Department of Economics
University of Western Ontario
Social Science Centre, Room 4071
London, Ontario, Canada, N6A 5C2
Tel: 519 661-3500
Fax: 519 661-3666
Information about this author at RePEc
NBER Working Papers and Publications
|September 2015||Model Uncertainty and the Effect of Shall-Issue Right-to-Carry Laws on Crime|
with Steven N. Durlauf, David A. Rivers: w21566
This paper explores the role of model uncertainty in explaining the different findings in the literature regarding the effect of shall-issue right-to-carry concealed weapons laws on crime. In particular, we systematically examine how different modeling assumptions affect the results. We find little support for some widely used assumptions in the literature (e.g., population weights), but find that allowing for the effect of the law to be heterogeneous across both counties and over time is important for explaining the observed patterns of crime. In terms of model uncertainty, we find that there is substantial variation in the estimated effects for each model across all dimensions of the model space. This suggests that one should be cautious in using the results from any particular model to ...
Published: Steven N. Durlauf & Salvador Navarro & David A. Rivers, 2016. "Model uncertainty and the effect of shall-issue right-to-carry laws on crime," European Economic Review, vol 81(), pages 32-67. citation courtesy of
|July 2007||The Identification and Economic Content of Ordered Choice Models with Stochastic Thresholds|
with Flavio Cunha, James J. Heckman: t0340
This paper extends the widely used ordered choice model by introducing stochastic thresholds and interval-specific outcomes. The model can be interpreted as a generalization of the GAFT (MPH) framework for discrete duration data that jointly models durations and outcomes associated with different stopping times. We establish conditions for nonparametric identification. We interpret the ordered choice model as a special case of a general discrete choice model and as a special case of a dynamic discrete choice model.
Published: THE IDENTIFICATION AND ECONOMIC CONTENT OF ORDERED CHOICE MODELS WITH STOCHASTIC THRESHOLDS† Flavio Cunha, James J. Heckman, Salvador Navarro‡ Article first published online: 11 DEC 2007 DOI: 10.1111/j.1468-2354.2007.00462.x International Economic Review Volume 48, Issue 4, pages 1273–1309, November 2007
|October 2005||Dynamic Discrete Choice and Dynamic Treatment Effects|
with James J. Heckman: t0316
This paper considers semiparametric identification of structural dynamic discrete choice models and models for dynamic treatment effects. Time to treatment and counterfactual outcomes associated with treatment times are jointly analyzed. We examine the implicit assumptions of the dynamic treatment model using the structural model as a benchmark. For the structural model we show the gains from using cross equation restrictions connecting choices to associated measurements and outcomes. In the dynamic discrete choice model, we identify both subjective and objective outcomes, distinguishing ex post and ex ante outcomes. We show how to identify agent information sets.
|January 2005||Separating Uncertainty from Heterogeneity in Life Cycle Earnings|
with Flavio Cunha, James J. Heckman: w11024
This paper develops and applies a method for decomposing cross section variability of earnings into components that are forecastable at the time students decide to go to college (heterogeneity) and components that are unforecastable. About 60% of variability in returns to schooling is forecastable. This has important implications for using measured variability to price risk and predict college attendance.
Published: Cunha, Flavio, James Heckman and Salvador Navarro. "Separating Uncertainty From Heterogeneity In Life Cycle Earnings," Oxford Economic Papers, 2005, v57(2,Apr), 191-261. citation courtesy of