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Tiemen Woutersen

Personal Details

First Name:Tiemen
Middle Name:
Last Name:Woutersen
Suffix:
RePEc Short-ID:pwo126
[This author has chosen not to make the email address public]
http://econ.jhu.edu/people/woutersen/index.html
Terminal Degree:2000 Economics Department; Brown University (from RePEc Genealogy)

Affiliation

Department of Economics
Johns Hopkins University

Baltimore, Maryland (United States)
http://www.econ.jhu.edu/

: (410)516-7601
(410)516-7600
Mergenthaler Hall, 3400 North Charles Street, Baltimore, MD 21218
RePEc:edi:dejhuus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Govert Bijwaard & Geert Ridder & Tiemen Woutersen, 2012. "A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model," Norface Discussion Paper Series 2012035, Norface Research Programme on Migration, Department of Economics, University College London.
  2. Robert M. de Jong & Tiemen Woutersen, 2007. "Dynamic time series binary choice," Economics Working Paper Archive 538, The Johns Hopkins University,Department of Economics.
  3. Jerry Hausman & Whitney K. Newey & Tiemen M. Woutersen & John Chao & Norman Swanson, 2007. "Instrumental variable estimation with heteroskedasticity and many instruments," CeMMAP working papers CWP22/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Tiemen Woutersen & Jerry Hausman, 2005. "Estimating a Semi-Parametric Duration Model without Specifying Heterogeneity," Economics Working Paper Archive 525, The Johns Hopkins University,Department of Economics.
  5. Geert Ridder & Tiemen Woutersen, 2002. "The Singularity of the Information Matrix of the Mixed Proportional Hazard Model," UWO Department of Economics Working Papers 20026, University of Western Ontario, Department of Economics.
  6. Tiemen Woutersen, 2002. "Minimal Asymptotic Distributions for Estimators of Panel Data Models," UWO Department of Economics Working Papers 200212, University of Western Ontario, Department of Economics.
  7. Tiemen Woutersen, 2002. "Robustness against Incidental Parameters," UWO Department of Economics Working Papers 20028, University of Western Ontario, Department of Economics.
  8. Tiemen Woutersen & Marcel Voia, 2002. "Adaptive Estimation of the Dynamic Linear Model with Fixed Effects," UWO Department of Economics Working Papers 200210, University of Western Ontario, Department of Economics.
  9. Tiemen Woutersen, 2001. "Robustness Against Incidental Parameters and Mixing Distributions," UWO Department of Economics Working Papers 200110, University of Western Ontario, Department of Economics.
  10. Tiemen Woutersen, 2001. "Robustness against priors and mixing distributions," Computing in Economics and Finance 2001 168, Society for Computational Economics.
  11. Geert Ridder & Tiemen Woutersen, 2001. "The Singularity of the Efficiency Bound of the Mixed Proportional Hazard Model," UWO Department of Economics Working Papers 20019, University of Western Ontario, Department of Economics.
  12. Tiemen Woutersen, 2000. "Estimators for Panel Duration Data with Endogenous Censoring and Endogenous Regressors," Econometric Society World Congress 2000 Contributed Papers 1581, Econometric Society.

Articles

  1. Geert Ridder & Tiemen M. Woutersen, 2003. "The Singularity of the Information Matrix of the Mixed Proportional Hazard Model," Econometrica, Econometric Society, vol. 71(5), pages 1579-1589, September.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Govert Bijwaard & Geert Ridder & Tiemen Woutersen, 2012. "A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model," Norface Discussion Paper Series 2012035, Norface Research Programme on Migration, Department of Economics, University College London.

    Cited by:

    1. Janys, Lena, 2017. "A General Semiparametric Approach to Inference with Marker-Dependent Hazard Rate Models," Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168077, Verein für Socialpolitik / German Economic Association.
    2. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.
    3. Hausman, Jerry A. & Woutersen, Tiemen, 2014. "Estimating a semi-parametric duration model without specifying heterogeneity," Journal of Econometrics, Elsevier, vol. 178(P1), pages 114-131.
    4. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
    5. Govert Bijwaard & Christian Schluter, 2016. "Interdependent Hazards, Local Interactions, and the Return Decision of Recent Migrants," CReAM Discussion Paper Series 1620, Centre for Research and Analysis of Migration (CReAM), Department of Economics, University College London.

