NON‐LINEAR DSGE MODELS AND THE CENTRAL DIFFERENCE KALMAN FILTER

This paper introduces a quasi maximum likelihood approach based on the central difference Kalman filter to estimate non‐linear dynamic stochastic general equilibrium (DSGE) models with potentially non‐Gaussian shocks. We argue that this estimator can be expected to be consistent and asymptotically normal for DSGE models solved up to third order. These properties are verified in a Monte Carlo study for a DSGE model solved to second and third order with structural shocks that are Gaussian, Laplace distributed, or display stochastic volatility. Copyright © 2012 John Wiley & Sons, Ltd.     

SEQUENTIAL MONTE CARLO SAMPLING FOR DSGE MODELS

We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models; wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using two empirical illustrations consisting of the Smets and Wouters model and a larger news shock model we show that the SMC algorithm is better suited for multimodal and irregular posterior distributions than the widely used random walk Metropolis–Hastings algorithm. We find that a more diffuse prior for the Smets and Wouters model improves its marginal data density and that a slight modification of the prior for the news shock model leads to drastic changes in the posterior inference about the importance of news shocks for fluctuations in hours worked. Unlike standard Markov chain Monte Carlo (MCMC) techniques; the SMC algorithm is well suited for parallel computing. Copyright © 2014 John Wiley & Sons, Ltd.     

PRACTICAL TOOLS FOR POLICY ANALYSIS IN DSGE MODELS WITH MISSING SHOCKS

In this paper we analyze the propagation of shocks originating in sectors that are not present in a baseline dynamic stochastic general equilibrium (DSGE) model. Specifically, we proxy the missing sector through a small set of factors that feed into the structural shocks of the DSGE model to create correlated disturbances. We estimate the factor structure by either matching impulse responses of the augmented DSGE model to those generated by an auxiliary model or by using Bayesian techniques. We apply this methodology to track the effects of oil shocks and housing demand shocks in models without energy or housing sectors. Copyright © 2014 John Wiley & Sons, Ltd.     

COMBINING REVEALED AND STATED PREFERENCE DATA TO ESTIMATE THE NONMARKET VALUE OF ECOLOGICAL SERVICES: AN ASSESSMENT OF THE STATE OF THE SCIENCE

Abstract This paper reviews the marketing, transportation and environmental economics literature on the joint estimation of revealed preference (RP) and stated preference (SP) data. The RP and SP approaches are first described with a focus on the strengths and weaknesses of each. Recognizing these strengths and weaknesses, the potential gains from combining data are described. A classification system for combined data that emphasizes the type of data combination and the econometric models used is proposed. A methodological review of the literature is pursued based on this classification system. Examples from the environmental economics literature are highlighted. A discussion of the advantages and disadvantages of each type of jointly estimated model is then presented. Suggestions for future research, in particular opportunities for application of these models to environmental quality valuation, are presented.     

DESIGNING EXPERIMENTS TO FIT AND VALIDATE REGRESSION MODELS

A graphical procedure is proposed for selecting designs suitable for estimation and validation of linear regression models. The procedure is exemplified with polynomial regression involving one and two explanatory variables.     

ECONOMETRIC APPROACHES TO EMPIRICAL MODELS OF EXCHANGE RATE DETERMINATION

Abstract. This paper identifies four principal econometric approaches to the estimation and testing of asset market models of exchange rate determination: the traditional, static reduced-form approach; the error correction and co-integration, dynamic reduced-form approaches; the simultaneous equations approach; and large scale, multi-equation macroeconometric simulation models. Each of these econometric approaches is evaluated with respect to its theoretical validity and the comparative properties of the empirical results obtained. This leads to the conclusion that although there may be little to choose between the different theoretical exchange rate models, there may be grounds for favouring a multi-equation, simultaneous estimation procedure for this class of models.     

IS THE TRANSITION TO THE MARKET TOO IMPORTANT TO BE LEFT TO THE MARKET?

Most models of Central and Eastern European transition fail to appreciate the de facto organising principles that governed life in the Soviet-type system. Concentration has instead been focused on the de jure pronouncements of what constituted these systems. It is this misidentification, we contend, that has caused the major problems for economists devising strategies for reform. Proper appreciation of the de facto realities underlying Soviet-type economies sheds light on the crucial role that market forces must be allowed to play in the process of transition.     

THE DISTRIBUTION OF WEALTH: MEASUREMENT AND MODELS

Abstract. Analysts debating the consequences of a policy change for the wealth distribution may come to different conclusions because of different views about how the distribution should be defined and measured, or about the processes determining the distribution. The aim of this survey is to provide an analytical framework within which such conflicts may be assessed. The first part of the paper discusses conceptual issues in the definition of 'wealth', and compares methods of deriving estimates of wealth distribution. The second, and larger, part of the paper surveys lifecycle and intergenerational models of the distribution of wealth, including a discussion of the role played by inheritance. The presentation is largely theoretical. Indeed, one of the paper's conclusions is that empirical modelling of the wealth distribution is under-developed, at least for the purposes of addressing many topical policy issues.     

SEMI-PARAMETRIC ESTIMATION OF HURDLE REGRESSION MODELS WITH AN APPLICATION TO MEDICAID UTILIZATION

This paper develops a semi-parametric estimation method for hurdle (two-part) count regression models. The approach in each stage is based on Laguerre series expansion for the unknown density of the unobserved heterogeneity. The semi-parametric hurdle model nests Poisson and negative binomial hurdle models, which have been used in recent applied literature. The empirical part of the paper evaluates the impact of managed care programmes for Medicaid eligibles on utilization of health-care services using a key utilization variable, the number of doctor and health centre visits. Health status measures and age seem to be more important in determining health-care utilization than other socio-economic and enrollment variables. The semi-parametric approach is particularly useful for the analysis of overdispersed individual level data characterized by a large proportion of non-users, and highly skewed distribution of counts for users. © 1997 John Wiley & Sons, Ltd.