Review of American Generosity: Who Gives and Why by Patricia Snell Herzog and Heather E. Price

Excerpt: In American Generosity, sociologists Patricia Herzog and Heather Price provide comprehensive, detailed, and realistic portraits of generosity among American adults. The research in this book was conducted as part of the University of Notre Dame’s Science of Generosity Initiative. In an earlier book, Christian Smith and Hilary Davidson described the paradox of generosity in which few Americans generously give despite the many benefits that generosity brings back to the giver. American Generosity sheds some light on why this paradox exists, asking: Who gives, who does not give, and why do some people give more than others

Higher Order Bias Correcting Moment Equation for M-Estimation and its Higher Order Efficiency

This paper studies an alternative bias correction for the M-estimator, which is obtained by correcting the moment equation in the spirit of Firth (1993). In particular, this paper compares the stochastic expansions of the analytically bias-corrected estimator and the alternative estimator and finds that the third-order stochastic expansions of these two estimators are identical. This implies that at least in terms of the third order stochastic expansion, we cannot improve on the simple one-step bias correction by using the bias correction of moment equations. Though the result in this paper is for a fixed number of parameters, our intuition may extend to the analytical bias correction of the panel data models with individual specific effects. Noting the M-estimation can nest many kinds of estimators including IV, 2SLS, MLE, GMM, and GEL, our finding is a rather strong result.Third-order Stochastic Expansion, Bias Correction, M-estimation

Uniform Convergence Rate of the SNP Density Estimator and Testing for Similarity of Two Unknown Densities

This paper studies the uniform convergence rate of the turncated SNP (semi-nonparametric) density estimator. Using the uniform convergence rate result we obtain, we propose a test statistic testing the equivalence of two unknown densities where two densities are estimated using the SNP estimator and supports of densities are possibly unbounded.SNP Density Estimator, Uniform Convergence Rate, Comparison of Two Densities

Uniform Convergence Rate of the SNP Density Estimator and Testing for Similarity of Two Unknown Densities

This paper studies the uniform convergence rate of the turncated SNP (semi-nonparametric) density estimator. Using the uniform convergence rate result we obtain, we propose a test statistic testing the equivalence of two unknown densities where two densities are estimated using the SNP estimator and supports of densities are possibly unbounded.SNP Density Estimator, Uniform Convergence Rate, Comparison of Two Densities

Semiparametric Estimation of Signaling Games

This paper studies an econometric modeling of a signaling game with two players where one player has one of two types. In particular, we develop an estimation strategy that identifies the payoffs structure and the distribution of types from data of observed actions. We can achieve uniqueness of equilibrium using a refinement, which enables us to identify the parameters of interest. In the game, we consider non-strategic public signals about the types. Because the mixing distribution of these signals is nonparametrically specified, we propose to estimate the model using a sieve conditional MLE. We achieve the consistency and the asymptotic normality of the structural parameters estimates. As an alternative, we allow for the possibility of multiple equilibria, without using an equilibrium selection rule. As a consequence, we adopt a set inference allowing for multiplicity of equilibria.Semiparametric Estimation, Signaling Game, Set Inference, Infinite Dimensional Parame- ters, Sieve Simultaneous Conditional MLE

Higher Order Bias Correcting Moment Equation for M-Estimation and its Higher Order Efficiency

This paper studies an alternative bias correction for the M-estimator, which is obtained by correcting the moment equation in the spirit of Firth (1993). In particular, this paper compares the stochastic expansions of the analytically bias-corrected estimator and the alternative estimator and finds that the third-order stochastic expansions of these two estimators are identical. This implies that at least in terms of the third order stochastic expansion, we cannot improve on the simple one-step bias correction by using the bias correction of moment equations. Though the result in this paper is for a .xed number of parameters, our intuition may extend to the analytical bias correction of the panel data models with individual speci.c eects. Noting the M-estimation can nest many kinds of estimators including IV, 2SLS, MLE, GMM, and GEL, our .nding is a rather strong result.Third-order Stochastic Expansion, Bias Correction, M-estimation