  2. Robert M. de Jong & Tiemen Woutersen, 2007. "Dynamic time series binary choice," Economics Working Paper Archive 538, The Johns Hopkins University,Department of Economics.

    Cited by:

    1. Don Harding & Adrian Pagan, 2009. "An Econometric Analysis of Some Models for Constructed Binary Time Series," NCER Working Paper Series 39, National Centre for Econometric Research, revised 02 Jul 2009.
    2. Seo, Myung Hwan & Linton, Oliver, 2007. "A smoothed least squares estimator for threshold regression models," Journal of Econometrics, Elsevier, vol. 141(2), pages 704-735, December.
    3. Hahn, Jinyong & Kuersteiner, Guido, 2010. "Stationarity and mixing properties of the dynamic Tobit model," Economics Letters, Elsevier, vol. 107(2), pages 105-111, May.
    4. Siddhartha Chib & Michael J. Dueker, 2004. "Non-Markovian regime switching with endogenous states and time-varying state strengths," Working Papers 2004-030, Federal Reserve Bank of St. Louis.
    5. Frazier, David T. & Liu, Xiaochun, 2016. "A new approach to risk-return trade-off dynamics via decomposition," Journal of Economic Dynamics and Control, Elsevier, vol. 62(C), pages 43-55.
    6. DHAENE, Geert & JOCHMANS, Koen, 2010. "Split-panel jackknife estimation of fixed-effect models," CORE Discussion Papers 2010003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. George Monokroussos, 2009. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Discussion Papers 09-07, University at Albany, SUNY, Department of Economics.
    8. Le-Yu Chen & Sokbae Lee, 2016. "Best Subset Binary Prediction," Papers 1610.02738, arXiv.org, revised May 2018.
    9. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    10. Moysiadis, Theodoros & Fokianos, Konstantinos, 2014. "On binary and categorical time series models with feedback," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 209-228.
    11. Park, Byeong U. & Simar, Léopold & Zelenyuk, Valentin, 2017. "Nonparametric estimation of dynamic discrete choice models for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 97-120.
    12. Harri Ponka, 2017. "The Role of Credit in Predicting US Recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 469-482, August.
    13. Taisuke Otsu & Myung Hwan Seo, 2014. "Asymptotics for maximum score method under general conditions," STICERD - Econometrics Paper Series 571, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    14. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.
    15. Taylor, James W., 2017. "Probabilistic forecasting of wind power ramp events using autoregressive logit models," European Journal of Operational Research, Elsevier, vol. 259(2), pages 703-712.
    16. Gnabo, Jean-Yves & de Mello, Luiz & Moccero, Diego, 2008. "Interdependencies between Monetary Policy and Foreign Exchange Intervention under Inflation Targeting: The Case of Brazil and the Czech Republic," WIDER Working Paper Series 095, World Institute for Development Economic Research (UNU-WIDER).
