We consider a varying coefficient regression model for sparse functional data, with time varying response variable depending linearly on some time independent covariates with coefficients as functions of time dependent covariates. Based on spline smoothing, we propose data driven simultaneous...

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...

Random subspace methods are a novel approach to obtain accurate forecasts in high-dimensional regression settings. We provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables. We focus on two approaches....