This note observes that instrumental variables techniques are useful not only for estimating causal effects, but that they simultaneously solve the ecological inference problem. ∗Correspondence can be addressed to the author at MEDS Department, Kellogg School of Management, 2001 Sheridan Rd, Evanston, IL 60208, or by email: [email protected]. The ecological inference problem i...

We develop averaged instrumental variables estimators as a way to deal with many weak instruments. We propose a weighted average of the preliminary k-class estimators, where each estimator is obtained using different subsets of the available instrumental variables. The averaged estimators are shown to be consistent and to satisfy asymptotic normality. Furthermore, its approximate mean squared e...

This paper concerns the assessment of direct causal effects from a combination of: (i) non­ experimental data, and (ii) qualitative do­ main knowledge. Domain knowledge is en­ coded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. We provide a generalization of the well-known method of Instrumental Var...

This study examined the determinants of rural household agriculture credit fungibility (CF). The found agricultural CF among farmers, with approximately 79% farmers involved in CF. Household financial burden was to be main cause studied farmers. Most fungible used for clothing and food consumption. A probit model employed analyze survey data collected from four regions Ghana. We an instrumental...

We consider a binary probit model where the latent variable follow a Gaussian Markov Random Field (GMRF). Our main objective is to derive an efficient Gibbs sampler for the above model. For this purpose, we first review two Gibbs samplers available for the classical probit model with one latent variable. We find that the joint update of variables increases the rate of convergence. We use these ...

This document provides supplementary material to our paper “Gender, Competitiveness and Career Choices”. Appendix I presents tables with additional results. This includes descriptive statistics of the control variables (A.I), a comparison of study tracks by gender in the national statistics and in our sample (A.II), the distribution of undergraduate major students by study track at the pre-univ...