Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of high-dimensional data. However, in many applications such as risk quantification in finance or climatology, one is interested in capturing the tail variations rather than variation around the mean. In...

This paper compares the effectiveness of date- and state-based forward guidance issued by the Federal Reserve since mid-2011 accounting for the influence of disagreement within the FOMC. Effectiveness is investigated through the lens of interest rates' sensitivity to macroeconomic news and I...

How does a negative labor demand shock impact individual-level fertility? I analyze this question in the context of the East German fertility decline after the fall of the Berlin Wall in 1989. Exploiting di erential pressure for restructuring across industries, I nd that throughout the 1990s,...

Counter to the credit channel of monetary transmission, monetary policy tightening induces a rise in lending by two different types of non-bank financial institutions (NBFI): shadow banks and investment funds. A monetary DSGE model is able to replicate the empirical facts when augmented with...

We examine optimal managerial compensation and turnover policy in a principal-agent model in which the firm output is serially correlated over time. The model captures a learning-by-doing feature: higher effort by the manager increases the quality of the match between the firm and the manager in...

We analyze a novel feedback mechanism between market and funding liquidity that causes self-fulfilling liquidity dry-ups. Financial firms facing funding withdrawals have an incentive to acquire information about their assets. Those with good assets gain by resorting to outside liquidity sources...

We analyse a sample of funds and other securities each assigned a total rating score by an unknown expert entity. The scores are based on a number of risk and complexity factors, each assigned a category (factor score) of Low, Medium, or High by the expert entity. A principal component analysis...

In the present paper we study the dynamics of penalization parameter ? of the least absolute shrinkage and selection operator (Lasso) method proposed by Tibshirani (1996) and extended into quantile regression context by Li and Zhu (2008). The dynamic behaviour of the parameter ? can be observed...

We examine collaboration in a one-arm bandit problem in which the players' actions affect the distribution over future payoffs. The players need to exert costly effort both to enhance the value of a risky technology and to learn about its current state. Both product value and learning are public...

It is a challenging task to understand the complex dependency structures in an ultra-high dimensional network, especially when one concentrates on the tail dependency. To tackle this problem, we consider a network quantile autoregres- sion model (NQAR) to characterize the dynamic quantile...