Malin Adolfson

Evaluating an Estimated New Keynesian Small Open Economy Model

This paper estimates and tests a new Keynesian small open economy model in the tradition of Christiano, Eichenbaum, and Evans (2005) and Smets and Wouters (2003) using Bayesian estimation techniques on Swedish data. To account for the switch to an inflation targeting regime in 1993 we allow for a discrete break in the central banks instrument rule. A key equation in the model - the uncovered interest rate parity (UIP) condition - is well known to be rejected empirically. Therefore we explore the consequences of modifying the UIP condition to allow for a negative correlation between the risk premium and the expected change in the nominal exchange rate. The results show that the modification increases the persistence and volatility in the real exchange rate and that this model has an empirical advantage compared with the standard UIP specification.

Modern Forecasting Models in Action: Improving Macroeconomic Analyses at Central Banks

There are many indications that formal methods are not used to their full potential by central banks today. In this paper, using data from Sweden, we demonstrate how BVAR and DSGE models can be used to shed light on questions that policymakers deal with in practice. We compare the forecast performance of BVAR and DSGE models with the Riksbanks official, more subjective forecasts, both in terms of actual forecasts and root mean-squared errors. We also discuss how to combine model- and judgment-based forecasts, and show that the combined forecast performs well out of sample. In addition, we show the advantages of structural analysis and use the models for interpreting the recent development of the inflation rate through historical decompositions. Last, we discuss the monetary transmission mechanism in the models by comparing impulse-response functions.

Forecasting Performance of an Open Economy DSGE Model

This paper analyzes the forecasting performance of an open economy dynamic stochastic general equilibrium (DSGE) model, estimated with Bayesian methods, for the Euro area during 1994Q1–2002Q4. We compare the DSGE model and a few variants of this model to various reduced-form forecasting models such as vector autoregressions (VARs) and vector error correction models (VECM), estimated both by maximum likelihood and two different Bayesian approaches, and traditional benchmark models, e.g., the random walk. The accuracy of point forecasts, interval forecasts and the predictive distribution as a whole are assessed in an out-of-sample rolling event evaluation using several univariate and multivariate measures. The results show that the open economy DSGE model compares well with more empirical models and thus that the tension between rigor and fit in older generations of DSGE models is no longer present. We also critically examine the role of Bayesian model probabilities and other frequently used low-dimensional summaries, e.g., the log determinant statistic, as measures of overall forecasting performance.

EMPIRICAL PROPERTIES OF CLOSED- AND OPEN-ECONOMY DSGE MODELS OF THE EURO AREA

In this paper, we compare the empirical proper-ties of closed- and open-economy DSGE models estimated on Euro area data. The comparison is made along several dimensions; we examine the models in terms of their marginal likelihoods, forecasting performance, variance decompositions, and their transmission mechanisms of monetary policy.

Bayesian Analysis of DSGE Models—Some Comments

Sungbae An and Frank Schorfheide have provided an excellent review of the main elements of Bayesian inference in Dynamic Stochastic General Equilibrium (DSGE) models. Bayesian methods have, for reasons clearly outlined in the paper, a very natural role to play in DSGE analysis, and the appeal of the Bayesian paradigm is indeed strongly evidenced by the flood of empirical applications in the area over the last couple of years. We expect their paper to be the natural starting point for applied economists interested in learning about Bayesian techniques for analyzing DSGE models, and as such the paper is likely to have a strong influence on what will be considered best practice for estimating DSGE models. The authors have, for good reasons, chosen a stylized six-equation model to present the methodology. We shall use here the large-scale model in Adolfson et al. (2005), henceforth ALLV, to illustrate a few econometric problems which we have found to be especially important as the size of the model increases. The model in ALLV is an open economy extension of the closed economy model in Christiano et al. (2005). It consists of 25 log-linearized equations, which can be written as a state space representation with 60 state variables, many of them unobserved. Fifteen observed unfiltered time series are used to estimate 51 structural parameters. An additional complication compared to the model in An and Schorfheides paper is that some of the coefficients in the measurement equation are non-linear functions of the structural parameters. The model is currently the main vehicle for policy analysis at Sveriges Riksbank (Central Bank of Sweden) and similar models are being developed in many other policy institutions, which testifies to the models practical relevance. The version considered here is estimated on Euro area data over the period 1980Q1–2002Q4. We refer to ALLV for details.

Parameter Identification in an Estimated New Keynesian Open Economy Model

In this paper, we use Monte Carlo methods to study the small sample properties of the classical maximum likelihood (ML) estimator in artificial samples generated by the New- Keynesian open economy DSGE model estimated by Adolfson et al. (2008) with Bayesian techniques. While asymptotic identification tests show that some of the parameters are weakly identified in the model and by the set of observable variables we consider, we document that ML is unbiased and has low MSE for many key parameters if a suitable set of observable variables are included in the estimation. These findings suggest that we can learn a lot about many of the parameters by confronting the model with data, and hence stand in sharp contrast to the conclusions drawn by Canova and Sala (2009) and Iskrev (2008). Encouraged by our results, we estimate the model using classical techniques on actual data, where we use a new simulation based approach to compute the uncertainty bands for the parameters. From a classical viewpoint, ML estimation leads to a significant improvement in fit relative to the log-likelihood computed with the Bayesian posterior median parameters, but at the expense of some the ML estimates being implausible from a microeconomic viewpoint. We interpret these results to imply that the model at hand suffers from a substantial degree of model misspecification. This interpretation is supported by the DSGE-VAR( ) analysis in Adolfson et al. (2008). Accordingly, we conclude that problems with model misspecification, and not primarily weak identification, is the main challenge ahead in developing quantitative macromodels for policy analysis.