Tiemen M Woutersen
 Professor, Economics
 Associate Professor, StatisticsGIDP
Contact
 (520) 6216224
 McClelland Hall, Rm. 401
 Tucson, AZ 85721
 woutersen@email.arizona.edu
Biography
Personal Details
Year of Birth: 1973
Citizenship: Dutch, US permanent resident
Degrees
 M.A. Economics
 Brown University, Providence, Rhode Island, United States
 Ph.D. Economics
 Brown University, Providence, Rhode Island, United States
 Essays on the Integrated Hazard and Orthogonality Concepts
Work Experience
 University of Arizona, Tucson, Arizona (2013  Ongoing)
Awards
 Eller College research award
 Eller College of Management, Summer 2017
 University of Arizona Eller Dean’s Fellowship
 Summer 2017
 University of Arizona Eller Fellow
 University of Arizona, Spring 2016
 University of Arizona, Spring 2013
 Eller Fellowship
 Karl Eller via the University of Arizona Foundation, Spring 2014
 University of Arizona Gianelli Fellowship
 University of Arizona, Spring 2014
Interests
Research
Econometrics; identification of IO models, development, unemployment duration
Teaching
Econometrics; Financial Econometrics; Applied Econometrics; Empirical research
Courses
201819 Courses

Econometric Modeling I
ECON 696E (Spring 2019) 
Econometrics
ECON 522A (Spring 2019) 
Econometrics
ECON 522B (Fall 2018)
201718 Courses

Econometrics
ECON 522A (Spring 2018)
201617 Courses

Dissertation
ECON 920 (Spring 2017) 
Econometrics
ECON 522A (Spring 2017) 
Dissertation
ECON 920 (Fall 2016) 
Econometric Modeling I
ECON 696E (Fall 2016) 
Econometrics
ECON 522B (Fall 2016)
Scholarly Contributions
Journals/Publications
 Woutersen, T. M., Hu, Y., & Shiu, J. (2016). Identification in Nonseparable Models with Measurement Error and Endogeneity. Econometic Letters.
 Hu, Y., Shiu, J., & Woutersen, T. (2015). Identification and Estimation of Single Index Models with Measurement Error and Endogeneity. Econometrics Journal.
 Woutersen, T. M., Hu, Y., & Shiu, J. (2015). “Identification and Estimation of Single Index Models with Measurement Error and Endogeneity”. Econometrics Journal.
 Chao, J. C., Hausman, J. A., Newey, W. K., Swanson, N. R., & Woutersen, T. (2014). Testing overidentifying restrictions with many instruments and heteroskedasticity. Journal of Econometrics, 178(PART 1), 1521.More infoAbstract: This paper gives a test of overidentifying restrictions that is robust to many instruments and heteroskedasticity. It is based on a jackknife version of the overidentifying test statistic. Correct asymptotic critical values are derived for this statistic when the number of instruments grows large, at a rate up to the sample size. It is also shown that the test is valid when the number of instruments is fixed and there is homoskedasticity. This test improves on recently proposed tests by allowing for heteroskedasticity and by avoiding assumptions on the instrument projection matrix. This paper finds in Monte Carlo studies that the test is more accurate and less sensitive to the number of instruments than the HausmanSargan or GMM tests of overidentifying restrictions.
 Hausman, J. A., & Woutersen, T. (2014). Estimating a semiparametric duration model without specifying heterogeneity. Journal of Econometrics, 178(PART 1), 114131.More infoAbstract: This paper presents a new estimator for the mixed proportional hazard model that allows for a nonparametric baseline hazard and timevarying regressors. In particular, this paper allows for discrete measurement of the durations as happens often in practice. The integrated baseline hazard and all parameters are estimated at the regular rate, N, where N is the number of individuals. A hazard model is a natural framework for timevarying regressors. In particular, if a flow or a transition probability depends on a regressor that changes with time, a hazard model avoids the curse of dimensionality that would arise from interacting the regressors at each point in time with one another. This paper also presents a new test to detect unobserved heterogeneity.
 Hausman, J., & Woutersen, T. (2014). Estimating the Derivative Function and Counterfactuals in Duration Models with Heterogeneity. Econometric Reviews, 33(56), 472496.More infoAbstract: This paper presents a new estimator for counterfactuals in duration models. The counterfactual in a duration model is the length of the spell in case the regressor would have been different. We introduce the structural duration function, which gives these counterfactuals. The advantage of focusing on counterfactuals is that one does not need to identify the mixed proportional hazard model. In particular, we present examples in which the mixed proportional hazard model is unidentified or has a singular information matrix but our estimator for counterfactuals still converges at rate N 1/2, where N is the number of observations. We apply the structural duration function to simulate important policy effects, including a change in welfare benefits. © 2014 Copyright Taylor and Francis Group, LLC.
 Woutersen, T. M., Bijwaard, G., & Ridder, G. (2013). A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model. Journal of Econometric Methods.
