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Helmut Farbmacher

Citations

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Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Helmut Farbmacher, 2012. "GMM with many weak moment conditions: Replication and application of Newey and Windmeijer (2009)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 343-346, March.

    Mentioned in:

    1. GMM with many weak moment conditions: Replication and application of Newey and Windmeijer (2009) (Journal of Applied Econometrics 2012) in ReplicationWiki ()

Working papers

  1. Farbmacher, Helmut & Tauchmann, Harald, 2021. "Linear fixed-effects estimation with non-repeated outcomes," FAU Discussion Papers in Economics 03/2021, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, revised 2021.

    Cited by:

    1. Jaka Cepec & Peter Grajzl & Barbara Mörec, 2022. "Public cash and modes of firm exit," Journal of Evolutionary Economics, Springer, vol. 32(1), pages 247-298, January.
    2. Elena Yurkevich & Harald Tauchmann, 2024. "cfbinout and xtdhazard: Control-function estimation of binary-outcome models and the discrete-time hazard model," German Stata Conference 2024 02, Stata Users Group.
    3. Sam Desiere & Bart Cockx, 2021. "How effective are hiring subsidies to reduce long-term unemployment among prime-aged jobseekers? Evidence from Belgium," LIDAM Discussion Papers IRES 2021024, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    4. Garcia, Alberto & Heilmayr, Robert, 2024. "Impact evaluation with nonrepeatable outcomes: The case of forest conservation," Journal of Environmental Economics and Management, Elsevier, vol. 125(C).

  2. Bucher-Koenen, Tabea & Farbmacher, Helmut & Guber, Raphael & Vikström, Johan, 2020. "Double trouble: The burden of child rearing and working on maternal mortality," Working Paper Series 2020:7, IFAU - Institute for Evaluation of Labour Market and Education Policy.

    Cited by:

    1. Yan Xiong & Guojin Jiao & Jiaming Zheng & Jian Gao & Yaqing Xue & Buwei Tian & Jingmin Cheng, 2022. "Fertility Intention and Influencing Factors for Having a Second Child among Floating Women of Childbearing Age," IJERPH, MDPI, vol. 19(24), pages 1-12, December.
    2. Öberg, Stefan, 2018. "Instrumental variables based on twin births are by definition not valid (v.3.0)," SocArXiv zux9s, Center for Open Science.
    3. Mingjia XIE & Ting YIN & Yi ZHANG & Takashi OSHIO, 2022. "The Hidden Cost of Having More Children: The Impact of Fertility on the Elderly's Healthcare Utilization," Discussion papers 22033, Research Institute of Economy, Trade and Industry (RIETI).
    4. Gerrit Bauer & Martina Brandt & Thorsten Kneip, 2023. "The Role of Parenthood for Life Satisfaction of Older Women and Men in Europe," Journal of Happiness Studies, Springer, vol. 24(1), pages 275-307, January.
    5. Beatrice Baaba Tawiah, 2023. "The Effect of Children on Health," Working Papers Dissertations 103, Paderborn University, Faculty of Business Administration and Economics.

  3. Helmut Farbmacher & Martin Huber & Luk'av{s} Laff'ers & Henrika Langen & Martin Spindler, 2020. "Causal mediation analysis with double machine learning," Papers 2002.12710, arXiv.org, revised Feb 2021.

    Cited by:

