Hidden Markov models is an extension of mixture models providing a flexible class of models exhibiting dependence and a possibly large degree of variability. In this paper the authors show how jump Markov chain Monte Carlo techniques can be used to estimate the parameters as well as the number...

This paper presents a new simulated maximum-likelihood method that rests on estimating the likelihood nonparametrically on a simulated sample. We prove that this method, which can be used on very general models, is consistent and asymptotically efficient.

Models use for natural resources prices usually preclude the possibility of large changes (jumps) resulting from discrete, unexpected events. To test for the presence of jumps and ARCH effects, we propose to use bounds and bootstrap test techniques, thus solving the unidentified nuisance...

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models.

We nowcast and forecast Austrian economic activity, namely real gross domestic product (GDP), consumption and investment, which are available at a quarterly frequency. While nowcasting uses data up to (and including) the quarter to be predicted, forecasting uses only data up to the previous...

We consider two ways to aggregate expert opinions using simple averages: averaging probabilities and averaging quantiles. We examine analytical properties of these forecasts and compare their ability to harness the wisdom of the crowd. In terms of location, the two average forecasts have the...

This collection of papers analyzes the versatility and predictive power of survey expectations data in asset pricing and macroeconomic forecasting. The first paper, Using Sentiment Surveys to Predict GDP Growth and Stock Returns sheds new light on the question of whether or not sentiment...