This paper aims at identifying a most frequently multivariate technique,Principal Components Analysis (PCA), to solve a multicollinear single equation econometric model .results of the method used were compared to Ordinary Least Squares (OLS) and(Two Stages Least Squares (2SLS) to see if...

In this research, the omitted variable problem in a spatial autoregressive model is analyzed by simulation. We examine the performances of estimators when an omitted variable is correlated with explanatory variables. In the literature, theoretical aspects of estimating spatial autoregressive...

A number of authors have suggested that omitted variables affect spatial regression methods less than ordinary least-squares (OLS). To explore these conjectures, we derive an expression for OLS omitted variable bias in a univariate model with spatial dependence and show that positive dependence...

We consider a lag-augmented two- or three-stage least squares estimator for a structural dynamic model of nonstationary and possibly cointegrated variables without the prior knowledge of unit roots or rank of cointegration. We show that the conventional two- and three-stage least squares...

We provide a simulation smoother to a exible state-space model with lagged states and lagged dependent variables. Qian (2014) has introduced this state-space model and proposes a fast Kalman filter with time-varying state dimension in the presence of missing observations in the data. In this...

In this paper, we investigate the problem of estimating distributed lags in short panels. Estimates of the parameter of distributed lag relationships based on single time-series of observations have been usually rather imprecise. The promise of panel data in this context is in the N repetitions...