In this paper we study various methods for detecting the co integrating rank as the number of variables gets large. We show that the use of standard tools will always lead to misleading inferences in such settings due to excessive size distortions. Particularly the LR test tends to produce too...

Since the objective of economic policy is to change target variables in the DGP, when economic policy analysis uses an econometric model, it is important that the model delivers reliable inferences about policy responses in the DGP. This requires that the model be congruent and encompassing, and...

The Fisherian hypothesis was tested for four regional agricultural interest rates in the 11th Federal Reserve District (Dallas). These interest rates represented agricultural loans of different terms to maturity. Shocks in expected inflation resulted in positive but less than equivalent...

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with...

We consider selecting an econometric model when there is uncertainty over both the choice of variables and the occurrence and timing of multiple location shifts.  The theory of general-to-simple (Gets) selection is outlined and its efficacy demonstrated in a new set of simulation experiments...

Big Data offer potential benefits for statistical modelling, but confront problems like an excess of false positives, mistaking correlations for causes, ignoring sampling biases, and selecting by inappropriate methods.  We consider the many important requirements when searching for a data-based...

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with...

This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with...

A specification search algorithm is proposed that aims to assist the user in the process of constructing Vector Error Correction Models. The algorithm automates testing the cointegration rank of the system and performs simplifications based on possible weak exogeneity of some variables....