    17. James W. Taylor & Keming Yu, 2016. "Using auto-regressive logit models to forecast the exceedance probability for financial risk management," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 1069-1092, October.
    18. Anatolyev Stanislav, 2009. "Multi-Market Direction-of-Change Modeling Using Dependence Ratios," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(1), pages 1-24, March.
    19. Freitag L., 2014. "Procyclicality and path dependence of sovereign credit ratings: The example of Europe," Research Memorandum 020, Maastricht University, Graduate School of Business and Economics (GSBE).
    20. Michel, Jon & de Jong, Robert M., 2018. "Mixing properties of the dynamic Tobit model with mixing errors," Economics Letters, Elsevier, vol. 162(C), pages 112-115.
    21. Fokianos, Konstantinos & Moysiadis, Theodoros, 2017. "Binary time series models driven by a latent process," Econometrics and Statistics, Elsevier, vol. 2(C), pages 117-130.
    22. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
    23. Antunes, António & Bonfim, Diana & Monteiro, Nuno & Rodrigues, Paulo M.M., 2018. "Forecasting banking crises with dynamic panel probit models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 249-275.
    24. Lei, J., 2013. "Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models," Discussion Paper 2013-061, Tilburg University, Center for Economic Research.
    25. Dardanoni, Valentino & Li Donni, Paolo, 2012. "Incentive and selection effects of Medigap insurance on inpatient care," Journal of Health Economics, Elsevier, vol. 31(3), pages 457-470.
    26. Igor Kheifets & Carlos Velasco, 2012. "Model Adequacy Checks for Discrete Choice Dynamic Models," Working Papers w0170, Center for Economic and Financial Research (CEFIR).
    27. Francis Bismans & Reynald Majetti, 2013. "Forecasting recessions using financial variables: the French case," Empirical Economics, Springer, vol. 44(2), pages 419-433, April.
    28. Gnabo, Jean-Yves & Laurent, Sébastien & Lecourt, Christelle, 2009. "Does transparency in central bank intervention policy bring noise to the FX market?: The case of the Bank of Japan," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(1), pages 94-111, February.
    29. Manner, Hans & Türk, Dennis & Eichler, Michael, 2016. "Modeling and forecasting multivariate electricity price spikes," Energy Economics, Elsevier, vol. 60(C), pages 255-265.
    30. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, Center for Economic and Financial Research (CEFIR).
    31. Brause, Alexander, 2008. "Foreign exchange interventions in emerging market countries: New lessons from Argentina," W.E.P. - Würzburg Economic Papers 79, University of Würzburg, Chair for Monetary Policy and International Economics.
    32. Chen, Songnian & Zhang, Hanghui, 2015. "Binary quantile regression with local polynomial smoothing," Journal of Econometrics, Elsevier, vol. 189(1), pages 24-40.
    33. Liu, Xiaochun & Luger, Richard, 2015. "Unfolded GARCH models," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 186-217.

