Linear Methods are often used to compute approximate solutions to dynamic models, as these models often cannot be solved analytically. Linear methods are very popular, as they can easily be implemented. Also, they provide a useful starting point for understanding more elaborate numerical...

We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids. The Smolyak operator underlying the sparse grids approach frees global approximation from the curse of dimensionality and we apply it to a Chebyshev approximation of the...

We present an object-oriented software framework allowing to specify, solve, and estimate nonlinear dynamic general equilibrium (DSGE) models. The implemented solution methods for finding the unknown policy function are the standard linearization around the deterministic steady state, and a...

Stock picking is the field of financial analysis that is of particular interest for many professional investors and researchers. In this study stock picking is implemented via binary classification trees. Optimal tree size is believed to be the crucial factor in forecasting performance of the...

We introduce new stylometry tools based on the sliced conditional compression complexity of literary texts which are inspired by the nearly optimal application of the incomputable Kolmogorov conditional complexity (and presumably approximates it). Whereas other stylometry tools can occasionally...

This paper is intended as a guide to building insurance risk (loss) models. A typical model for insurance risk, the so-called collective risk model, treats the aggregate loss as having a compound distribution with two main components: one characterizing the arrival of claims and another...

In this paper, we review the most common specifications of discrete-time stochasticvolatility (SV) models and illustrate the major principles of corresponding MarkovChain Monte Carlo (MCMC) based statistical inference. We provide a hands-on approachwhich is easily implemented in empirical...

The purpose of this paper is to propose a new likelihood-based panel cointegration testin the presence of a linear time trend in the data generating process. This new test is an extensionof the likelihood ratio (LR) test of Saikkonen & L¨utkepohl (2000) for trend-adjusteddata to the panel data...

In this paper we analyse bootstrap procedures for systems cointegration tests with a prior adjustment for deterministic terms suggested by Saikkonen & Lütkepohl (2000b) and Saikkonen, Lütkepohl & Trenkler (2006). The asymptotic properties of the bootstrap test procedures are derived and their...