Traditional choice models assume that observable behavior results from an unspecified evaluation process of the observed individual. When it comes to the revelation of this process mere choice models rapidly meet their boundaries, as psychological factors (e.g., consumers' perception or...

We consider two semiparametric models for the weight function in a bias sample model. The object of our interest parametrizes the weight function, and it is either Euclidean or non Euclidean. One of the models discussed in this paper is motivated by the estimation the mixing distribution of...

State price density (SPD) contains important information concerning market expectations. In existing literature, a constrained estimator of the SPD is found by nonlinear least squares in a suitable Sobolev space. We improve the behavior of this estimator by implementing a covariance structure...

In klassischen Wahlmodellen wird davon ausgegangen, dass sich ein beobachtetes Verhalten durch einen nicht näher spezifizierbaren Evaluationsprozess des beobachteten Individuums ergibt. Ist die Aufdeckung dieses Prozesses von Interesse, stoßen reine Wahlmodelle schnell an ihre methodischen...

We consider two semiparametric models for the weight function in a biased sample model. The object of our interest parametrizes the weight function, and it is either Euclidean or non Euclidean. One of the models discussed in this paper is motivated by the estimation the mixing distribution of...

A standard quantitative method to access credit risk employs a factor model based on joint multivariate normal distribution properties. By extending a one-factor Gaussian copula model to make a more accurate default forecast, this paper proposes to incorporate a state-dependent recovery rate...

We show that house prices from Aberdeen in the UK improve in- and out-of-sample oil price forecasts. The improvements are of a similar magnitude to those attained using macroeconomic indicators. We explain these forecast improvements with the dominant role of the oil industry in Aberdeen. House...

We propose a local adaptive multiplicative error model (MEM) accommodating timevarying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time....

We model the dynamic volatility and correlation structure of electricity futures of the European Energy Exchange index. We use a new multiplicative dynamic conditional correlation (mDCC) model to separate long-run from short-run components. We allow for smooth changes in the unconditional...

Market value predictions for residential properties are important for investment decisions and the risk management of households, banks, and real estate developers. The increased access to market data has spurred the development and application of Automated Valuation Models (AVMs), which can...