  3. Jerry Hausman & Whitney K. Newey & Tiemen M. Woutersen & John Chao & Norman Swanson, 2007. "Instrumental variable estimation with heteroskedasticity and many instruments," CeMMAP working papers CWP22/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Jorge Gallego & Stanislao Maldonado & Lorena Trujillo, 2018. "Blessing a Curse? Institutional Reform and Resource Booms in Colombia," Working Papers 122, Peruvian Economic Association.
    2. Morricone, Serena & Munari, Federico & Oriani, Raffaele & de Rassenfosse, Gaetan, 2017. "Commercialization Strategy and IPO Underpricing," Research Policy, Elsevier, vol. 46(6), pages 1133-1141.
    3. Einiö, Elias, 2016. "The loss of production work: evidence from quasiexperimental identification of labour demand functions," LSE Research Online Documents on Economics 69019, London School of Economics and Political Science, LSE Library.
    4. Eric French & Jae Song, 2014. "The Effect of Disability Insurance Receipt on Labor Supply," American Economic Journal: Economic Policy, American Economic Association, vol. 6(2), pages 291-337, May.
    5. Bekker, Paul A. & Crudu, Federico, 2012. "Symmetric Jackknife Instrumental Variable Estimation," MPRA Paper 37853, University Library of Munich, Germany.
    6. John Chao & Jerry Hausman & Whitney Newey & Norman Swanson & Tiemen Woutersen, 2013. "An Expository Note on the Existence of Moments of Fuller and HFUL Estimators," Departmental Working Papers 201311, Rutgers University, Department of Economics.
    7. Attanasio, Orazio & Levell, Peter & Low, Hamish & Sánchez-Marcos, Virginia, 2015. "Aggregating Elasticities: Intensive and Extensive Margins of Female Labour Supply," CEPR Discussion Papers 10732, C.E.P.R. Discussion Papers.
    8. Frank Windmeijer, 2018. "Testing Over- and Underidentification in Linear Models, with Applications to Dynamic Panel Data and Asset-Pricing Models," Bristol Economics Discussion Papers 18/696, Department of Economics, University of Bristol, UK.
    9. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Naoto Kunitomo, 2012. "An optimal modification of the LIML estimation for many instruments and persistent heteroscedasticity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(5), pages 881-910, October.
    11. Carrasco, Marine & Tchuente, Guy, 2015. "Regularized LIML for many instruments," Journal of Econometrics, Elsevier, vol. 186(2), pages 427-442.
    12. Federico Crudu & Giovanni Mellace & Zsolt Sandor, 2017. "Inference in instrumental variables models with heteroskedasticity and many instruments," Department of Economics University of Siena 761, Department of Economics, University of Siena.
    13. Bekker, Paul A. & Crudu, Federico, 2015. "Jackknife instrumental variable estimation with heteroskedasticity," Journal of Econometrics, Elsevier, vol. 185(2), pages 332-342.
    14. Steven F. Lehrer & Weili Ding, 2017. "Are genetic markers of interest for economic research?," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 6(1), pages 1-23, December.
    15. Jan F. KIVIET & Qu FENG, 2014. "Efficiency Gains by Modifying GMM Estimation in Linear Models under Heteroskedasticity," Economic Growth Centre Working Paper Series 1413, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    16. Marine Carrasco & Guy Tchuente, 2015. "Efficient estimation with many weak instruments using regularization techniques," Studies in Economics 1517, School of Economics, University of Kent.
    17. Eric Gautier & Alexandre Tsybakov, 2011. "High-Dimensional Instrumental Variables Regression and Confidence Sets," Working Papers 2011-13, Center for Research in Economics and Statistics.
    18. Michal Kolesár & Raj Chetty & John Friedman & Edward Glaeser & Guido W. Imbens, 2015. "Identification and Inference With Many Invalid Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 474-484, October.
    19. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    20. Naoto Kunitomo, 2008. "An Optimal Modification of the LIML Estimation for Many Instruments and Persistent Heteroscedasticity," CIRJE F-Series CIRJE-F-576, CIRJE, Faculty of Economics, University of Tokyo.
    21. Hausman, Jerry & Lewis, Randall & Menzel, Konrad & Newey, Whitney, 2011. "Properties of the CUE estimator and a modification with moments," Journal of Econometrics, Elsevier, vol. 165(1), pages 45-57.
    22. Crudu, Federico & Sándor, Zsolt, 2011. "On the finite-sample properties of conditional empirical likelihood estimators," MPRA Paper 34116, University Library of Munich, Germany.
