Corrigendum to “Bayesian reduced rank regression in econometrics” [J. Econometrics 75 (1996) 121–146]

Bayesian Econometrics ; Demand ; Econometrics ; Matrix Algebra ; Maximum Likelihood Regression ; Regression

yt and Pt replaced by any variables that affect supply but not demand. In the 1970s econometricians began to recognize that how the supply or demand equation is normalized affects the estimator of the supply or demand elasticity (¡M or ¬R ) when the two-stage least squares (2SLS) approach is employed. The quality of this estimator is sensitive to the strength of instruments used in the 2SLS est...

Bayesian Econometrics

Suppose a data vector X = (X1, ..., Xn) follows a distribution with a density function pn(x|θ) which is fully characterized by some parameter vector θ = (θ1, ..., θd)′. Suppose that the prior belief about θ is characterized by a density p(θ) defined over a parameter space Θ, a subset of a Euclidian space R. Using Bayes’ rule to incorporate the information provided by the data, we can form poste...

Reduced rank regression in Bayesian FDA

In functional data analysis (FDA) it is of interest to generalize techniques of multivariate analysis like canonical correlation analysis or regression to functions which are often observed with noise. In the proposed Bayesian approach to FDA two tools are combined: (i) a special Demmler-Reinsch like basis of interpolation splines to represent functions parsimoniously and ‡exibly; (ii) latent v...

Econometrics II Lecture 3: Regression and Causality

Bayesian sparse reduced rank multivariate regression

Many modern statistical problems can be cast in the framework of multivariate regression, where the main task is to make statistical inference for a possibly sparse and low-rank coefficient matrix. The low-rank structure in the coefficient matrix is of intrinsic multivariate nature, which, when combined with sparsity, can further lift dimension reduction, conduct variable selection, and facilit...