Suchergebnisse

1 Introduction -- 1.1 Specification and misspecification of the econometric model -- 1.2 The purpose and scope of this study -- 2 Preliminary Mathematics -- 2.1 Random variables, independence, Borel measurable functions and mathematical expectation -- 2.2 Convergence of random variables and distributions -- 2.3 Uniform convergence of random functions -- 2.4 Characteristic functions, stable distributions and a central limit theorem -- 2.5 Unimodal distributions -- 3 Nonlinear Regression Models -- 3.1 Nonlinear least-squares estimation -- 3.2 A class of nonlinear robust M-estimators -- 3.3 Weighted nonlinear robust M-estimation -- 3.4 Miscellaneous notes on robust M-estimation -- 4 Nonlinear Structural Equations -- 4.1 Nonlinear two-stage least squares -- 4.2 Minimum information estimators: introduction -- 4.3 Minimum information estimators: instrumental variable and scaling parameter -- 4.4 Miscellaneous notes on minimum information estimation -- 5 Nonlinear Models with Lagged Dependent Variables -- 5.1 Stochastic stability -- 5.2 Limit theorem for stochastically stable processes -- 5.3 Dynamic nonlinear regression models and implicit structural equations -- 5.4 Remarks on the stochastic stability concept -- 6 Some Applications -- 6.1 Applications of robust M-estimation -- 6.2 An application of minimum information estimation -- References.

AbstractWe consider a linear regression model where some explanatory variables are unknown members of sets of alternative explanatory variables. It will be shown that under weak conditions the minimum residual variance criterion for selecting these explanatory variables has the property that the probability of selecting wrong explanatory variables vanishes if the number of observations increases to infmity. Moreover, the O.L.S. estimator of the resulting "specified" model turns out to be consistent, while in the case that all the parameters are nonzero it can be shown that this O.L.S. estimator has the same limiting distribution as the O.L.S. estimator of the true model.

Abstract
The objective of this paper is to re-evaluate the effect of the 1985 "Employment Services for Ex-Offenders" (ESEO) program on recidivism in San Diego, Chicago and Boston. The initial group of program participants was split randomly in a control group and a treatment group. The actual treatment (mainly being job related counseling) only takes place conditional on finding a job and not having been arrested for those selected in the treatment group. We use interval-censored proportional hazard models for job search and recidivism time, where the latter model incorporates the conditional treatment effect, depending on covariates. We find that the effect of the program depends on location and age. The ESEO program reduces the risk of recidivism only for ex-inmates over the age of 27 in San Diego and Chicago and over the age of 36 in Boston, but increases the risk of recidivism for the other ex-inmates in the treatment group.