In this paper, I examine the properties of the class of generalized empirical likelihood estimators of moment-condition models. These nonparametric likelihood estimators satisfy exactly the moment conditions and automatically remove any bias due to a lack of centering. Moreover, the bias of the...

This paper considers the economic implications of having unit roots in stochastic processes of variables like consumption or GDP. Using a variety of models, we develop indirect tests for unit roots based on sharp distinctions that should arise when the scale variable is either difference...

This paper introduces Bayesian inference in a Markov switching partial cointegration model. The partial cointegration allows the cointegration relationships to be switched on and off depending on the regime, unlike conventional cointegration analysis that assumes linear adjustment toward...

Many economic time series are charecterized by high persistence which typically requires nonstandard limit theory for inference. This paper proposes a new method for constructing confidence intervals for the impulse response functions of nearly nonstationary processes. The method is based on...

Recent developments in the aggregation of large cross section of linear time series processes provide a complete characterization of the link between the dynamic properties of the aggregate series and the shape and degree of heterogeneity of the coefficients of the micro time series (see Lippi...

In this paper we develop a likelihood based approach for estimating the joint equilibrium parameter distribution in random coefficient-random utility models. Under this demand specification and a profit maximizing supply specification, the equilibrium distribution of prices and quantities has an...

Berry, Levinsohn, and Pakes (1995) developed an estimator for an equilibium model of differentiated products markets using aggregate data, without assuming the existence of a representative agent, or imposing prior restrictions on elasticities. Their estimator though, was computationally...