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.

In this chapter, we study the asymptotic distributions for ordinary least squares (OLS), fully modified OLS (FMOLS), and dynamic OLS (DOLS) estimators in cointegrated regression models in panel data. We show that the OLS, FMOLS, and DOLS estimators are all asymptotically normally distributed....

Relying on Clive Granger's many and varied contributions to econometric analysis, this paper considers some of the key econometric considerations involved in estimating Taylor-type rules for US data. We focus on the roles of unit roots, cointegration, structural breaks, and non-linearities to...

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...

Band spectrum regression is considered for cointegrated time series with long memory innovations. The estimates we advocate are shown to be consistent when cointegrating relationships among stationary variables are investigated, while OLS are inconsistent due to correlation between the regressor...

We provide a new asymptotic theory for local time density estimation for a general class of functionals of integrated time series. This result provides a convenient basis for developing an asymptotic theory for nonparametric cointegrating regression and autoregression. Our treatment directly...

We interpret the linear relations from exact rational expectations models as restrictions on the parameters of the statistical model called the cointegrated vector autoregressive model for non-stationary variables. We then show how reduced rank regression, Anderson (1951), plays an important...