzed by large models like the Federal Reserve's FRB/US model.3 TheEstimated, Dynamic, Optimization-based (Edo) model project at the Federal Reserve has been designed to build on earlier work at policy institutions, as well as academic research such as Smets and Wouters [2007] and Altig et al. [2004], byexpanding the modeling of domestic economic decisions while investigating the ability of such DSGE models to examine a range of policy questions. For a detailed description and discussion of previous applications, the reader is referred to Edge et al. [2008], Edge et al. [2007a], and Edge et al. [2007b]. 2b1af7f3a8