Björn Bartling, Ernst Fehr, Holger Herz. The Intrinsic Value of Decision Rights

Philosophers, psychologists, and economists have long argued that certain decision rights carry not only instrumental value but may also be valuable for their own sake. The ideas of autonomy, freedom, and liberty derive their intuitive appeal – at least partly – from an assumed positive intrinsic value of decision rights. Proving the existence of this value and measuring its size, however, is intricate. Here, we develop an experimental method capable of achieving these goals. The data reveal t...

Read more

Dennis Dittrich, Werner Güth, Boris Maciejovsky. Overconfidence in Investment Decisions: An Experimental Approach

The evidence on decision biases and heuristics challenges the prescriptive validity of standard finance theory which is still the dominant paradigm in modern finance. One such violation is the overcon fidence bias in the sense of systematically overestimating the accuracy of one's decisions and the precision of one's knowledge. Overconfidence has been observed in many professionals (for a survey see Yates (1990)). Most relevant for our study, overconfidence was found in entrepreneurs (Cooper e...

Read more

Jess Benhabib, Pengfei Wang, Yi Wen. Uncertainty and Sentiment-Driven Equilibria

We construct a model to capture the Keynesian idea that production and employment decisions are based on expectations of aggregate demand driven by sentiments, and that realized demand follows from the production and employment decisions of firms. We cast the Keynesian idea into a simple model with imperfect information about aggregate demand and we characterize the rational expectations equilibria of this model. We find that the equilibrium is not unique despite the absence of any non-convexi...

Read more

Olivier Ledoit, Michael Wolf. Spectrum Estimation: A Unified Framework for Covariance Matrix Estimation and PCA in Large Dimensions

Covariance matrix estimation and principal component analysis (PCA) are two cornerstones of multivariate analysis. Classic textbook solutions perform poorly when the dimension of the data is of a magnitude similar to the sample size, or even larger. In such settings, there is a common remedy for both statistical problems: nonlinear shrinkage of the eigenvalues of the sample covariance matrix. The optimal nonlinear shrinkage formula depends on unknown population quantities and is thus not avail...

Read more

Go to top