Instrumental variables estimation is classically employed to avoid simultaneous equations bias in a stable environment. Here we use it to improve upon ordinary least squares estimation of cointegrating regressions between nonstationary and/or long memory stationary variables where the...

1. Introduction 2 -- Start using Gretl and R 3 -- Basic Material 4 -- Hypothesis testing 5 -- Simple linear regression 6 -- Multiple regression 7 -- Regression using dummy variables 8 -- Non linear models 9 -- Time series analysis 10 -- Other statistical tools.

We propose a method to explore the causal transmission of an intervention through two endogenous variables of interest. We refer to the intervention as a catalyst variable. The method is based on the reduced-form system formed from the conditional distribution of the two endogenous variables...

It has been know since Phillips and Hansen (1990) that cointegrated systems can be consistently estimated using stochastic trend instruments that are independent of the system variables. A similar phenomenon occurs with deterministically trending instruments. The present work shows that such...

Cointegration requires all the variables in the system to have exact unit roots; accordingly it is conventional for researchers to test for a unit root in each variable prior to a cointegration analysis. Unfortunately, these unit root tests are not powerful. Meanwhile, conventional cointegration...

This paper proposes new cointegration tests based on instrumental variable (IV) estimation. An important property of our tests is that the asymptotic distribution remains standard normal (or Chi-square) regardless of the number of regressors, differing deterministic terms, structural dummies,...