We develop an algorithm that incorporates network information into regression settings. It simultaneously estimates the covariate coefficients and the signs of the network connections (i.e. whether the connections are of an activating or of a repressing type). For the coefficient estimation...

The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization is combined with a reduction of dimension. In particular it is shown that localization yields...

The use of generalized additive models in statistical data analysis suffers from the restriction to few explanatory variables and the problems of selection of smoothing parameters. Generalized additive model boosting circumvents these problems by means of stagewise fitting of weak learners. A...

Ridge regression is a well established method to shrink regression parameters towards zero, thereby securing existence of estimates. The present paper investigates several approaches to combining ridge regression with boosting techniques. In the direct approach the ridge estimator is used to fit...

Expectations play a crucial role in modern macroeconomic models. We replace the common assumption of rational expectations in a New Keynesian framework by the assumption that expectations are formed according to a heuristics switching model that has performed well in earlier work. We show how...

In a competitive market with taxed transactions, it does not matter under full rationality which side of the market legally transfers the taxes. In the labor market, a tax levied on employers and a corresponding income tax levied on employees are equivalent. With boundedly rational agents, this...