We propose a Generalized Method of Moments (GMM) Lagrange multiplier statistic, i.e. the K-statistic, that uses the Jacobian at the evaluated parameter value instead of the expected Jacobian. To obtain its limit behavior, we use a novel assumption that brings GMM closer to maximum likelihood and...

We show that statistical inference on the risk premia in linear factor models that is based on the Fama–MacBeth (FM) and generalized least squares (GLS) two-pass risk premia estimators is misleading when the β’s are small and/or the number of assets is large. We propose novel statistics,...

We propose in this paper a likelihood-based framework for cointegration analysis in panels of a fixed number of vector error correction models. Maximum likelihood estimators of the cointegrating vectors are constructed using iterated Generalized Method of Moments estimators. Using these...

In this paper we extend the univariate periodic integration model to multivariate cointegrated time series. We analyze representation issues of a multivariate periodic model. We argue that simple adding an index s to the parameters in an otherwise nonperiodic Vector AutoRegression (VAR) leads to...

Diffuse priors lead to pathological posterior behaviour 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, and a posteriori favour...

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...