Most time series models used in econometrics and empirical finance are estimated withmaximum likelihood methods, in particular when interest centers on density and Value{at{Risk (VaR) prediction. The standard maximum likelihood principle implicitly placesequal weight on each of the observations...

This paper introduces a new method for nonlinear continuous-discrete filtering.It is shown that the Fokker-Planck-Equation can be solved numericallyby using distributed approximating functionals (DAFs). Theapproximation is very accurate and resolves the time interval between observationsin one...

A structural equation model (SEM) with deterministic intercepts isintroduced. The gaussian likelihood function does not contain determinantsof sample moment matrices and is thus well dened for only onestatistical unit. The SEM is applied to the dynamic state space model andcompared with the...

In this paper, we discuss the derivation and application of a bivariate ordered probit modelwith mixed effects. Our approach allows one to estimate the distribution of the effect (gamma) of anendogenous ordered variable on an ordered explanatory variable. By allowing gamma to vary over...

Schätzungen für Parameter einer unbekannten Grundgesamtheit aus vorgegebenen Stichprobendaten mit Hilfe des von R. A. Fisher (1890-1962) eingeführten Maximum-Likelihood Prinzips (gleich Größ-tes Wahrscheinlichkeitsprinzip) sind integraler Bestandteil von Grundkursen in Schließender...

This paper examines the impact of self-reported work limitation on the labour forceparticipation of the Australian working age population. Five consecutive waves of theHousehold, Income and Labour Dynamics in Australia (HILDA) Survey are used toinvestigate this relationship...

The stochastic frontier analysis (Aigner et al., 1977, Meeusen and van de Broeck, 1977)is widely used to estimate individual efficiency scores. The basic idea lies in the introductionof an additive error term consisting of a noise and an inefficiency term. Most oftenthe assumption of a...

The ARCH model introduced by Engle [1982] and extended to the GARCH model byBollerslev [1986] is able to capture some of the stylized facts of nancial data Rama [2001].The analysis of nancial time series is sensitive to outliers, due to the temporal dependencein the data. According to Carnero et...

This paper introduces a new method for online estimation and predictionof states and parameters of nonlinear stochastic differential equations. Inthis setup parameters are considered as random variables in a Bayesiansense, which requires integration over parameter distributions. This...

Linear stochastic dierential equations (SDE) are expressed as an exactdiscrete model (EDM) and estimated with structural equation models(SEM) and the Kalman lter (KF) algorithm. The SEM likelihood is welldened even for the times series case and the SEM and KF approach yieldthe same likelihood....