Hostname: page-component-7b7567c8c-kwt9d Total loading time: 0 Render date: 2026-11-14T20:44:15.980Z Has data issue: false hasContentIssue false
  • English
  • Français

THE ET INTERVIEW: PROFESSOR JOEL L. HOROWITZ

Part of:ET Interviews

Published online by Cambridge University Press:  13 February 2025

Sokbae LeeOpen the ORCID record for Sokbae Lee[Opens in a new window]
Sokbae Lee*
Affiliation:
Columbia University
*
Sokbae Lee: Department of Economics, Columbia University, New York, USA; email: sl3841@columbia.edu
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Information

Type
ET INTERVIEW
Information
Econometric Theory , Volume 42 , Issue 2 , April 2026, pp. 246 - 293
Copyright
© The Author(s), 2025. Published by Cambridge University Press

References

REFERENCES

Blundell, R., & Horowitz, J. L. (2007). A nonparametric test for exogeneity. Review of Economic Studies , 74, 1035–1058.CrossRefGoogle Scholar
Blundell, R., Horowitz, J. L., & Parey, M. (2012). Measuring the price responsiveness of gasoline demand: Economic shape restrictions and nonparametric estimation. Quantitative Economics , 3, 29–51.CrossRefGoogle Scholar
Blundell, R., Horowitz, J. L., & Parey, M. (2017). Nonparametric estimation of a nonseparable demand function under the Slutsky inequality restriction. Review of Economics and Statistics , 99, 291–304.CrossRefGoogle Scholar
Blundell, R., Horowitz, J. L., & Parey, M. (2022). Estimation of a heterogeneous demand function with Berkson errors. Review of Economics and Statistics , 104, 877–899.CrossRefGoogle Scholar
Bugni, F. A., Hall, P., Horowitz, J. L., & Neumann, G. R. (2009). Goodness-of-fit tests for functional data. The Econometrics Journal , 12, S1–S18.CrossRefGoogle Scholar
Bugni, F. A., & Horowitz, J. L. (2021). Permutation tests for equality of distributions of functional data. Journal of Applied Econometrics , 36, 861–877.CrossRefGoogle Scholar
Forsythe, R., Horowitz, J. L., Savin, N. E., & Sefton, M. (1994). Fairness in simple bargaining experiments. Games and Economic Behavior , 6, 347–369.CrossRefGoogle Scholar
Freyberger, J., & Horowitz, J. L. (2015). Identification and shape restrictions in nonparametric instrumental variables estimation. Journal of Econometrics , 189, 41–53.CrossRefGoogle Scholar
Fu, J.-Y. M., Horowitz, J. L., & Parey, M. (2021). Testing exogeneity in nonparametric instrumental variables models identified by conditional quantile restrictions. The Econometrics Journal , 24, 23–40.CrossRefGoogle Scholar
Hall, P., & Horowitz, J. L. (1990). Bandwidth selection in semiparametric estimation of censored linear regression models. Econometric Theory , 6, 123–150.CrossRefGoogle Scholar
Hall, P., & Horowitz, J. L. (1996). Bootstrap critical values for tests based on generalized-method-of-moments estimators. Econometrica , 64, 891–916.CrossRefGoogle Scholar
Hall, P., & Horowitz, J. L. (2005). Nonparametric methods for inference in the presence of instrumental variables. Annals of Statistics , 33, 2904–2929.CrossRefGoogle Scholar
Hall, P., & Horowitz, J. L. (2007). Methodology and convergence rates for functional linear regression. Annals of Statistics , 35, 70–91.CrossRefGoogle Scholar
Hall, P., & Horowitz, J. L. (2013). A simple bootstrap method for constructing confidence bands for functions. Annals of Statistics , 41, 1892–1914.CrossRefGoogle Scholar
Hall, P., Horowitz, J. L., & Jing, B.-Y. (1995). On blocking rules for the bootstrap with dependent data. Biometrika , 82, 561–574.CrossRefGoogle Scholar
Härdle, W., & Horowitz, J. L. (1994). Testing a parametric model against a semiparametric alternative. Econometric Theory , 10, 821–848.Google Scholar
Härdle, W., & Horowitz, J. L. (1996). Direct semiparametric estimation of a single-index model with discrete covariates. Journal of the American Statistical Association , 91, 1632–1640.Google Scholar
Härdle, W., Horowitz, J. L., & Kreiss, J.-P. (2003). Bootstrap methods for time series. International Statistical Review , 71, 435–459.CrossRefGoogle Scholar
