Matching Theory and Data: Bayesian Vector Autoregression and Dynamic Stochastic General Equilibrium Models

This paper shows how to identify the structural shocks of a Vector Autore- gression (VAR) while at the same time estimating a dynamic stochastic general equilibrium (DSGE) model that is not assumed to replicate the data generating process. It proposes a framework to estimate the parameters of the VAR model and the DSGE model jointly: the VAR model is identified by sign restrictions derived from the DSGE model; the DSGE model is estimated by matching the corresponding impulse response functions.Bayesian Model Estimation, Vector Autoregression, Identification.

Confronting Model Misspecification in Macroeconomics

We estimate a Markov-switching mixture of two familiar macroeconomic models: a richly parameterized DSGE model and a corresponding BVAR model. We show that the Markov-switching mixture model dominates both individual models and improves the fit considerably. Our estimation indicates that the DSGE model plays an important role only in the late 1970s and the early 1980s. We show how to use the mixture model as a data filter for estimation of the DSGE model when the BVAR model is not identified. Moreover, we show how to compute the impulse responses to the same type of shock shared by the DSGE and BVAR models when the shock is identified in the BVAR model. Our exercises demonstrate the importance of integrating model uncertainty and parameter uncertainty to address potential model misspecification in macroeconomics.

Putting the New Keynesian DSGE model to the real-time forecasting test

Dynamic stochastic general equilibrium models have recently become standard tools for policy-oriented analyses. Nevertheless, their forecasting properties are still barely explored. We fill this gap by comparing the quality of real-time forecasts from a richly-specified DSGE model to those from the Survey of Professional Forecasters, Bayesian VARs and VARs using priors from a DSGE model. We show that the analyzed DSGE model is relatively successful in forecasting the US economy in the period of 1994-2008. Except for short-term forecasts of inflation and interest rates, it is as good as or clearly outperforms BVARs and DSGE-VARs. Compared to the SPF, the DSGE model generates better output forecasts at longer horizons, but less accurate short-term forecasts for interest rates. Conditional on experts' now casts, however, the forecasting power of the DSGE turns out to be similar or better than that of the SPF for all the variables and horizons. JEL Classification: C11, C32, C53, D58, E17Bayesian VAR, DSGE, forecasting, real-time data, SPF

DSGE Model-Slovakia

DSGE Slovakia is a medium size New Keynesian open economy model designed to simulate dynamic behavior of Slovak economy. It consists of about 50 equations and contains all important macroeconomic variables including real GDP and all its main components- consumption, investment, government expenditures, import and export then factors of production – labor, capital and oil and also consumer, producer, import and export price deflators, nominal interest rate and exchange rate. Most parameters of the model are calibrated and remaining ones are estimated by various estimation technique. Appropriateness of the model is judged by comparing statistics of simulated data with real ones, by analyzing impulse response functions and by reproducing historical time series.General equilibrium model, Slovakia

On the fit and forecasting performance of New Keynesian models

The paper provides new tools for the evaluation of DSGE models and applies them to a large-scale New Keynesian dynamic stochastic general equilibrium (DSGE) model with price and wage stickiness and capital accumulation. Specifically, we approximate the DSGE model by a vector autoregression (VAR) and then systematically relax the implied cross-equation restrictions. Let --denote the extent to which the restrictions are being relaxed. We document how the in- and out-of-sample fit of the resulting specification (DSGE-VAR) changes as a function of --. Furthermore, we learn about the precise nature of the misspecification by comparing the DSGE model’s impulse responses to structural shocks with those of the best-fitting DSGE-VAR. We find that the degree of misspecification in large-scale DSGE models is no longer so large as to prevent their use in day-to-day policy analysis, yet it is not small enough that it cannot be ignored.

Monetary policy analysis with potentially misspecified models

The paper proposes a novel method for conducting policy analysis with potentially misspecified dynamic stochastic general equilibrium (DSGE) models and applies it to a New Keynesian DSGE model along the lines of Christiano, Eichenbaum, and Evans (JPE 2005) and Smets and Wouters (JEEA 2003). We first quantify the degree of model misspecification and then illustrate its implications for the performance of different interest rate feedback rules. We find that many of the prescriptions derived from the DSGE model are robust to model misspecification.

