We establish the relationships between certain Bayesian and classical approaches to instrumental variable regression. We determine the form of priors that lead to posteriors for structural parameters that have similar properties as classical 2SLS and LIML and in doing so provide some new insight...

We propose a natural conjugate prior for the instrumental variables regression model. The prior is a natural conjugate one since the marginal prior and posterior of the structural parameter have the same functional expressions which directly reveal the update from prior to posterior. The...

Generalized Method of Moments (GMM) Estimators are derived for Reduced Rank Regression Models, the Error Correction Cointegration Model (ECCM) and the Incomplete Simultaneous Equations Model (INSEM). The GMM (2SLS) estimators of the cointegrating vector in the ECCM are shown to have normal...

We present a new framework for the joint estimation of the default-free government term structure and corporate credit spread curves. By using a high-quality data set of German mark denominated bonds, we show that this yields more realistic spreads than conventionally obtained spread curves that...

Parameters in AutoRegressive Moving Average (ARMA) models are locally nonidentified, due to the problem of root cancellation. Parameters can be constructed which represent this identification problem. We argue that ARMA parameters should be analyzed conditional on these identifying parameters....

Statistical inference in nested linear models that result from linear restrictions on the parameters of encompassing linear models can be considered to result from the conditional distribution under the encompassing model. We extend this reasoning to nested models that result from general...

Diffuse priors lead to pathological posterior behavior when used in Bayesian analyses of Simultaneous Equation Models (SEMs). This results from the local nonidentification of certain parameters in SEMs. When this, a priori known, feature is not captured appropriately, an a posteriori favor for...

Many common statistical models can be specified as linear models with restrictions imposed on the parameters. A large amount of these models impose restrictions which do not allow for the analytical construction of the probability density function (pdf) of the parameters given the restrictions....

We consider representation, estimation and inference on cointegration in a (PVAR). We show that cointegration amounts to a restriction on a product of parameter matrices. We therefore use GMM to construct estimators of the long-run (cointegration) parameters and to obtain test statistics for...

We construct limiting and small sample distributions of maximum likelihood estimators (mle) from the property that they satisfy the first order condition (foc). The foc relates the mle of the analyzed model to the mle of an encompassing model and shows that the mle of the analyzed model is a...