This paper proposes the Bayesian semiparametric dynamic Nelson-Siegel model, where the density of the yield curve factors and thereby the density of the yields are estimated along with other model parameters. This is accomplished by modeling the error distributions of the factors according to a...

The “hollowing-out,” or “two poles” hypothesis is tested in the context of a Markov chain model of exchange rate transitions. In particular, two versions of the hypothesis—that hard pegs are an absorbing state, or that fixes and floats form a closed set, with no transitions to...

В статье раскрываются основные этапы развития теории и практики риск-менеджмента. При этом особое внимание уделено осмыслению понятия «риск» в экономической...

GARCH models are commonly used as latent processes in econometrics, financial economics and macroeconomics. Yet no exact likelihood analysis of these models has been provided so far. In this paper we outline the issues and suggest a Markov chain Monte Carlo algorithm which allows the calculation...

This paper develops the theoretical background for the Limited Information Bayesian Model Averaging (LIBMA). The proposed approach accounts for model uncertainty by averaging over all possible combinations of predictors when making inferences about the variables of interest, and it...

This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited Information Bayesian Model Averaging (LIBMA) methodology and then test it using simulated data. Simulation...

In this paper we discuss consistency of the posterior distribution in cases where the Kullback-Leibler condition is not verified. This condition is stated as : for all $\epsilon 0$ the prior probability of sets in the form $\{f ; KL(f0 , f ) \leq \epsilon\}$ where KL(f0 , f ) denotes the...

In the context of nonparametric Bayesian estimation a Markov chain Monte Carlo algorithm is devised and implemented to sample from the posterior distribution of the drift function of a continuously or discretely observed one-dimensional diffusion. The drift is modeled by a scaled linear...

The subject of this paper is the estimation of a probability measure on Rd from the data observed with an additive noise, under the Wasserstein metric of order p (with p≥1). We assume that the distribution of the errors is known and belongs to a class of supersmooth distributions, and we give...