    1. Wang, Lulu & Lyu, Jie & Wang, Shanshan & Zhang, Junyan, 2025. "Unveiling the influence of agricultural mechanization on greenhouse gas emission intensity: Insights from China using causal machine learning model," Agricultural Systems, Elsevier, vol. 226(C).
    2. Yunpeng Fu & Zixuan Wang & Wenjia Zhao, 2025. "The Impact of Information Consumption Pilot Policy on Urban Land Green Use Efficiency: An Empirical Study from China," Land, MDPI, vol. 14(5), pages 1-31, April.
    3. AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021. "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised Mar 2022.
    4. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    5. Chiara Di Maria, 2025. "Investigating the Causal Effect of Deforestation on Infant Health Through Soil Characteristics: A Comparison of Traditional and Machine Learning Mediation Analysis Using Simulated and Real Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(2), pages 466-490, June.
    6. Aleksei Opacic, 2025. "Monotonic Path-Specific Effects: Application to Estimating Educational Returns," Papers 2508.13366, arXiv.org, revised Feb 2026.
    7. Lulu Wang & Jie Lyu & Junyan Zhang, 2024. "Explicating the Role of Agricultural Socialized Services on Chemical Fertilizer Use Reduction: Evidence from China Using a Double Machine Learning Model," Agriculture, MDPI, vol. 14(12), pages 1-16, November.
    8. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    9. Kentaro Kawato, 2025. "Balancing Weights for Causal Mediation Analysis," Papers 2512.09337, arXiv.org.
    10. Hugo Bodory & Martin Huber & Luk'av{s} Laff'ers, 2020. "Evaluating (weighted) dynamic treatment effects by double machine learning," Papers 2012.00370, arXiv.org, revised Jun 2021.
    11. Linsen Zhu & Yan Li & Lei Suo & Haiying Feng, 2025. "The Impact of High-Quality Development of Foreign Trade on Marine Economic Quality: Empirical Evidence from Coastal Provinces and Cities in China," Sustainability, MDPI, vol. 17(17), pages 1-29, August.
    12. Huang, Zhehao & Dong, Hao & Liu, Zhaofei & Albitar, Khaldoon, 2025. "Unleashing the empowered effect of data resource on inclusive green growth: Based on double machine learning," Economic Analysis and Policy, Elsevier, vol. 85(C), pages 1270-1290.
    13. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Jan 2025.
    14. Ding, Yijiu & Li, Bo & Lan, Dahai & Yu, Chunrong & Zhang, Xueqing, 2025. "Research on wage distortion in R&D and innovation activities —— Evidence from China's listed manufacturing enterprises," International Review of Economics & Finance, Elsevier, vol. 102(C).
    15. Wang, Wenna & Yang, Zhen & Chen, Jin & Liu, Jingjiang, 2025. "How does ESG affect government procurement in countries where ESG systems are established from the top down?," Economic Modelling, Elsevier, vol. 146(C).
    16. Su, Xiaoying & Zha, Donglan & Zhang, Chaoqun & Zhao, Kuokuo, 2025. "Does household lifestyle impact the decarbonization effect of carbon generalized system of preferences? Evidence from household survey in China," Journal of Asian Economics, Elsevier, vol. 98(C).
    17. Jiahui Li & Yu Yang & Yuqi Ye, 2025. "Rural Tourism, Economic Growth, and Environmental Sustainability: Empirical Evidence Based on County-Level Data in China," Sustainability, MDPI, vol. 17(20), pages 1-32, October.
    18. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    19. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    20. Linlin Wu & Zhen Qin & Peng Jing & Ying Xue & Danning Shao & Pan Luo, 2025. "The Causal Effect of Active School Travel on Children’s Subjective Well-Being: Evidence from the China Family Panel Survey," Journal of Happiness Studies, Springer, vol. 26(6), pages 1-28, August.
    21. Yuanyuan Zhang & Yongzhou Chen & Rong Xu, 2025. "The Health Cost of Rural Banquet Culture: The Mediating Role of Labor Time and Health Decision-Making—Evidence from Jiangsu, China," Sustainability, MDPI, vol. 17(12), pages 1-24, June.
    22. Li, Pengshi & Lin, Yan & Yu, Xing & Liu, Guifang, 2025. "Does bid-ask spread explains the smile? On DVF and DML," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    23. Yuchen Lu & Jiakun Zhuang & Jun Chen & Chenlu Yang & Mei Kong, 2025. "The Impact of Farmland Transfer on Urban–Rural Integration: Causal Inference Based on Double Machine Learning," Land, MDPI, vol. 14(1), pages 1-30, January.
    24. Jingwen Zhang & Yi Liu, 2025. "How Does Digital Innovation Empower the Development of New Quality Productive Forces? An Empirical Study Based on Double Machine Learning," Sustainability, MDPI, vol. 17(6), pages 1-29, March.
