We study semi-parametric estimation and inference in cointegrated panels with endogenous feedback, allowing for general time-series and cross-section dependence and heterogeneity.Central to this literature are the fully-modified OLS of Phillips and Hansen (1990) that use a spectral...

Abstract Staffing decisions typically account for a large portion of a retailer's operational costs. The effectiveness of these decisions has often been analyzed by relating staffing levels to revenues. However, such approach does not explicitly consider the mechanisms by which the staff can...

This paper explores whether powerful politicians' shocks cause changes in corporate R&D investment in the United States. The article employs an original hand-collected data on firms' R&D investment to sort firms into those connected to the powerful congress chairman's representing state and...

We provide a data-driven adjustment for estimated betas that leads to material improvements in the accuracy of weights and risk forecasts for minimum variance portfolios. Like the widely used Blume 2/3 rule and Vasicek correction developed in the 1970s, our beta adjustment operates by shrinking...

Asset pricing models can reinforce asset allocation decisions and promote risk management gains. We compare the out-of-sample performance of mean-variance strategies when mean and covariance are sample estimators of (1) unfiltered excess returns; and (2) filtered excess returns through an asset...

Noting that risk neutral distributions are estimated by minimizing the squared deviations between market and model option prices we consider using option payoff moments in estimating distributional parameters from a sample of observations. It is observed, in particular when compared to maximum...

We present a new measure of extreme credit risk in the time domain, namely the conditional expected time to default (CETD). This measure has a clear interpretation and can be applied in a straightforward way to the analyses of loan performance in time. In contrast to the probability of default,...

We study parameter estimation from the sample X, when the objective is to maximize the expected value of a criterion function, Q, for a distinct sample, Y. This is the situation that arises when a model is estimated for the purpose of describing other data than those used for estimation. The...

The paper proposes a new robust estimator for GARCH-type models: the nonlinear iterative least squares (NL-ILS). This estimator is especially useful on specifications where errors have some degree of dependence over time (weak-GARCH) or when the conditional variance is misspecified. I illustrate...