    23. Einiö, Elias, 2015. "The Loss of Production Work: Identification of Demand Shifts Based on Local Soviet Trade Shocks," Working Papers 61, VATT Institute for Economic Research.
    24. Bekker, Paul & Wansbeek, Tom, 2016. "Simple many-instruments robust standard errors through concentrated instrumental variables," Economics Letters, Elsevier, vol. 149(C), pages 52-55.
    25. Abutaliev, Albert & Anatolyev, Stanislav, 2013. "Asymptotic variance under many instruments: Numerical computations," Economics Letters, Elsevier, vol. 118(2), pages 272-274.
    26. Eric French & Jae Song, 2012. "The effect of Disability Insurance receipt on labor supply: a dynamic analysis," Working Paper Series WP-2012-12, Federal Reserve Bank of Chicago.
    27. Kolesár, Michal, 2018. "Minimum distance approach to inference with many instruments," Journal of Econometrics, Elsevier, vol. 204(1), pages 86-100.
    28. Hübler, Olaf, 2013. "Methods in empirical economics - a selective review with applications," Hannover Economic Papers (HEP) dp-513, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    29. Naoto Kunitomo & Yukitoshi Matsushita, 2008. "Improving the Rank-Adjusted Anderson-Rubin Test with Many Instruments and Persistent Heteroscedasticity," CIRJE F-Series CIRJE-F-588, CIRJE, Faculty of Economics, University of Tokyo.
    30. Ng Serena & Bai Jushan, 2009. "Selecting Instrumental Variables in a Data Rich Environment," Journal of Time Series Econometrics, De Gruyter, vol. 1(1), pages 1-34, April.
    31. Canay, Ivan A., 2010. "Simultaneous selection and weighting of moments in GMM using a trapezoidal kernel," Journal of Econometrics, Elsevier, vol. 156(2), pages 284-303, June.
    32. Antoine, Bertille & Lavergne, Pascal, 2014. "Conditional moment models under semi-strong identification," Journal of Econometrics, Elsevier, vol. 182(1), pages 59-69.
    33. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    34. Christian Hansen & Jerry Hausman & Whitney K. Newey, 2006. "Estimation with many instrumental variables," CeMMAP working papers CWP19/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    35. Elias Einiö, 2016. "The Loss of Production Work: Evidence from Quasi-Experimental Identification of Labour Demand Functions," CEP Discussion Papers dp1451, Centre for Economic Performance, LSE.
    36. Calhoun, Gray, 2010. "Hypothesis Testing in Linear Regression when K/N is Large," Staff General Research Papers Archive 32216, Iowa State University, Department of Economics.
    37. Vahagn Galstyan, 2016. "LIML Estimation of Import Demand and Export Supply Elasticities," Trinity Economics Papers tep0316, Trinity College Dublin, Department of Economics, revised Jun 2016.
    38. Pierre Chausse, 2017. "Regularized Empirical Likelihood as a Solution to the No Moment," Working Papers 1708, University of Waterloo, Department of Economics, revised Nov 2017.
    39. Florens, Jean-Pierre & Van Bellegem, Sébastien, 2015. "Instrumental variable estimation in functional linear models," Journal of Econometrics, Elsevier, vol. 186(2), pages 465-476.
    40. Paul J. Devereux & Daniel A. Ackerberg, 2008. "Improved Jive estimators for overidentified linear models with and without heteroskedasticity," Working Papers 200817, School of Economics, University College Dublin.
    41. Wang, Wenjie & Kaffo, Maximilien, 2016. "Bootstrap inference for instrumental variable models with many weak instruments," Journal of Econometrics, Elsevier, vol. 192(1), pages 231-268.
    42. Priebe, Jan, 2011. "Child Costs and the Causal Effect of Fertility on Female Labor Supply: An investigation for Indonesia 1993-2008," Proceedings of the German Development Economics Conference, Berlin 2011 67, Verein für Socialpolitik, Research Committee Development Economics.
    43. Yoonseok Lee & Yu Zhou, 2015. "Averaged Instrumental Variables Estimators," Center for Policy Research Working Papers 180, Center for Policy Research, Maxwell School, Syracuse University.
    44. Jaeger, David A. & Parys, Juliane, 2009. "On the Sensitivity of Return to Schooling Estimates to Estimation Methods, Model Specification, and Influential Outliers If Identification Is Weak," IZA Discussion Papers 3961, Institute for the Study of Labor (IZA).
    45. Van Bellegem, Sébastien & Florens, Jean-Pierre, 2014. "Instrumental variable estimation in functional linear models," CORE Discussion Papers 2014056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