    25. Kangqi Jiang & Xiaofeng Chen & Jiayun Li & Mengling Zhou, 2025. "Technology adoption and extreme stock risk: Evidence from digital tax reform in China," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-20, December.
    26. Lu Kang & Jie Lv & Haoyang Zhang, 2024. "Can the Water Resource Fee-to-Tax Reform Promote the “Three-Wheel Drive” of Corporate Green Energy-Saving Innovations? Quasi-Natural Experimental Evidence from China," Energies, MDPI, vol. 17(12), pages 1-38, June.
    27. Shen, Neng & Zhang, Lin & Wang, Qunwei & Wu, Lianjun, 2025. "The power of institutional innovation in the urban carbon unlocking: A study on collaborative empowerment of digital policy and new energy policy," Energy Policy, Elsevier, vol. 206(C).
    28. Yang, Bai & Huang, Jingfeng & Chen, Yinzhong, 2025. "The relationship between ESG ratings and digital technological innovation in manufacturing: Insights via dual machine learning models," Finance Research Letters, Elsevier, vol. 71(C).
    29. Yangyang Zhong & Yilin Zhong & Longpeng Zhang & Zhiwei Tang, 2024. "The Path to Urban Sustainability: Urban Intelligent Transformation and Green Development—Evidence from 286 Cities in China," Sustainability, MDPI, vol. 16(23), pages 1-32, November.
    30. Zemenghong Bao & Zhisen Lin & Tiantian Jin & Kun Lv, 2024. "Regional Breakthrough Innovation Change Strategies, Ecological Location Suitability of High-Tech Industry Innovation Ecosystems, and Green Energy," Energies, MDPI, vol. 17(16), pages 1-34, August.
    31. Hu, Zheng & Xu, Yingzhi, 2025. "Can new energy demonstration cities break through the multiple carbon lock-in? Evidence based on double machine learning," Energy Policy, Elsevier, vol. 199(C).
    32. Wang, Yewen & Tang, Jiaxuan & Li, Cheng, 2025. "Registration reform and stock mispricing: Causal inference based on double machine learning," Research in International Business and Finance, Elsevier, vol. 73(PB).
    33. Zhang, Yingheng & Li, Haojie & Ren, Gang, 2025. "Analysing the role of traffic volume as mediator in transport policy evaluation with causal mediation analysis and targeted learning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 192(C).
    34. Ruiyu Hu & Zemenghong Bao & Zhisen Lin & Kun Lv, 2024. "The Innovative Construction of Provinces, Regional Artificial Intelligence Development, and the Resilience of Regional Innovation Ecosystems: Quasi-Natural Experiments Based on Spatial Difference-in-Differences Models and Double Machine Learning," Sustainability, MDPI, vol. 16(18), pages 1-37, September.
    35. Song, Hongti & Chen, Wei, 2025. "A digital blueprint for sustainability: Can digital infrastructure policies promote renewable energy innovation?," Renewable Energy, Elsevier, vol. 244(C).
    36. Ruonan Cai & Wencan Tian & Rundong Luo & Zhichao Fang & Zhigang Hu, 2025. "Do articles with multiple corresponding authorships have a citation advantage? A double machine learning analysis approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(5), pages 2523-2550, May.
    37. Xinyu Wei & Mingwang Cheng & Kaifeng Duan & Xiangxing Kong, 2024. "Effects of Big Data on PM 2.5 : A Study Based on Double Machine Learning," Land, MDPI, vol. 13(3), pages 1-21, March.
    38. Rahul Singh & Liyuan Xu & Arthur Gretton, 2021. "Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves," Papers 2111.03950, arXiv.org, revised Mar 2025.
    39. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects," Econometrica, Econometric Society, vol. 90(3), pages 967-1027, May.
    40. Dongli Chen & Qianxuan Huang, 2024. "The New Policy for Innovative Transformation in Regional Industrial Chains, the Conversion of New and Old Kinetic Energy, and Energy Poverty Alleviation," Energies, MDPI, vol. 17(11), pages 1-37, May.
    41. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2024. "The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting," Health Economics, John Wiley & Sons, Ltd., vol. 33(6), pages 1387-1411, June.
    42. Wang, Zhenzhen & Zhou, Feite & Zhong, Junhao, 2024. "Can China's low-carbon city pilot policy facilitate carbon neutrality? Evidence from a machine learning approach," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 756-773.
    43. Yingheng Zhang & Haojie Li & Gang Ren, 2025. "Ex-post evaluation of transport interventions with causal mediation analysis," Transportation, Springer, vol. 52(1), pages 93-126, February.

