experienced approximately 50 percent sample loss from cumulative attrition from its initial 1968 membership. We study the effect of this attrition on the unconditional distributions of several socioeconomic variables and on the estimates of several sets of regression coefficients. We provide a statistical framework for conducting tests for attrition bias that draws a sharp distinction between selection on unobservables and on observables and that shows that weighted least squares can generate consistent parameter estimates when selection is based on observables, even when they are endogenous. Our empirical analysis shows that attrition is highly selective and is concentrated among lower socioeconomic status individuals. We also show that attrition is concentrated among those with more unstable earnings, marriage, and migration histories. Nevertheless, we find that these variables explain very little of the attrition in the sample, and that the selection that occurs is moderated by regression-to-the-mean effects

Sociological investigations of economic exchange reveal how institutions and social structures shape transaction patterns among economic actors. This article explores how interfirm networks in the U.S. venture capital (VC) market affect spatial patterns of exchange. Evidence suggests that information about potential investment opportunities generally circulates within geographic and industry spaces. In turn, the circumscribed flow of information within these spaces contributes to the geographic- and industry-localization of VC investments. Empirical analyses demonstrate that the social networks in the VC community—built up through the industry’s extensive use of syndicated investing—diffuse information across boundaries and therefore expand the spatial radius of exchange. Venture capitalists that build axial positions in the industry’s coinvestment network invest more frequently in spatially distant companies. Thus, variation in actors ’ positioning within the structure of the market appears to differentiate market participants ’ ability to overcome boundaries that otherwise would curtail exchange.

We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99 % of their (nonfixed) data collection costs or to collect much more meaningful explanatory

ome detail more recent developments in the economic theory of choice, and modifications to this theory that are being forced by experimental evidence from cognitive psychology. I will close with a survey of statistical methods that have developed as part of the research program on economic choice behavior. Science is a cooperative enterprise, and my work on choice behavior reflects not only my own ideas, but the results of exchange and collaboration with many other scholars. 1 First, of course, is my co-laureate James Heckman, who among his many contributions pioneered the important area of dynamic discrete choice analysis. Nine other individuals who played a major role in channeling microeconometrics and choice theory toward their modern forms, and had a particularly important influence on my own work, are Zvi Griliches, L.L. Thurstone, Jacob Marschak, Duncan Luce, Danny Kahneman, Amos Tversky, Moshe Ben-Akiva, Charles Manski, and Kenneth Train. A gallery of their p

This paper considers regression models for cross-section data that exhibit crosssection dependence due to common shocks, such as macroeconomic shocks. The paper analyzes the properties of least squares (LS) estimators in this context. The results of the paper allow for any form of cross-section dependence and heterogeneity across population units. The probability limits of the LS estimators are determined, and necessary and sufficient conditions are given for consistency. The asymptotic distributions of the estimators are found to be mixed normal after recentering and scaling. The t� Wald, and F statistics are found to have asymptotic standard normal, χ2,andscaledχ2 distributions, respectively, under the null hypothesis when the conditions required for consistency of the parameter under test hold. However, the absolute values of t, Wald, and F statistics are found to diverge to infinity under the null hypothesis when these conditions fail. Confidence intervals exhibit similarly dichotomous behavior. Hence, common shocks are found to be innocuous in some circumstances, but quite problematic in others. Models with factor structures for errors and regressors are considered. Using the general results, conditions are determined under which consistency of the LS estimators holds and fails in models with factor structures. The results are extended to cover heterogeneous and functional factor structures in which common factors have different impacts on different population units.

TO PREDICT CHURN In this paper, bagging and boosting techniques are proposed as performing tools for churn prediction. These methods consist of sequentially applying a classification algorithm to resampled or reweigthed versions of the data set. We apply these algorithms on a customer database of an anonymous U.S. wireless telecom company. Bagging is easy to put in practice and, as well as boosting, leads to a significant increase of the classification performance when applied to the customer database. Furthermore, we compare bagged and boosted classifiers computed, respectively, from a balanced versus a proportional sample to predict a rare event (here, churn), and propose a simple correction method for classifiers constructed from balanced training samples.

Abstract: This paper addresses three questions: (i) Are multinational firms (MNCs) really better than markets at transferring knowledge across borders? (ii) How actively do MNCs exchange knowledge with their host countries? (iii) Do they contribute as much to local knowledge as they learn from their host countries? To answer these questions, I analyze data on citations for over half a million patents from 4,400 firms and organizations from six countries, covering all manufacturing sectors. I estimate the probability of individual knowledge flows, as measured using patent citations, through a weighted maximum likelihood estimation approach for choice-based samples. Cross-border knowledge flows within the same MNC are found to be several times stronger than those between different entities even within the same country. Interestingly, these intra-MNC flows are equally strong in both directions between the home base and the foreign subsidiaries. Turning to intra-national knowledge flows, foreign MNC subsidiaries learn more from domestic entities than they contribute to host country knowledge, though this pattern differs across countries and industries. Knowledge flows from host countries to MNCs are in fact as strong as those between domestic entities, showing that MNC subsidiaries are not disadvantaged by their foreign affiliation. Finally, parent firms of MNCs with a higher fraction of innovative activity located abroad also learn more from other countries, suggesting

Abstract not found

Both authors contributed equally to this work. The University of Chicago

Congressional Budget Office working papers in this series are preliminary and are circulated to stimulate discussion and critical comment. Those papers are not subject to Congressional Budget Office’s formal review and editing processes. The analysis and conclusions expressed in them are those of the authors and should not be interpreted as those of the Congressional Budget Office. References in publications should be cleared with the authors. Papers in the