Business Cycle Theory and Econometrics

We outline in turn criticisms made by econometricians of the methods used in empirical business-cycle research and then criticisms made by business-cycle researchers of some methods used by econometricians. The aim is to clarify and in some cases correct these criticisms. Overall there is no conflict in using rigourous statistical procedures to study modern dynamic stochastic general equilibrium models. We also provide a concise bibliography of recent research on statistical methods for business-cycle models.business cycles, time-series econometrics

Empirical Likelihood Block Bootstrapping

Monte Carlo evidence has made it clear that asymptotic tests based on generalized method of moments (GMM) estimation have disappointing size. The problem is exacerbated when the moment conditions are serially correlated. Several block bootstrap techniques have been proposed to correct the problem, including Hall and Horowitz (1996) and Inoue and Shintani (2006). We propose an empirical likelihood block bootstrap procedure to improve inference where models are characterized by nonlinear moment conditions that are serially correlated of possibly infinite order. Combining the ideas of Kitamura (1997) and Brown and Newey (2002), the parameters of a model are initially estimated by GMM which are then used to compute the empirical likelihood probability weights of the blocks of moment conditions. The probability weights serve as the multinomial distribution used in resampling. The first-order asymptotic validity of the proposed procedure is proven, and a series of Monte Carlo experiments show it may improve test sizes over conventional block bootstrapping.Econometric and statistical methods

Empirical Likelihood Block Bootstrapping

Monte Carlo evidence has made it clear that asymptotic tests based on generalized method of moments (GMM) estimation have disappointing size. The problem is exacerbated when the moment conditions are serially correlated. Several block bootstrap techniques have been proposed to correct the problem, including Hall and Horowitz (1996) and Inoue and Shintani (2006). We propose an empirical likelihood block bootstrap procedure to improve inference where models are characterized by nonlinear moment conditions that are serially correlated of possibly infinite order. Combining the ideas of Kitamura (1997) and Brown and Newey (2002), the parameters of a model are initially estimated by GMM which are then used to compute the empirical likelihood probability weights of the blocks of moment conditions. The probability weights serve as the multinomial distribution used in resampling. The first-order asymptotic validity of the proposed procedure is proven, and a series of Monte Carlo experiments show it may improve test sizes over conventional block bootstrapping.generalized methods of moments, empirical likelihood, block bootstrap

Information-Theoretic Estimation of Preference Parameters: Macroeconomic Applications and Simulation Evidence

This paper investigates the behaviour of estimators based on the Kullback-Leibler information criterion (KLIC), as an alternative to the generalized method of moments (GMM). We first study the estimators in a Monte Carlo simulation model of consumption growth with power utility. Then we compare KLIC and GMM estimators in macroeconomic applications, in which preference parameters are estimated with aggregate data. KLIC probability measures serve as useful diagnostics. In dependent data, tests of overidentifying restrictions in the KLIC framework have size properties comparable to those of the J-test in iterated GMM, but superior size-adjusted power.KLIC estimation, generalized method of moments, Monte Carlo

Testing the Unbiasedness Hypothesis in the Forward Foreign Exchange Market: A Specification Analysis

This paper evaluates two popular regression methods of testing the unbiasedness hypothesis in the forward foreign exchange market. For the 30-day Canada/United States forward foreign exchange market, the evidence overwhelmingly indicates that it is inappropriate to treat the structure of the systematic and stochastic components of the test relations as constant over time. Hence, conclusions inferred from parameter significance testing based upon full-sample estimation can be very misleading. Accordingly, we argue for a specification analysis of the test relations, and more explicit modelling of market fundamentals.The financial support of the Social Sciences and Humanities Research Council of Canada and the Advisory Research Committee of Queen's University is acknowledged