  4. Tiemen Woutersen & Jerry Hausman, 2005. "Estimating a Semi-Parametric Duration Model without Specifying Heterogeneity," Economics Working Paper Archive 525, The Johns Hopkins University,Department of Economics.

    Cited by:

    1. Burda, Martin & Harding, Matthew, 2014. "Environmental Justice: Evidence from Superfund cleanup durations," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 380-401.
    2. Khan, Shakeeb & Tamer, Elie, 2007. "Partial rank estimation of duration models with general forms of censoring," Journal of Econometrics, Elsevier, vol. 136(1), pages 251-280, January.
    3. Shin, Youngki, 2008. "Rank estimation of monotone hazard models," Economics Letters, Elsevier, vol. 100(1), pages 80-82, July.

  5. Geert Ridder & Tiemen Woutersen, 2002. "The Singularity of the Information Matrix of the Mixed Proportional Hazard Model," UWO Department of Economics Working Papers 20026, University of Western Ontario, Department of Economics.

    Cited by:

    1. Ruixuan Liu, 2016. "A Single-index Cox Model Driven by Levy Subordinators," Emory Economics 1602, Department of Economics, Emory University (Atlanta).
    2. Burda, Martin & Harding, Matthew, 2014. "Environmental Justice: Evidence from Superfund cleanup durations," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 380-401.
    3. Chen, Xiaohong & Liao, Zhipeng, 2014. "Sieve M inference on irregular parameters," Journal of Econometrics, Elsevier, vol. 182(1), pages 70-86.
    4. Effraimidis, Georgios, 2016. "Nonparametric Identification of a Time-Varying Frailty Model," COHERE Working Paper 2016:6, University of Southern Denmark, COHERE - Centre of Health Economics Research.
    5. Jerry Hausman & Tiemen M. Woutersen, 2005. "Estimating a semi-parametric duration model without specifying heterogeneity," CeMMAP working papers CWP11/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.
    7. Hausman, Jerry A. & Woutersen, Tiemen, 2014. "Estimating a semi-parametric duration model without specifying heterogeneity," Journal of Econometrics, Elsevier, vol. 178(P1), pages 114-131.
    8. Jaap H. Abbring, 0000. "Mixed Hitting-Time Models," Tinbergen Institute Discussion Papers 07-057/3, Tinbergen Institute, revised 11 Aug 2009.
    9. Sasaki, Yuya, 2015. "Heterogeneity and selection in dynamic panel data," Journal of Econometrics, Elsevier, vol. 188(1), pages 236-249.
    10. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
    11. Bo E. Honoré & Aureo de Paula, 2009. ""Interdependent Durations" Third Version," PIER Working Paper Archive 09-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Feb 2008.
    12. Bo E. Honor & Áureo De Paula, 2010. "Interdependent Durations," Review of Economic Studies, Oxford University Press, vol. 77(3), pages 1138-1163.
    13. Arkadiusz Szydlowski, 2015. "Endogenous Censoring in the Mixed Proportional Hazard Model with an Application to Optimal Unemployment Insurance," Discussion Papers in Economics 15/06, Department of Economics, University of Leicester.
    14. Jaap H. Abbring, 2012. "Mixed Hitting‐Time Models," Econometrica, Econometric Society, vol. 80(2), pages 783-819, March.
    15. Jaap H. Abbring, 2010. "Identification of Dynamic Discrete Choice Models," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 367-394, September.
    16. Bo E. Honore & Aureo de Paula, 2007. "Interdependent Durations, Second Version," PIER Working Paper Archive 08-044, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Nov 2008.
    17. Arulampalam, Wiji & Corradi, Valentina & Gutknecht, Daniel, 2017. "Modeling heaped duration data: An application to neonatal mortality," Journal of Econometrics, Elsevier, vol. 200(2), pages 363-377.

  6. Tiemen Woutersen, 2002. "Robustness against Incidental Parameters," UWO Department of Economics Working Papers 20028, University of Western Ontario, Department of Economics.