  4. Bach, P. & Farbmacher, H. & Spindler, M., 2016. "Semiparametric Count Data Modeling with an Application to Health Service Demand," Health, Econometrics and Data Group (HEDG) Working Papers 16/20, HEDG, c/o Department of Economics, University of York.

    Cited by:

    1. Hofer, Florian & Birkner, Benjamin & Spindler, Martin, 2021. "Power of machine learning algorithms for predicting dropouts from a German telemonitoring program using standardized claims data," hche Research Papers 24, University of Hamburg, Hamburg Center for Health Economics (hche).
    2. Liu, Weiwei & Egan, Kevin J, 2019. "A Semiparametric Smooth Coefficient Estimator for Recreation Demand," MPRA Paper 95294, University Library of Munich, Germany.
    3. Corsini, Noemi & Viroli, Cinzia, 2022. "Dealing with overdispersion in multivariate count data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).

  5. Frank Windmeijer & Helmut Farbmacher & Neil Davies & George Davey Smith, 2016. "On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments," Bristol Economics Discussion Papers 16/674, School of Economics, University of Bristol, UK, revised 08 Aug 2017.

    Cited by:

    1. Caner, Mehmet & Fan, Qingliang & Grennes, Thomas, 2021. "Partners in debt: An endogenous non-linear analysis of the effects of public and private debt on growth," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 694-711.
    2. Nicolas Apfel & Helmut Farbmacher & Rebecca Groh & Martin Huber & Henrika Langen, 2022. "Detecting Grouped Local Average Treatment Effects and Selecting True Instruments," Papers 2207.04481, arXiv.org, revised Oct 2023.
    3. Gregor Steiner & Jeremie Houssineau & Mark F. J. Steel, 2025. "Possibilistic Instrumental Variable Regression," Papers 2511.16029, arXiv.org, revised Jan 2026.
    4. Gregor Steiner & Mark Steel, 2025. "Bayesian Model Averaging in Causal Instrumental Variable Models," Papers 2504.13520, arXiv.org, revised Feb 2026.
    5. Nicolas Apfel, 2019. "Relaxing the Exclusion Restriction in Shift-Share Instrumental Variable Estimation," Papers 1907.00222, arXiv.org, revised Jul 2022.
    6. Martin, Stephen & Claxton, Karl & Lomas, James & Longo, Francesco, 2023. "The impact of different types of NHS expenditure on health: Marginal cost per QALY estimates for England for 2016/17," Health Policy, Elsevier, vol. 132(C).
    7. Jinyuan Chang & Zhentao Shi & Jia Zhang, 2021. "Culling the herd of moments with penalized empirical likelihood," Papers 2108.03382, arXiv.org, revised May 2022.
    8. Qingliang Fan & Yaqian Wu, 2020. "Endogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments," Papers 2006.14998, arXiv.org.
    9. Frank Windmeijer & Xiaoran Liang & Fernando P Hartwig & Jack Bowden, 2019. "The Confidence Interval Method for Selecting Valid Instrumental Variables," Bristol Economics Discussion Papers 19/715, School of Economics, University of Bristol, UK.
    10. Marcus Munafò & Neil M. Davies & George Davey Smith, 2020. "Can genetics reveal the causes and consequences of educational attainment?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 681-688, February.
    11. Diakodimitriou, Danai & Tsioutsios, Alexandros & Papageorgiou, Theofanis, 2025. "Education Expenditures and Growth: Is R&D the link?," Journal of Policy Modeling, Elsevier, vol. 47(2), pages 322-337.
    12. Olmez Turan, Merve & Gilbert, Ben & Flamand, Tulay, 2025. "How good are weather shocks for identifying energy elasticities? A LASSO-IV approach to European natural gas demand," Journal of Commodity Markets, Elsevier, vol. 39(C).
    13. Prosper Dovonon & Firmin Doko Tchatoka & Michael Aguessy, 2019. "Relevant moment selection under mixed identification strength," School of Economics and Public Policy Working Papers 2019-04, University of Adelaide, School of Economics and Public Policy.
    14. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2020. "Ill-posed estimation in high-dimensional models with instrumental variables," Journal of Econometrics, Elsevier, vol. 219(1), pages 171-200.
    15. Byunghoon Kang, 2018. "Higher Order Approximation of IV Estimators with Invalid Instruments," Working Papers 257105320, Lancaster University Management School, Economics Department.
    16. Qingliang Fan & Zijian Guo & Ziwei Mei, 2022. "A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates," Papers 2205.00171, arXiv.org, revised May 2024.
    17. Kumari, Meena & Bao, Yanchun & S. Clarke, Paul & Smart, Melissa, 2018. "A comparison of robust methods for Mendelian randomization using multiple genetic variants," ISER Working Paper Series 2018-08, Institute for Social and Economic Research.
    18. Matthew Harding & Carlos Lamarche & Chris Muris, 2022. "Estimation of a Factor-Augmented Linear Model with Applications Using Student Achievement Data," Papers 2203.03051, arXiv.org.
    19. Nicolas Apfel & Frank Windmeijer, 2022. "The Falsification Adaptive Set in Linear Models with Instrumental Variables that Violate the Exclusion or Conditional Exogeneity Restriction," Papers 2212.04814, arXiv.org, revised Apr 2024.
    20. Hyunseung Kang & Youjin Lee & T. Tony Cai & Dylan S. Small, 2022. "Two robust tools for inference about causal effects with invalid instruments," Biometrics, The International Biometric Society, vol. 78(1), pages 24-34, March.
    21. Liang, X.; & Sanderson, E.; & Windmeijer, F.;, 2022. "Selecting Valid Instrumental Variables in Linear Models with Multiple Exposure Variables: Adaptive Lasso and the Median-of-Medians Estimator," Health, Econometrics and Data Group (HEDG) Working Papers 22/22, HEDG, c/o Department of Economics, University of York.
    22. Hongwei Shi & Xinyu Zhang & Xu Guo & Baihua He & Chenyang Wang, 2025. "Testing overidentifying restrictions on high-dimensional instruments and covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 77(2), pages 331-352, April.
    23. Nicolas Apfel & Julia Hatamyar & Martin Huber & Jannis Kueck, 2024. "Learning control variables and instruments for causal analysis in observational data," Papers 2407.04448, arXiv.org, revised Sep 2025.
    24. Nicolas Apfel & Xiaoran Liang, 2024. "Agglomerative hierarchical clustering for selecting valid instrumental variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1201-1219, November.
    25. Ojah, Kalu & Kodongo, Odongo, 2024. "Effective financial inclusion and the need to put the horse before the cart: Saving!," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    26. Kogure, Katsuo & Kubo, Masahiro, 2022. "Cambodian Refugees," Discussion paper series HIAS-E-125, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    27. Biewen, Martin & Fitzenberger, Bernd & Seckler, Matthias, 2020. "Counterfactual quantile decompositions with selection correction taking into account Huber/Melly (2015): An application to the German gender wage gap," Labour Economics, Elsevier, vol. 67(C).
    28. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    29. Yiqi Lin & Frank Windmeijer & Xinyuan Song & Qingliang Fan, 2022. "On the instrumental variable estimation with many weak and invalid instruments," Papers 2207.03035, arXiv.org, revised Dec 2023.
    30. Ruoyu Wang & Qihua Wang & Wang Miao, 2023. "A robust fusion-extraction procedure with summary statistics in the presence of biased sources," Biometrika, Biometrika Trust, vol. 110(4), pages 1023-1040.
    31. Christian M. Dahl & Torben S. D. Johansen & Emil N. S{o}rensen & Christian E. Westermann & Simon F. Wittrock, 2021. "Applications of Machine Learning in Document Digitisation," Papers 2102.03239, arXiv.org.
    32. Christoph F. Kurz & Michael Laxy, 2020. "Application of Mendelian Randomization to Investigate the Association of Body Mass Index with Health Care Costs," Medical Decision Making, , vol. 40(2), pages 156-169, February.

