What are the statistical and computational problems associated with robust nonlinear regression? This paper presents a … number of possible approaches to these problems and develops a particular algorithm based on the work of Powell and Dennis …

) Improved Stochastic Ranking Evolution Strategy (ISRES), (iii) Multi-Level Single-Linkage (MLSL) algorithm, (iv) Stochastic … versions of TikTak, which is a multistart global optimization algorithm used in some recent economic applications. For … completeness, we add three popular local algorithms to the comparison—the Nelder-Mead downhill simplex algorithm, the Derivative …

We examine the higher order properties of the wild bootstrap (Wu, 1986) in a linear regression model with stochastic …

regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular … threshold in time. In this guide for practitioners, we discuss several features of this “Regression Discontinuity in Time” (RDiT … framework closer to an event study than a regression discontinuity design. Based on these features and motivated by hypothetical …

regression of Y onto X in a subpopulation homogenous in W = w (formally a conditional linear predictor). Let b₀ (w) be … the coefficient vector on X in this regression. We introduce a semiparametrically efficient estimate of the average β … methods of semiparametric regression (e.g., the partially linear regression model) …

. Extensions of the technique to the regression case are also considered and some computational points are briefly mentioned …

This paper attempts to provide the user of linear multiple regression with a battery of diagnostic tools to determine … decompositions of basic regression statistics. There is also a discussion of how these techniques can be used with robust and ridge …

This paper develops asymptotic distribution theory for instrumental variable regression when the partial correlation …