An algorithm to estimate time-varying parameter SURE models under different types of restriction
A nonparametric method to estimate time-varying coefficients in seemingly unrelated regression equations models is proposed. The procedure presents two main advantages with respect to other proposals in literature. First, it allows to incorporate both cross and time-varying restrictions into the parameters. Second, the estimator is obtained in a closed form, and there is no need of an iterative method to compute its value. However, this computation requires to solve a linear system where the number of equations and coefficients increases with the sample size. This problem is overcome by using an algorithm that reduces the computation cost. The algorithm enables to solve the linear system in a recursive manner where (in each step) a lower dimensional linear system is solved and there is no increase with sample size. (C) 2002 Elsevier Science B.V. All rights reserved.
nonparametric methods constrained estimators seasonal restrictions cross restrictions