Bounds for the probability distribution function of the linear ACD process

This paper derives both lower and upper bounds for the probability distribution function of stationary ACD(p, q) processes. For the purpose of illustration, I specialize the results to the main parent distributions in duration analysis. Simulations show that the lower bound is much tighter than the upper bound.

Inequality treatment effects

This paper presents semiparametric estimators for treatment effects parameters when selection to treatment is based on observable characteristics. The parameters of interest in this paper are those that capture summarized distributional effects of the treatment. In particular, the focus is on the impact of the treatment calculated by differences in inequality measures of the potential outcomes of receiving and not receiving the treatment. These differences are called here inequality treatment effects. The estimation procedure involves a first non-parametric step in which the probability of receiving treatment given covariates, the propensity-score, is estimated. Using the reweighting method to estimate parameters of the marginal distribution of potential outcomes, in the second step weighted sample versions of inequality measures are.computed. Calculations of semiparametric effciency bounds for inequality treatment effects parameters are presented. Root-N consistency, asymptotic normality, and the achievement of the semiparametric efficiency bound are shown for the semiparametric estimators proposed. A Monte Carlo exercise is performed to investigate the behavior in finite samples of the estimator derived in the paper.

Integral representation with convex capacities that are squeeze of (additive) probability measures

In this paper I will investigate the conditions under which a convex capacity (or a non-additive probability which exhibts uncertainty aversion) can be represented as a squeeze of a(n) (additive) probability measure associate to an uncertainty aversion function. Then I will present two alternatives forrnulations of the Choquet integral (and I will extend these forrnulations to the Choquet expected utility) in a parametric approach that will enable me to do comparative static exercises over the uncertainty aversion function in an easy way.

Knowledge disclosure as a weapon in technological change battles: straight shot or backfire?

This work aims at evaluating how effective is knowledge disclosure in attenuating institutional negative reactions caused by uncertainties brought by firms’ new strategies that respond to novel technologies. The empirical setting is from an era of technological ferment, the period of the introduction of the voice over internet protocol (VoIP) in the USA in the early 2000’s. This technology led to the convergence of the wireline telecommu- nications and cable television industries. The Institutional Brokers’ Estimate System (also known as the I/B/E/S system) was used to capture reactions of securities analysts, a revealed important source of institutional pressure on firms’ strategies. For assessing knowledge disclosure, a coding technique and a established content analysis framework were used to quantitatively measure the non-numerical and unstructured data of transcripts of business events occurred at that time. Eventually, several binary response models were tested in order to assess the effect of knowledge disclosure on the probability of institutional positive reactions. The findings are that the odds of favorable institutional reactions increase when a specific kind of knowledge is disclosed. It can be concluded that knowledge disclosure can be considered as a weapon in technological changes situations, attenuating adverse institutional reactions to the companies’ strategies in environments of technological changes.