    Cited by:

    1. Ivan Fernandez-Val & Martin Weidner, 2013. "Individual and Time Effects in Nonlinear Panel Models with Large N, T," Papers 1311.7065, arXiv.org, revised Nov 2015.
    2. Manuel Arellano & Stéphane Bonhomme, 2009. "Robust Priors in Nonlinear Panel Data Models," Econometrica, Econometric Society, vol. 77(2), pages 489-536, March.
    3. DHAENE, Geert & JOCHMANS, Koen, 2010. "Split-panel jackknife estimation of fixed-effect models," CORE Discussion Papers 2010003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Fernández-Val, Iván & Vella, Francis, 2007. "Bias Corrections for Two-Step Fixed Effects Panel Data Estimators," IZA Discussion Papers 2690, Institute for the Study of Labor (IZA).
    5. Martin Browning & Jesus Carro, 2006. "Heterogeneity and Microeconometrics Modelling," CAM Working Papers 2006-03, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    6. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
    7. Fernández-Val, Iván, 2009. "Fixed effects estimation of structural parameters and marginal effects in panel probit models," Journal of Econometrics, Elsevier, vol. 150(1), pages 71-85, May.
    8. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.
    9. Ivan Fernandez-Val & Martin Weidner, 2017. "Fixed effect estimation of large T panel data models," CeMMAP working papers CWP42/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Victor Chernozhukov & Ivan Fernandez-Val & Jinyong Hahn & Whitney Newey, 2009. "Identification and Estimation of Marginal Effects in Nonlinear Panel Models," Boston University - Department of Economics - Working Papers Series wp2009-b, Boston University - Department of Economics.
    11. Geert Dhaene & Koen Jochmans, 2013. "Likelihood inference in an Autoregression with fixed effects," Sciences Po publications 2013-07, Sciences Po.
    12. Hausman, Jerry A. & Woutersen, Tiemen, 2014. "Estimating a semi-parametric duration model without specifying heterogeneity," Journal of Econometrics, Elsevier, vol. 178(P1), pages 114-131.
    13. Jinyong Hahn & Whitney K. Newey, 2003. "Jackknife and analytical bias reduction for nonlinear panel models," CeMMAP working papers CWP17/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Hahn, Jinyong, 2004. "Does Jeffrey's prior alleviate the incidental parameter problem?," Economics Letters, Elsevier, vol. 82(1), pages 135-138, January.
    15. Kyoo il Kim, 2006. "Higher Order Bias Correcting Moment Equation for M-Estimation and its Higher Order Efficiency," Labor Economics Working Papers 22453, East Asian Bureau of Economic Research.
    16. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
    17. Mingli Chen & Iv'an Fern'andez-Val & Martin Weidner, 2014. "Nonlinear Factor Models for Network and Panel Data," Papers 1412.5647, arXiv.org, revised Jun 2018.
    18. Haruo Iwakura, 2014. "Deriving the Information Bounds for Nonlinear Panel Data Models with Fixed Effects," KIER Working Papers 886, Kyoto University, Institute of Economic Research.
    19. Arthur Lewbel, 2006. "Modeling Heterogeneity," Boston College Working Papers in Economics 650, Boston College Department of Economics.
    20. Hospido, Laura, 2010. "Modelling Heterogeneity and Dynamics in the Volatility of Individual Wages," IZA Discussion Papers 4712, Institute for the Study of Labor (IZA).
    21. Michael Lechner & Stefan Lollivier & Thierry Magnac, 2005. "Parametric Binary Choice Models," University of St. Gallen Department of Economics working paper series 2005 2005-23, Department of Economics, University of St. Gallen.
    22. Giovanni Forchini & Bin Peng, 2016. "A Conditional Approach to Panel Data Models with Common Shocks," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-12, January.
    23. Iv'an Fern'andez-Val & Martin Weidner, 2017. "Fixed Effect Estimation of Large T Panel Data Models," Papers 1709.08980, arXiv.org, revised Mar 2018.
    24. Amaresh Tiwari & Franz Palm, 2011. "Nonlinear Panel Data Models with Expected a Posteriori Values of Correlated Random Effects," CREPP Working Papers 1113, Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège.

  7. Tiemen Woutersen & Marcel Voia, 2002. "Adaptive Estimation of the Dynamic Linear Model with Fixed Effects," UWO Department of Economics Working Papers 200210, University of Western Ontario, Department of Economics.

    Cited by:

    1. Geert Dhaene & Koen Jochmans, 2013. "Likelihood inference in an Autoregression with fixed effects," Sciences Po publications 2013-07, Sciences Po.