  6. Farbmacher, Helmut & Guber, Raphael & Vikström, Johan, 2016. "Increasing the credibility of the Twin birth instrument," Working Paper Series 2016:10, IFAU - Institute for Evaluation of Labour Market and Education Policy.

    Cited by:

    1. Tabea Bucher-Koenen & Helmut Farbmacher & Raphael Guber & Johan Vikström, 2020. "Double Trouble: The Burden of Child-rearing and Working on Maternal Mortality," Demography, Springer;Population Association of America (PAA), vol. 57(2), pages 559-576, April.
    2. Huebener, Mathias & Odermatt, Reto, 2025. "The Wished-For Children: Do Mothers Carry the Burden While Fathers Reap the Joy?," IZA Discussion Papers 18039, IZA Network @ LISER.
    3. Joseph Boniface Ajefu, 2019. "Does having children affect women’s entrepreneurship decision? Evidence from Nigeria," Review of Economics of the Household, Springer, vol. 17(3), pages 843-860, September.
    4. Boyan Zheng & Qiongshi Lu & Jason Fletcher, 2023. "Estimating Causal Effects of Fertility on Life Course Outcomes: Evidence Using A Dyadic Genetic Instrumental Variable Approach," NBER Working Papers 30955, National Bureau of Economic Research, Inc.
    5. Manuel Denzer, 2019. "Estimating Causal Effects in Binary Response Models with Binary Endogenous Explanatory Variables - A Comparison of Possible Estimators," Working Papers 1916, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    6. Mark E. McGovern, 2018. "How Much Does Birth Weight Matter for Child Health in Developing Countries? Estimates from Siblings and Twins," CHaRMS Working Papers 18-04, Centre for HeAlth Research at the Management School (CHaRMS).
    7. Beatrice Baaba Tawiah, 2023. "The Effect of Children on Health," Working Papers Dissertations 103, Paderborn University, Faculty of Business Administration and Economics.
    8. Tumen, Semih & Turan, Belgi, 2020. "The Effect of Fertility on Female Labor Supply in a Labor Market with Extensive Informality," IZA Discussion Papers 13986, IZA Network @ LISER.
    9. Erika Raquel Badillo & Lina Cardona-Sosa & Carlos Medina & Leonardo Fabio Morales & Christian Posso, 2019. "Twin instrument, fertility and women’s labor force participation: evidence from Colombian low-income families," Borradores de Economia 1071, Banco de la Republica de Colombia.
    10. Hugo Bodory & Martin Huber & Michael Lechner, 2024. "The Finite Sample Performance of Instrumental Variable-Based Estimators of the Local Average Treatment Effect When Controlling for Covariates," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2053-2078, October.
    11. Cepaluni, Gabriel & Chewning, Taylor Kinsley & Driscoll, Amanda & Faganello, Marco Antonio, 2022. "Conditional cash transfers and child labor," World Development, Elsevier, vol. 152(C).

  7. Helmut Farbmacher & Peter Ihle & Ingrid Schubert & Joachim Winter & Amelie C. Wuppermann, 2013. "Heterogeneous Effects of a Nonlinear Price Schedule for Outpatient Care," CESifo Working Paper Series 4499, CESifo.

    Cited by:

