The paper proposes an approach to evaluate hypotheses about transition dynamics when only the distributions at two points in time are observed. Using the principle of statistical mechanics, we show how to adjust in the "most probable" way a hypothesis so that it becomes compatible with the...

The paper proposes an approach to evaluate hypotheses about transition dynamics when only the distributions at two points in time are observed. Using the principle of statistical mechanics, we show how to adjust in the most probable way a hypothesis so that it becomes compatible with the...

Based on a relative entropy approach, this paper proposes a method to estimate or update transition matrices using just cross-sectional observations at two points in time. The method is then applied to explain the development of the US income distribution. Starting from three hypothesized...

Based on a relative entropy approach, this paper proposes a method to estimate or update transition matrices using just cross-sectional observations at two points in time. The method is then applied to explain the development of the US income distribution. Starting from three hypothesized...

We give stable finite order VARMA(p*; q*) representations for M-state Markov switching second-order stationary time series whose autocovariances satisfy a certain matrix relation. The upper bounds for p* and q* are elementary functions of the dimension K of the process, the number M of regimes,...

We propose a new three-step model-selection framework for size distributions in empirical data. It generalizes a recent frequentist plausibility-of-fit analysis (Step 1) and combines it with a relative ranking based on the Bayesian Akaike Information Criterion (Step 2). We enhance these...

We define tail interdependence as a situation where extreme outcomes for some variables are informative about such outcomes for other variables. We extend the concept of multiinformation to quantify tail interdependence, decompose it into systemic and residual interdependence and measure the...

We define tail interdependence as a situation where extreme outcomes for some variables are informative about such outcomes for other variables. We extend the concept of multiinformation to quantify tail interdependence, decompose it into systemic and residual interdependence and measure the...