 Chao, J. C., Hausman, J. A., Newey, W. K., Swanson, N. R., & Woutersen, T. (2012). An expository note on the existence of moments of fuller and HFUL estimators. Advances in Econometrics, 29, 87106.More infoAbstract: In a recent paper, Hausman, Newey, Woutersen, Chao, and Swanson (2012) propose a new estimator, HFUL (Heteroscedasticity robust Fuller), for the linear model with endogeneity. This estimator is consistent and asymptotically normally distributed in the many instruments and many weak instruments asymptotics. Moreover, this estimator has moments, just like the estimator by Fuller (1977). The purpose of this note is to discuss at greater length the existence of moments result given in Hausman et al. (2012). In particular, we intend to answer the following questions: Why does LIML not have moments? Why does the Fuller modification lead to estimators with moments? Is normality required for the Fuller estimator to have moments? Why do we need a condition such as Hausman et al. (2012), Assumption 9? Why do we have the adjustment formula? Copyright © 2012 by Emerald Group Publishing Limited.
 Chao, J. C., Hausman, J. A., Newey, W. K., Swanson, N. R., & Woutersen, T. (2012). Combining two consistent estimators. Advances in Econometrics, 29, 3353.More infoAbstract: This chapter shows how a weighted average of a forward and reverse Jackknife IV estimator (JIVE) yields estimators that are robust against heteroscedasticity and many instruments. These estimators, called HFUL (Heteroscedasticity robust Fuller) and HLIM (Heteroskedasticity robust limited information maximum likelihood (LIML)) were introduced by Hausman, Newey, Woutersen, Chao, and Swanson (2012), but without derivation. Combining consistent estimators is a theme that is associated with Jerry Hausman and, therefore, we present this derivation in this volume. Additionally, and in order to further understand and interpret HFUL and HLIM in the context of jackknife type variance ratio estimators, we show that a new variant of HLIM, under specific grouped data settings with dummy instruments, simplifies to the Bekker and van der Ploeg (2005) MM (method of moments) estimator. Copyright © 2012 by Emerald Group Publishing Limited.
 Chao, J. C., Swanson, N. R., Hausman, J. A., Newey, W. K., & Woutersen, T. (2012). Asymptotic distribution of JIVE in a heteroskedastic IV regression with many instruments. Econometric Theory, 28(1), 4286.More infoAbstract: This paper derives the limiting distributions of alternative jackknife instrumental variables (JIV) estimators and gives formulas for accompanying consistent standard errors in the presence of heteroskedasticity and many instruments. The asymptotic framework includes the many instrument sequence of Bekker (1994, Econometrica 62, 657681) and the many weak instrument sequence of Chao and Swanson (2005, Econometrica 73, 16731691). We show that JIV estimators are asymptotically normal and that standard errors are consistent provided that √K n/r n → 0 as n → ∞, where K n and r n denote, respectively, the number of instruments and the concentration parameter. This is in contrast to the asymptotic behavior of such classical instrumental variables estimators as limited information maximum likelihood, biascorrected twostage least squares, and twostage least squares, all of which are inconsistent in the presence of heteroskedasticity, unless K n/r n → 0. We also show that the rate of convergence and the form of the asymptotic covariance matrix of the JIV estimators will in general depend on the strength of the instruments as measured by the relative orders of magnitude of r n and K n. © Copyright Cambridge University Press 2011.
 Hausman, J. A., Newey, W. K., Woutersen, T., Chao, J. C., & Swanson, N. R. (2012). Instrumental variable estimation with heteroskedasticity and many instruments. Quantitative Economics, 3(2), 211255.More infoAbstract: This paper gives a relatively simple, well behaved solution to the problem of many instruments in heteroskedastic data. Such settings are common in microeconometric applications where many instruments are used to improve efficiency and allowance for heteroskedasticity is generally important. The solution is a Fuller (1977) like estimator and standard errors that are robust to heteroskedasticity and many instruments. We show that the estimator has finite moments and high asymptotic efficiency in a range of cases. The standard errors are easy to compute, being like White's (1982), with additional terms that account for many instruments. They are consistent under standard, many instrument, and many weak instrument asymptotics. We find that the estimator is asymptotically as efficient as the limitedinformation maximum likelihood (LIML) estimator under many weak instruments. In Monte Carlo experiments, we find that the estimator performs as well as LIML or Fuller (1977) under homoskedasticity, and has much lower bias and dispersion under heteroskedasticity, in nearly all cases considered. © 2012 Jerry A. Hausman, Whitney K. Newey, Tiemen Woutersen, John C. Chao, and Norman R. Swanson.
 Woutersen, T. M., Chao, J. C., Hausman, J. A., Newey, W. K., & Swanson, N. R. (2012). An Expository Note on the Existence of Moments of Fuller and HFUL Estimators. Advances in Econometrics: Essays in Honor of Jerry Hausman.