    1. Klein, Tobias & Salm, Martin & Upadhyay, Suraj, 2020. "The Response to Dynamic Incentives in Insurance Contracts with a Deductible: Evidence from a Differences-in-Regression-Disconti," CEPR Discussion Papers 14552, C.E.P.R. Discussion Papers.
    2. Stefanie Thönnes, 2019. "Ex-post moral hazard in the health insurance market: empirical evidence from German data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(9), pages 1317-1333, December.
    3. Giampiero Marra & Matteo Fasiolo & Rosalba Radice & Rainer Winkelmann, 2022. "A flexible copula regression model with Bernoulli and Tweedie margins for estimating the effect of spending on mental health," ECON - Working Papers 413, Department of Economics - University of Zurich.
    4. Sá, Luís & Straume, Odd Rune, 2021. "Quality provision in hospital markets with demand inertia: The role of patient expectations," Journal of Health Economics, Elsevier, vol. 80(C).
    5. Johansson, Naimi & de New, Sonja C. & Kunz, Johannes S. & Petrie, Dennis & Svensson, Mikael, 2023. "Reductions in out-of-pocket prices and forward-looking moral hazard in health care demand," Journal of Health Economics, Elsevier, vol. 87(C).
    6. Michael Gerfin & Boris Kaiser & Christian Schmid, 2015. "Healthcare Demand in the Presence of Discrete Price Changes," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1164-1177, September.
    7. Galina Besstremyannaya, 2012. "Heterogeneous effect of coinsurance rate on the demand for health care: a finite mixture approach," Working Papers w0163, New Economic School (NES).
    8. Klein, Tobias J. & Salm, Martin & Upadhyay, Suraj, 2020. "The Response to Dynamic Incentives in Insurance Contracts with a Deductible: Evidence from a Differences-in-Regression-Discontinuities Design," IZA Discussion Papers 13108, IZA Network @ LISER.
    9. Winkelmann, Rainer, 2015. "An empirical model of health care demand under non-linear pricing," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113027, Verein für Socialpolitik / German Economic Association.
    10. Galina Besstremyannaya, 2014. "Heterogeneous effect of coinsurance rate on healthcare costs: generalized finite mixtures and matching estimators," Discussion Papers 14-014, Stanford Institute for Economic Policy Research.
    11. Haaga, Tapio & Böckerman, Petri & Kortelainen, Mika & Tukiainen, Janne, 2024. "Effects of nurse visit copayment on primary care use: Do low-income households pay the price?," Journal of Health Economics, Elsevier, vol. 94(C).
    12. Liu, Jiacheng & Wang, Yaojing & Yao, Yi, 2024. "Anticipated benefit termination and health care consumption responses: Evidence from a quasi-experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 227(C).
    13. Helmut Farbmacher & Peter Ihle & Ingrid Schubert & Joachim Winter & Amelie Wuppermann, 2017. "Heterogeneous Effects of a Nonlinear Price Schedule for Outpatient Care," Health Economics, John Wiley & Sons, Ltd., vol. 26(10), pages 1234-1248, October.
    14. Johannes S. Kunz & Rainer Winkelmann, 2015. "An econometric model of health care demand with non-linear pricing," ECON - Working Papers 204, Department of Economics - University of Zurich.

  8. Farbmacher, Helmut, 2009. "Copayments for doctor visits in Germany and the probability of visiting a physician - Evidence from a natural experiment," Discussion Papers in Economics 10951, University of Munich, Department of Economics.

    Cited by:

    1. Schmitz, Hendrik, 2013. "Practice budgets and the patient mix of physicians – The effect of a remuneration system reform on health care utilisation," Journal of Health Economics, Elsevier, vol. 32(6), pages 1240-1249.
    2. Eibich, Peter & Ziebarth, Nicolas R., 2014. "Analyzing regional variation in health care utilization using (rich) household microdata," Health Policy, Elsevier, vol. 114(1), pages 41-53.
    3. Mingming Xu & Benjamin Bittschi, 2022. "Does the abolition of copayment increase ambulatory care utilization?: a quasi-experimental study in Germany," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(8), pages 1319-1328, November.
    4. Wuppermann, Amelie Catherine, 2011. "Empirical Essays in Health and Education Economics," Munich Dissertations in Economics 13187, University of Munich, Department of Economics.

Articles

  1. Helmut Farbmacher & Maximilian Hartmann & Heinrich Kögel, 2022. "Economic Hardship, Sleep, and Self-Rated Health," American Journal of Health Economics, University of Chicago Press, vol. 8(2), pages 216-251.

    Cited by:

    1. Marks, Mindy & Prina, Silvia & Tahaj, Redina, 2024. "Short-term labor supply response to the timing of transfer payments: Evidence from the SNAP program," Labour Economics, Elsevier, vol. 91(C).

  2. Helmut Farbmacher & Raphael Guber & Sven Klaassen, 2022. "Instrument Validity Tests With Causal Forests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 605-614, April.

    Cited by:

    1. Thomas Carr & Toru Kitagawa, 2021. "Testing Instrument Validity with Covariates," Papers 2112.08092, arXiv.org, revised Sep 2023.
    2. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org, revised Oct 2024.
    3. Kugler, Philipp & Biewen, Martin, 2020. "Two-Stage Least Squares Random Forests with a Replication of Angrist and Evans (1998)," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224538, Verein für Socialpolitik / German Economic Association.
    4. Biewen, Martin & Kugler, Philipp, 2020. "Two-Stage Least Squares Random Forests with an Application to Angrist and Evans (1998)," IZA Discussion Papers 13613, IZA Network @ LISER.
    5. Bhuller, Manudeep & Sigstad, Henrik, 2024. "2SLS with multiple treatments," Journal of Econometrics, Elsevier, vol. 242(1).
    6. Eibich, Peter, 2023. "Instrumental variable estimates of the burden of parental caregiving," The Journal of the Economics of Ageing, Elsevier, vol. 26(C).
    7. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    8. Sun, Zhenting & Wüthrich, Kaspar, 2025. "Pairwise valid instruments," Journal of Econometrics, Elsevier, vol. 250(C).

  3. Farbmacher, Helmut & Kögel, Heinrich & Spindler, Martin, 2021. "Heterogeneous effects of poverty on attention," Labour Economics, Elsevier, vol. 71(C).

