Article

A Method for Taking Models to the Data

Department of Economics, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467-3806, USA
Journal of Economic Dynamics and Control (Impact Factor: 0.86). 02/2004; 28(6):1205-1226. DOI: 10.1016/S0165-1889(03)00080-0
Source: RePEc

ABSTRACT

This paper develops a method for combining the power of a dynamic, stochastic, general equilibrium model with the flexibility of a vector autoregressive time-series model to obtain a hybrid that can be taken directly to the data. It estimates this hybrid model via maximum likelihood and uses the results to address a number of issues concerning the ability of a prototypical real business cycle model to explain movements in aggregate output and employment in the postwar US economy, the stability of the real business cycle model's structural parameters, and the performance of the hybrid model's out-of-sample forecasts.

Full-text preview

Available from: bc.edu
  • Source
    • "In this section, we compare the estimation results of the two monetary policy rules given their alternative assumptions about the information utilized in making policy decisions. To estimate these area-wide and multi-country variants of the New Keynesian monetary model, we follow the dynamic stochastic general equilibrium (DSGE) approach, as suggested by Ireland (2004). This estimation strategy has become a standard procedure (e.g., Sö derlind, 1999; María-Dolores and Vá zquez, 2006; Jondeau and Sahuc, 2008a,b) for evaluating optimal monetary policy rules. "
    Dataset: ECOSYS 2009

    Full-text · Dataset · Sep 2015
  • Source
    • "In this section, we compare the estimation results of the two monetary policy rules given their alternative assumptions about the information utilized in making policy decisions. To estimate these area-wide and multi-country variants of the New Keynesian monetary model, we follow the dynamic stochastic general equilibrium (DSGE) approach, as suggested by Ireland (2004). This estimation strategy has become a standard procedure (e.g., Sö derlind, 1999; María-Dolores and Vá zquez, 2006; Jondeau and Sahuc, 2008a,b) for evaluating optimal monetary policy rules. "
    Dataset: ECOSYS 2009

    Full-text · Dataset · Sep 2015
  • Source
    • "These models appear to be particularly suited for conducting policy evaluation, as shown in the works of Smets and Wouters (2003, 2004), Del Negro and Schorfheide (2004), Adolfson et al. (2008) and Christiano et al. (2005). However, calibrated DSGE models face many important challenges such as the fragility of parameter estimates, statistical fit and the weak reliability of policy forecasts as reported in Stock and Watson (2001), Ireland (2004) and Schorfheide (2010). In recent years Bayesian estimation has become popular mainly because it provides a system-based estimation approach that offers the advantage of employing prior assumptions about the parameters based on economic theory. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Advanced Bayesian methods are employed in estimating dynamic stochastic general equilibrium (DSGE) models. Although policymakers and practitioners are particularly interested in DSGE models, these are typically too stylized to be taken directly to the data and often yield weak prediction results. Hybrid models can deal with some of the DSGE model misspecifications. Major advances in Bayesian estimation methodology could allow these models to outperform well-known time series models and effectively deal with more complex real-world problems as richer sources of data become available. A comparative evaluation of the out-of-sample predictive performance of many different specifications of estimated DSGE models and various classes of VAR models is performed, using datasets from the US economy. Simple and hybrid DSGE models are implemented, such as DSGE–VAR and Factor Augmented DSGEs and tested against standard, Bayesian and Factor Augmented VARs. Moreover, small scale models including the real gross domestic product, the harmonized consumer price index and the nominal short-term federal funds interest rate, are comparatively assessed against medium scale models featuring additionally sticky nominal prices, wage contracts, habit formation, variable capital utilization and investment adjustment costs. The investigated period spans 1960:Q4–2010:Q4 and forecasts are produced for the out-of-sample testing period 1997:Q1–2010:Q4. This comparative validation can be useful to monetary policy analysis and macro-forecasting with the use of advanced Bayesian methods.
    Full-text · Article · Mar 2014 · Computational Statistics & Data Analysis
Show more