 Woutersen, T. M., Hausman, J. A., Newey, W. K., Chao, J. C., & Swanson, N. R. (2012). An Expository Note on the Existence of Moments of Fuller and HFUL Estimators. Quantitative Economics.
 M., R., & Woutersen, T. (2011). DYNAMIC TIME SERIES BINARY CHOICE. Econometric Theory, 130.More infoAbstract: This paper considers dynamic time series binary choice models. It proves near epoch dependence and strong mixing for the dynamic binary choice model with correlated errors. Using this result, it shows in a time series setting the validity of the dynamic probit likelihood procedure when lags of the dependent binary variable are used as regressors, and it establishes the asymptotic validity of Horowitz's smoothed maximum score estimation of dynamic binary choice models with lags of the dependent variable as regressors. For the semiparametric model, the latent error is explicitly allowed to be correlated. It turns out that no longrun variance estimator is needed for the validity of the smoothed maximum score procedure in the dynamic time series framework.
 Woutersen, T. (2011). Consistent estimation and orthogonality. Advances in Econometrics, 27 A, 155178.More infoAbstract: Observations in a dataset are rarely missing at random. One can control for this nonrandom selection of the data by introducing fixed effects or other nuisance parameters. This chapter deals with consistent estimation the presence of many nuisance parameters. It derives a new orthogonality concept that gives sufficient conditions for consistent estimation of the parameters of interest. It also shows how this orthogonality concept can be used to derive and compare estimators. The chapter then shows how to use the orthogonality concept to derive estimators for unbalanced panels and incomplete data sets (missing data). Copyright © 2011 by Emerald Group Publishing Limited.
 Ridder, G., & Woutersen, T. M. (2003). The singularity of the information matrix of the mixed proportional hazard model. Econometrica, 71(5), 15791589.More infoAbstract: This paper presents new identification conditions for the mixed proportional hazard model. In particular, the baseline hazard is assumed to be bounded away from 0 and ∞ near t = 0. These conditions ensure that the information matrix is nonsingular. The paper also presents an estimator for the mixed proportional hazard model that converges at rate N1/2.
 Woutersen, T. (2003). BAYESIAN ANALYSIS OF MISSPECIFIED MODELS WITH FIXED EFFECTS. Advances in Econometrics, 17, 235249.More infoAbstract: One way to control for the heterogeneity in panel data is to allow for timeinvariant, individual specific parameters. This fixed effect approach introduces many parameters into the model which causes the "incidental parameter problem": the maximum likelihood estimator is in general inconsistent. Woutersen (2001) shows how to approximately separate the parameters of interest from the fixed effects using a reparametrization. He then shows how a Bayesian method gives a general solution to the incidental parameter for correctly specified models. This paper extends Woutersen (2001) to misspecified models. Following White (1982), we assume that the expectation of the score of the integrated likelihood is zero at the true values of the parameters. We then derive the conditions under which a Bayesian estimator converges at rate N where N is the number of individuals. Under these conditions, we show that the variancecovariance matrix of the Bayesian estimator has the form of White (1982). We illustrate our approach by the dynamic linear model with fixed effects and a duration model with fixed effects. © 2003.
Presentations
 Buckman, J. R., Hashim, M. J., Woutersen, T. M., & Bockstedt, J. C. (2017, June). Fool Me Twice: An Analysis of Repeat Data Breaches within Firms. Workshop on the Economics of Information Security. La Jolla, CA.
 Woutersen, T. M., & Hausman, J. (2017, October). Improving the Power of Specification Tests. Departamental Seminar. Cambridge, MA: Massachusetts Institute of Technology.
 Woutersen, T. M., & Hausman, J. (2017, September). Improving the Power of Specification Tests. Departmental Seminar. College Station, PA: Penn State University.
 Woutersen, T. M., & Hausman, J. (2017, Summer). Increasing the Power of Specification Tests. Econometric Society Meeting, 2017. St. Louis, MO: Econometric Society.
 Woutersen, T. M. (2015, Fall). Increasing the Power of Specification Tests.. California Econometrics Conference. Brigham Young University.
 Woutersen, T. M. (2015, Fall). Confidence Sets for Continuous and Discontinuous Functions of Estimated Parameters.. Econometric Society World Congress. Montreal.
 Woutersen, T. M. (2014, Fall). Testing the Assumptions of the Local Average Treatment Effect Model.. Conference in honor of Jerry Hausman. Xiamen University.
 Woutersen, T. M. (2014, Spring). Confidence Sets for Continuous and Discontinuous Functions of Estimated Parameters.. Asian Econometric Society Meeting. Taipei, Taiwan.
 Woutersen, T. M. (2012, Fall). Testing Overidentifying Restrictions with Many Instruments and Heteroskedasticity. California Econometrics Conference at UC Davis. UC Davis.
 Woutersen, T. M. (2012, Summer). Testing Overidentifying Restrictions with Many Instruments and Heteroskedasticity.. Econometric Society Meeting. Evanston, IL.