This paper analyzes the stochastic inventory control problem when the demand distribution is not known. In contrast to previous Bayesian inventory models, this paper adopts a non-parametric Bayesian approach in which the firm's prior information is characterized by a Dirichlet process prior....

This paper reviews recent advances in the specification and estimation of Bayesian Vector Autoregressive models (BVARs). After describing the Bayesian principle of estimation, we first present the methodology originally developed by Litterman (1986) and Doan et al. (1984) and review alternative...

This paper analyses the empirical performance of a new Keynesian sticky price model with delayed effects of monetary impulses on inflation and output for the German pre-EMU economy. The model is augmented with rule-of-thumb behaviour in consumption and price setting. Using recently developed...

This paper describes a semiparametric Bayesian method for analyzing duration data. The proposed estimator specifies a complete functional form for duration spells, but allows flexibility by introducing an individual heterogeneity term, which follows a Dirichlet mixture distribution. I show how...

We apply both classical and Bayesian econometric methods to characterize the dynamic behavior of inflation for twelve industrial countries over the period 1984-2003, using four different price indices for each country. In particular, we estimate a univariate autoregressive (AR) model for each...

More formally, every machine learning algorithm depends on 3 things which need to be able to be programmed. First, there needs to exist an experience set, sometimes called a training set. This is data that the algorithm will “learn” from. Next, there needs to be some task, some action that...