We present a global sensitivity analysis that quantifies the impact of parameter uncertainty on model outcomes. Specifically, we propose variance-decomposition-based Sobol' indices to establish an importance ranking of parameters and univariate effects to determine the direction of their impact....

The four most readily available tests of autocorrelation in dynamic models namely Durbin's M test, Durbin's H test, Breusch-Godfrey (BGF) test and Ljung and Box (Q) test are compared in terms of their power for varying sample sizes, levels of autocorrelation and significance using Monte Carlo...

Sensitivity analysis assesses the influence of input parameters on the conclusion of a model. Traditional analysis methods—based on evaluating the model at a reference parameter vector and changing one parameter at a time—are local, linear, and usually do not capture interactions among the...

Analyzing price data from sequential German electricity markets, namely the day-ahead and intraday auction, a puzzling but apparently systematic pattern of price premiums can be identified. The price premiums are highly correlated with the underlying demand profile. As there is evidence that...

Was erwartet mich an der Hochschule? Werde ich den Anforderungen gerechtwerden können? Wie ist das in einer Vorlesung? Wie sind die Professoren?Wer studiert außer mir das Fach? Diese und weitere Fragen beschäftigen jedenStudienanfänger. Der Schritt von der Schule oder aus der beruflichen...

We model the dynamics of asset prices and associated derivatives by considerationof the dynamics of the conditional probability density process for the value of an assetat some specied time in the future. In the case where the asset is driven by Brownianmotion, an associated \master equation"...

Since Paul Samuelson introduced the theory of revealed preference, it has become one of the most important concepts in economics. This chapter surveys some recent contributions in the revealed preference literature. We depart from Afriat's theorem, which provides the conditions for a data set to...

We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The „curse of dimensionality“ is avoided due to four complementary techniques: (1) exploiting...

We model the dynamics of asset prices and associated derivatives by consideration of the dynamics of the conditional probability density process for the value of an asset at some specified time in the future. In the case where the asset is driven by Brownian motion, an associated "master...

In this paper, we consider deterministic (both fluid and discrete) polling systems with N queues with infinite buffers and we show how to compute the best polling sequence (minimizing the average total workload). With two queues, the best polling sequence is always periodic when the system is...