    Cited by:

    1. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    2. Bernard, René, 2022. "Mental Accounting and the Marginal Propensity to Consume," VfS Annual Conference 2022 (Basel): Big Data in Economics 264186, Verein für Socialpolitik / German Economic Association.
    3. Timothée Demont & Daniela Horta Sáenz & Eva Raiber, 2023. "Turning worries into cognitive performance: Results from an online experiment during Covid," AMSE Working Papers 2302, Aix-Marseille School of Economics, France.
    4. Bernard, René, 2023. "Mental accounting and the marginal propensity to consume," Discussion Papers 13/2023, Deutsche Bundesbank.
    5. Quiroga Gutierrez, Ana Cecilia, 2024. "Picture this: Making health insurance choices easier for those who need it," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 111(C).
    6. Sharafi, Zahra, 2023. "Poverty and perseverance: The detrimental effect of poverty on effort provision," Journal of Development Economics, Elsevier, vol. 162(C).
    7. Eva Raiber & Daniela Horta Saenz & Timothée Demont, 2023. "Turning worries into performance: Results from an online experiment during COVID," French Stata Users' Group Meetings 2023 08, Stata Users Group.
    8. Hui Lan & Vasilis Syrgkanis, 2023. "Causal Q-Aggregation for CATE Model Selection," Papers 2310.16945, arXiv.org, revised Apr 2025.
    9. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).

  4. Maurice J. G. Bun & Helmut Farbmacher & Rutger W. Poldermans, 2020. "Finite sample properties of the GMM Anderson–Rubin test," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 1042-1056, November.

    Cited by:

    1. Hugo Kruiniger, 2025. "Uniform Quasi ML based inference for the panel AR(1) model," Papers 2508.20855, arXiv.org, revised Dec 2025.
    2. Qu Feng & Sombut Jaidee & Wenjie Wang, 2025. "Robust Inference with High-Dimensional Instruments," Papers 2506.23834, arXiv.org.
    3. Federico Crudu & Giovanni Mellace & Zsolt Sándor, 2020. "Inference in instrumental variables models with heteroskedasticity and many instruments," Department of Economics University of Siena 821, Department of Economics, University of Siena.
    4. Dennis Lim & Wenjie Wang & Yichong Zhang, 2024. "A Dimension-Agnostic Bootstrap Anderson-Rubin Test For Instrumental Variable Regressions," Papers 2412.01603, arXiv.org, revised Sep 2025.

  5. Tabea Bucher-Koenen & Helmut Farbmacher & Raphael Guber & Johan Vikström, 2020. "Double Trouble: The Burden of Child-rearing and Working on Maternal Mortality," Demography, Springer;Population Association of America (PAA), vol. 57(2), pages 559-576, April.
    See citations under working paper version above.
  6. Frank Windmeijer & Helmut Farbmacher & Neil Davies & George Davey Smith, 2019. "On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1339-1350, July.
    See citations under working paper version above.
  7. Helmut Farbmacher & Raphael Guber & Johan Vikström, 2018. "Increasing the credibility of the twin birth instrument," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(3), pages 457-472, April.
    See citations under working paper version above.
  8. Bach, Philipp & Farbmacher, Helmut & Spindler, Martin, 2018. "Semiparametric count data modeling with an application to health service demand," Econometrics and Statistics, Elsevier, vol. 8(C), pages 125-140.
    See citations under working paper version above.
  9. Helmut Farbmacher & Peter Ihle & Ingrid Schubert & Joachim Winter & Amelie Wuppermann, 2017. "Heterogeneous Effects of a Nonlinear Price Schedule for Outpatient Care," Health Economics, John Wiley & Sons, Ltd., vol. 26(10), pages 1234-1248, October.
    See citations under working paper version above.
  10. Helmut Farbmacher & Heinrich Kögel, 2017. "Testing under a special form of heteroscedasticity," Applied Economics Letters, Taylor & Francis Journals, vol. 24(4), pages 264-268, February.

    Cited by:

    1. Tomasz Chrulski & Mariusz Łaciak, 2021. "Analysis of Natural Gas Consumption Interdependence for Polish Industrial Consumers on the Basis of an Econometric Model," Energies, MDPI, vol. 14(22), pages 1-26, November.

  11. Helmut Farbmacher, 2013. "Extensions Of Hurdle Models For Overdispersed Count Data," Health Economics, John Wiley & Sons, Ltd., vol. 22(11), pages 1398-1404, November.

    Cited by:

    1. Koppenberg, Maximilian & Mishra, Ashok K. & Hirsch, Stefan, 2023. "Food aid and violent conflict: A review and Empiricist’s companion," Food Policy, Elsevier, vol. 121(C).
    2. White-Means, Shelley I. & Osmani, Ahmad Reshad, 2018. "Affordable Care Act and Disparities in Health Services Utilization among Ethnic Minoritiy Breast Cancer Survivors: Evidence from Longitudinal Medical Expenditure Panel Surveys 2008-2015," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(9), pages 1-26.
    3. Koppenberg, Maximilian & Mishra, Ashok K. & Hirsch, Stefan, 2023. "Food Aid and Violent Conflict: A Review of Literature," IZA Discussion Papers 16574, IZA Network @ LISER.

