A large number of papers have been written by physicists documenting an alleged signature of imminent financial crashes involving so-called log-periodic oscillations-oscillations which are periodic with respect to the logarithm of the time to the crash. In addition to the obvious practical...

In this paper, the author presents an efficient method of analyzing an interest-rate model using a new approach called 'data augmentation Bayesian forecasting.' First, a dynamic linear model estimation was constructed with a hierarchically-incorporated model. Next, an observational replication...

This paper presents a model in which rational and emotional investors are compelled to make decisions under uncertainty in order to ensure their survival. Using a neurofinancial setting, we show that, when different investor types fight for market capital, emotional traders tend not only to...

Multivariate economic and business data frequently suffer from a missing data phenomenon that has not been sufficiently explored in the literature: both the independent and dependent variables for one or more dimensions are absent for some of the observational units. For example, in choice based...

We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past information. We provide sufficient conditions for geometric...

Datenqualität, Genauigkeit bzw. Ungenauigkeit von Daten und anderen Informationen sind grundlegende Aspekte von Messungen und Beobachtungen, die quantitativ beschrieben werden müssen, um unrealistische Resultate von Analysen zu vermeiden. In vielen praktischen Anwendungen erscheint die Angabe...

Historically, time series forecasts of economic variables have used only a handful of predictor variables, while forecasts based on a large number of predictors have been the province of judgmental forecasts and large structural econometric models. The past decade, however, has seen considerable...

Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of...