Estimation of quasi-rational DSGE monetary models

This paper proposes the estimation of small-scale dynamic stochastic general equilibrium (DSGE) monetary models under the quasi-rational expectations (QRE) hypothesis. The QRE-DSGE model is based on the idea that the determinate reduced form solution associated with the structural model, if it exists, must have the same lag structure as the ‘best fitting’ vector autoregressive (VAR) model for the observed time series. After discussing solution properties and the local identifiability of the model, a likelihood-based iterative algorithm for estimating the structural parameters and testing the data adequacy of the system is proposed. A Monte Carlo experiment shows that, even controlling for the omitted dynamics bias, the over-rejection of the nonlinear cross-equation restrictions when asymptotic critical values are used and variables are highly persistent is a relevant issue in finite samples. An application based on euro area data illustrates the advantages of using error-correcting formulations of the QRE-DSGE model when the inflation rate and the short-term interest rate are approximated as difference stationary processes. A parametric bootstrap version of the likelihood-ratio test for the implied cross-equation restrictions does not reject the estimated QRE-DSGE model.Dynamic stochastic general equilibrium model, Maximum Likelihood estimation, Quasi-Rational Expectations, VAR. Modelli DSGE, Stima di massima verosimiglianza, Aspettative Quasi-Razionali, Modelli VAR.

Can a simple DSGE model outperform Professional Forecasters?

DSGE models have recently become one of the most frequently used tools in policy analysis. Nevertheless, their forecasting proprieties are still unexplored. In this article we address this problem by examining the quality of forecasts from a small size DSGE model, a trivariate VAR model and the Philadelphia Fed Survey of Professional Forecasters. The forecast performance of these methods is analysed for the key U.S. economic variables: the three month Treasury bill yield, the GDP growth rate and the GDP price index inflation. We evaluate the ex post forecast errors on the basis of the data from the period of 1994–2006. We apply the Philadelphia Fed “Real-Time Data Set for Macroeconomists,” described by Croushore and Stark (2001a), to ensure that the information available to the SPF was exactly the same as the data used to estimate the DSGE and VAR models. Overall, the results are mixed. It appears that when comparing the root mean squared errors for some forecast horizons the DSGE model seems to outperform the SPF in forecasting the GDP growth rate. However, this characteristic turned out to be not statistically significant. In principle most forecasts of the GDP price index inflation and the short term interest rate by the SPF are significantly better than those from the DSGE model. The forecast quality of the VAR model turned out to be the worst one.forecasting, real-time data, Survey of Professional Forecasters, DSGE, VAR

Impulse response identification in DSGE models

Dynamic stochastic general equilibrium (DSGE) models have become a widely used tool for policymakers. This paper modifies the global identification theory used for structural vector autoregressions, and applies it to DSGE models. We use this theory to check whether a DSGE model structure allows for unique estimates of structural shocks and their dynamic effects. The potential cost of a lack of identification for policy oriented models along that specific dimension is huge, as the same model can generate a number of contrasting yet theoretically and empirically justifiable recommendations. The problem and methodology are illustrated using a simple New Keynesian business cycle model.

A Small BVAR-DSGE Model for Forecasting the Australian Economy

This paper estimates a small structural model of the Australian economy, designed principally for forecasting the key macroeconomic variables of output growth, underlying inflation and the cash rate. In contrast to models with purely statistical foundations, which are often used for forecasting, the Bayesian Vector Autoregressive Dynamic Stochastic General Equilibrium (BVAR-DSGE) model uses the theoretical information of a DSGE model to offset in-sample over-fitting. We follow the method of Del Negro and Schorfheide (2004) and use a variant of the small open economy DSGE model of Lubik and Schorfheide (2007) to provide prior information for the VAR. The forecasting performance of the model is competitive with benchmark models such as a Minnesota VAR and an independently estimated DSGE model.BVAR-DSGE; forecasting