Horowitz, J. L. (1992). A smoothed maximum score estimator for the binary response model. Econometrica , 60, 505–531.CrossRefGoogle Scholar
Horowitz, J. L. (1996). Semiparametric estimation of a regression model with an unknown transformation of the dependent variable. Econometrica , 64, 103–137.CrossRefGoogle Scholar
Horowitz, J. L. (1998a). Semiparametric methods in econometrics . Springer.CrossRefGoogle Scholar
Horowitz, J. L. (1998b). Bootstrap methods for median regression models. Econometrica , 66, 1327–1351.CrossRefGoogle Scholar
Horowitz, J. L. (1999). Semiparametric estimation of a proportional hazard model with unobserved heterogeneity. Econometrica , 67, 1001–1028.CrossRefGoogle Scholar
Horowitz, J. L. (2001). The bootstrap in econometrics. In Heckman, J. J. & Leamer, E. E. (Eds.), Handbook of econometrics (Chapter 52, Vol. 5, pp. 3159–3228). Elsevier.Google Scholar
Horowitz, J. L. (2002). Bootstrap critical values for tests based on the smoothed maximum score estimator. Journal of Econometrics , 111, 141–167.CrossRefGoogle Scholar
Horowitz, J. L. (2009). Semiparametric and nonparametric methods in econometrics . Springer.CrossRefGoogle Scholar
Horowitz, J. L. (2019). Bootstrap methods in econometrics. Annual Review of Economics , 11, 193–204.CrossRefGoogle Scholar
Horowitz, J. L., & Huang, J. (2013). Penalized estimation of high-dimensional models under a generalized sparsity condition. Statistica Sinica , 23, 725–748.Google Scholar
Horowitz, J. L., & Krishnamurthy, A. (2018). A bootstrap method for construction of pointwise and uniform confidence bands for conditional quantile functions. Statistica Sinica , 28, 2609–2632.Google Scholar
Horowitz, J. L., & Mammen, E. (2004). Nonparametric estimation of an additive model with a link function. Annals of Statistics , 32, 2412–2443.CrossRefGoogle Scholar
Horowitz, J. L., & Mammen, E. (2007). Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions. Annals of Statistics , 35, 2589–2619.CrossRefGoogle Scholar
Horowitz, J. L., & Mammen, E. (2011). Oracle-efficient nonparametric estimation of an additive model with an unknown link function. Econometric Theory , 27, 582–608.CrossRefGoogle Scholar
Horowitz, J. L., Mammen, E., & Klemelä, J. (2006). Optimal estimation in additive regression models. Bernoulli , 12, 271–298.CrossRefGoogle Scholar
Horowitz, J. L., & Manski, C. F. (1995). Identification and robustness with contaminated and corrupted data. Econometrica , 63, 281–302.CrossRefGoogle Scholar
Horowitz, J. L., & Nesheim, L. (2021). Using penalized likelihood to select parameters in a random coefficients multinomial logit model. Journal of Econometrics , 222, 1057–1082.CrossRefGoogle Scholar
Horowitz, J. L., & Neumann, G. R. (1987). Semiparametric estimation of employment duration models. Econometric Reviews , 6, 5–40.CrossRefGoogle Scholar
Horowitz, J. L., & Neumann, G. R. (1992). A generalized moments specification test of the proportional hazards model. Journal of the American Statistical Association , 87, 234–240.CrossRefGoogle Scholar
Horowitz, J. L., & Spokoiny, V. (2001). An adaptive, rate-optimal test of a parametric mean-regression model against a nonparametric alternative. Econometrica , 69, 599–631.CrossRefGoogle Scholar
Horowitz, J. L., & Spokoiny, V. (2002). An adaptive, rate-optimal test of linearity for median regression models. Journal of the American Statistical Association , 97, 822–835.CrossRefGoogle Scholar
Huang, J., Horowitz, J. L., & Ma, S. (2008). Asymptotic properties of bridge estimators in sparse, high-dimensional regression models. Annals of Statistics , 36, 587–613.CrossRefGoogle Scholar
Huang, J., Horowitz, J. L., & Wei, F. (2010). Variable selection in nonparametric additive models. Annals of Statistics , 38, 2282–2231.CrossRefGoogle ScholarPubMed
Shen, G., Jiao, Y., Lin, Y., Horowitz, J. L., & Huang, J. (2024). Nonparametric estimation of non-crossing quantile regression process with deep ReQU neural networks. Journal of Machine Learning Research , 25(88), 1–75.Google Scholar