We develop a methodology for estimating time-varying alphas and factor loadings based on nonparametric techniques. We test whether conditional alphas and long-run alphas, which are averages of conditional alphas, are equal to zero and derive test statistics for the constancy of factor loadings. The tests can be performed for a single asset or jointly across portfolios. The traditional Gibbons, ...

We address the problem that occurs when inferences about counterfactuals—predictions, ‘‘what-if’’ questions, and causal effects—are attempted far from the available data. The danger of these extreme counterfactuals is that substantive conclusions drawn from statistical models that fit the data well turn out to be based largely on speculation hidden in convenient modeling assumptions that few wo...

Inter-organizational partnerships are widely praised as a vehicle for planning and implementing complex, comprehensive community interventions. This article explores conceptual, design, and measurement issues relevant to the evaluation of coalitions, with particular reference to anti-crime initiatives. A general theory of partnerships is outlined that goes beyond organizational models to focus ...

We present a method for conditional maximum likelihood estimation of N-gram models used for text or speech utterance classification. The method employs a well known technique relying on a generalization of the Baum-Eagon inequality from polynomials to rational functions. The best performance is achieved for the 1-gram classifier where conditional maximum likelihood training reduces the class er...

We propose a class of specification tests for Autoregressive Conditional Duration (ACD) models that are robust to time-varying conditional dispersion and higher order conditional moments of unknown form. Both linear and nonlinear ACD models are covered. No specific estimation method is required, and the tests have a convenient null asymptotic N(0,1) distribution. To reduce the impact of paramet...