  8. Tiemen Woutersen, 2001. "Robustness Against Incidental Parameters and Mixing Distributions," UWO Department of Economics Working Papers 200110, University of Western Ontario, Department of Economics.

    Cited by:

    1. Carro, Jesus M., 2007. "Estimating dynamic panel data discrete choice models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 503-528, October.
    2. Arellano, M., 2001. "Discrete Choices with Panel Data," Papers 0101, Centro de Estudios Monetarios Y Financieros-.

  9. Geert Ridder & Tiemen Woutersen, 2001. "The Singularity of the Efficiency Bound of the Mixed Proportional Hazard Model," UWO Department of Economics Working Papers 20019, University of Western Ontario, Department of Economics.

    Cited by:

    1. G.E. Bijwaard, 2002. "Instrumental Variable Estimation for Duration Data: A Reappraisal of the Illinois Reemployment Bonus Experiment," Econometrics 0204001, EconWPA.
    2. Govert Bijwaard & Geert Ridder & Tiemen Woutersen, 2012. "A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model," Norface Discussion Paper Series 2012035, Norface Research Programme on Migration, Department of Economics, University College London.
    3. Bijwaard, Govert, 2011. "Unobserved Heterogeneity in Multiple-Spell Multiple-States Duration Models," IZA Discussion Papers 5748, Institute for the Study of Labor (IZA).
    4. Govert E. Bijwaard, 2008. "Instrumental Variable Estimation for Duration Data," Tinbergen Institute Discussion Papers 08-032/4, Tinbergen Institute.
    5. Govert Bijwaard, 2014. "Multistate event history analysis with frailty," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(58), pages 1591-1620, May.

  10. Tiemen Woutersen, 2000. "Estimators for Panel Duration Data with Endogenous Censoring and Endogenous Regressors," Econometric Society World Congress 2000 Contributed Papers 1581, Econometric Society.

    Cited by:

    1. Govert Bijwaard & Geert Ridder & Tiemen Woutersen, 2012. "A Simple GMM Estimator for the Semiparametric Mixed Proportional Hazard Model," Norface Discussion Paper Series 2012035, Norface Research Programme on Migration, Department of Economics, University College London.
    2. Joel L. Horowitz & Sokbae Lee, 2002. "Semiparametric Estimation of a Panel Data Proportional Hazards Model with Fixed Effects," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 A5-3, International Conferences on Panel Data.
    3. Wolter, James Lewis, 2016. "Kernel estimation of hazard functions when observations have dependent and common covariates," Journal of Econometrics, Elsevier, vol. 193(1), pages 1-16.
    4. Hausman, Jerry A. & Woutersen, Tiemen, 2014. "Estimating a semi-parametric duration model without specifying heterogeneity," Journal of Econometrics, Elsevier, vol. 178(P1), pages 114-131.
    5. James Wolter, 2015. "Kernel Estimation Of Hazard Functions When Observations Have Dependent and Common Covariates," Economics Series Working Papers 761, University of Oxford, Department of Economics.
    6. Van den Berg, Gerard J., 2000. "Duration Models: Specification, Identification, and Multiple Durations," MPRA Paper 9446, University Library of Munich, Germany.

Articles

  1. Geert Ridder & Tiemen M. Woutersen, 2003. "The Singularity of the Information Matrix of the Mixed Proportional Hazard Model," Econometrica, Econometric Society, vol. 71(5), pages 1579-1589, September.
    See citations under working paper version above.Sorry, no citations of articles recorded.

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 11 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (11) 2002-04-22 2002-04-22 2002-12-10 2002-12-10 2002-12-10 2004-10-30 2005-09-17 2005-12-20 2007-06-11 2007-11-24 2012-12-10. Author is listed
  2. NEP-DCM: Discrete Choice Models (2) 2004-10-30 2007-06-11
  3. NEP-ETS: Econometric Time Series (2) 2004-10-30 2007-06-11

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