  12. Helmut Farbmacher & Joachim Winter, 2013. "Per‐Period Co‐Payments And The Demand For Health Care: Evidence From Survey And Claims Data," Health Economics, John Wiley & Sons, Ltd., vol. 22(9), pages 1111-1123, September.

    Cited by:

    1. Stefanie Thönnes, 2019. "Ex-post moral hazard in the health insurance market: empirical evidence from German data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(9), pages 1317-1333, December.
    2. Schmitz, Hendrik, 2013. "Practice budgets and the patient mix of physicians – The effect of a remuneration system reform on health care utilisation," Journal of Health Economics, Elsevier, vol. 32(6), pages 1240-1249.
    3. Gregori Baetschmann & Rainer Winkelmann, 2014. "A Dynamic Hurdle Model for Zero-Inflated Count Data: With an Application to Health Care Utilization," SOEPpapers on Multidisciplinary Panel Data Research 648, DIW Berlin, The German Socio-Economic Panel (SOEP).
    4. Johansson, Naimi & de New, Sonja C. & Kunz, Johannes S. & Petrie, Dennis & Svensson, Mikael, 2023. "Reductions in out-of-pocket prices and forward-looking moral hazard in health care demand," Journal of Health Economics, Elsevier, vol. 87(C).
    5. Kuhn, Michael & Ochsen, Carsten, 2019. "Population change and the regional distribution of physicians," The Journal of the Economics of Ageing, Elsevier, vol. 14(C).
    6. Winkelmann, Rainer, 2015. "An empirical model of health care demand under non-linear pricing," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113027, Verein für Socialpolitik / German Economic Association.
    7. Kim Dalziel & Jinhu Li & Anthony Scott & Philip Clarke, 2018. "Accuracy of patient recall for self‐reported doctor visits: Is shorter recall better?," Health Economics, John Wiley & Sons, Ltd., vol. 27(11), pages 1684-1698, November.
    8. Helmut Farbmacher & Peter Ihle & Ingrid Schubert & Joachim Winter & Amelie Wuppermann, 2017. "Heterogeneous Effects of a Nonlinear Price Schedule for Outpatient Care," Health Economics, John Wiley & Sons, Ltd., vol. 26(10), pages 1234-1248, October.
    9. Johannes S. Kunz & Rainer Winkelmann, 2015. "An econometric model of health care demand with non-linear pricing," ECON - Working Papers 204, Department of Economics - University of Zurich.
    10. Raúl Del Pozo-Rubio & Isabel Pardo-García & Francisco Escribano-Sotos, 2020. "Financial Catastrophism Inherent with Out-of-Pocket Payments in Long Term Care for Households: A Latent Impoverishment," IJERPH, MDPI, vol. 17(1), pages 1-19, January.
    11. Himmel, Konrad & Schneider, Udo, 2017. "Ambulatory care at the end of a billing period," hche Research Papers 14, University of Hamburg, Hamburg Center for Health Economics (hche).
    12. Andree Ehlert & Eva García‐Morán, 2022. "Workers' self‐selection into public sector employment: A tale of absenteeism," Kyklos, Wiley Blackwell, vol. 75(3), pages 394-409, August.

  13. Helmut Farbmacher, 2012. "GMM with many weak moment conditions: Replication and application of Newey and Windmeijer (2009)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 343-346, March.

    Cited by:

    1. Andrew Berg & Jonathan D. Ostry & Charalambos G. Tsangarides & Yorbol Yakhshilikov, 2018. "Redistribution, inequality, and growth: new evidence," Journal of Economic Growth, Springer, vol. 23(3), pages 259-305, September.
    2. Asongu, Simplice A. & Rahman, Mushfiqur & Nnanna, Joseph & Haffar, Mohamed, 2020. "Enhancing information technology for value added across economic sectors in Sub-Saharan Africa✰," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    3. Asongu, Simplice & Rahman, Mushfiqur & Nnanna, Joseph & Haffar, Mohamed, 2020. "Enhancing Information Technology for Value Added Across Economic Sectors in Sub-Saharan Africa," MPRA Paper 107238, University Library of Munich, Germany.

  14. Helmut Farbmacher, 2011. "Estimation of hurdle models for overdispersed count data," Stata Journal, StataCorp LLC, vol. 11(1), pages 82-94, March.

    Cited by:

    1. Emely Ek Blæhr & Beatriz Gallo Cordoba & Niels Skipper & Rikke Søgaard, 2024. "Variation in Psychiatric Hospitalisations: A Multiple-Membership Multiple-Classification Analysis," IJERPH, MDPI, vol. 21(8), pages 1-26, July.

Software components

  1. Helmut Farbmacher, 2017. "SIVREG: Stata module to perform adaptive Lasso with some invalid instruments," Statistical Software Components S458394, Boston College Department of Economics, revised 31 Jul 2018.

    Cited by:

    Sorry, no citations of software components recorded.

Chapters

    Sorry, no citations of chapters recorded.
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