The classical approach for specifying statistical models with binary dependent variables in econometrics using latent variables or threshold models can leave the model misspecified, resulting in biased and inconsistent estimates as well as erroneous inferences. Furthermore, methods for trying to...

This paper focuses on the practice of serial correlation correcting of the Linear Regression Model (LRM) by modeling the error. Simple Monte Carlo experiments are used to demonstrate the following points regarding this practice. First, the common factor restrictions implicitly imposed on the...

The single most crucial weakness of current macroeconometric modeling stems from the fact that modelers ‘quantify/estimate’ their structural modeldirectly, ignoring the fact that behind every structural model there is a statistical model whose validity vis-a-vis the data underwrites the...

Statistical model specification and validation raise crucial foundational problems whose pertinent resolution holds the key to learning from data by securing the reliability of frequentist inference. The paper questions the judiciousness of several current practices, including the theory-driven...

The paper questions the appropriateness of the practice known as 'error-autocorrelation correcting' in linear regression, by showing that adopting an AR(1) error formulation is equivalent to assuming that the regressand does not Granger cause any of the regressors. This result is used to...