IDEAS home Printed from https://ideas.repec.org/r/cwl/cwldpp/1417.html
   My bibliography  Save this item

Consistent Estimation with a Large Number of Weak Instruments

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
  2. Costa-Font, Joan & Jiménez-Martín, Sergi & Vilaplana-Prieto, Cristina, 2022. "Do Public Caregiving Subsidies and Supports affect the Provision of Care and Transfers?," Journal of Health Economics, Elsevier, vol. 84(C).
  3. Xiaobo Xu & Jiali Fang & Martin Young & Liping Zou, 2024. "The impact of post‐retirement financial market participation on retirement income sufficiency in Australia," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 903-939, March.
  4. Guisinger, Amy Y., 2020. "Gender differences in the volatility of work hours and labor demand," Journal of Macroeconomics, Elsevier, vol. 66(C).
  5. Liu, Hong & Zhao, Zhong, 2014. "Parental job loss and children's health: Ten years after the massive layoff of the SOEs' workers in China," China Economic Review, Elsevier, vol. 31(C), pages 303-319.
  6. Forchini, Giovanni, 2010. "The Asymptotic Distribution Of The Liml Estimator In A Partially Identified Structural Equation," Econometric Theory, Cambridge University Press, vol. 26(3), pages 917-930, June.
  7. Chirok Han & Peter C. B. Phillips, 2006. "GMM with Many Moment Conditions," Econometrica, Econometric Society, vol. 74(1), pages 147-192, January.
  8. J. Ginger Meng & Gang Hu & Jushan Bai, 2011. "Olive: A Simple Method For Estimating Betas When Factors Are Measured With Error," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 34(1), pages 27-60, March.
  9. Leoncini, Riccardo & Montresor, Sandro & Rentocchini, Francesco, 2016. "CO2-reducing innovations and outsourcing: Evidence from photovoltaics and green construction in North-East Italy," Research Policy, Elsevier, vol. 45(8), pages 1649-1659.
  10. David M. Kaplan & Xin Liu, 2024. "k-Class instrumental variables quantile regression," Empirical Economics, Springer, vol. 67(1), pages 111-141, July.
  11. Marine Carrasco & Guy Tchuente, 2016. "Efficient Estimation with Many Weak Instruments Using Regularization Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1609-1637, December.
  12. Hansen, Christian & Kozbur, Damian, 2014. "Instrumental variables estimation with many weak instruments using regularized JIVE," Journal of Econometrics, Elsevier, vol. 182(2), pages 290-308.
  13. 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.
  14. Tsiboe, Francis & Turner, Dylan, 2023. "The crop insurance demand response to premium subsidies: Evidence from U.S. Agriculture," Food Policy, Elsevier, vol. 119(C).
  15. Nshakira-Rukundo, Emmanuel & Mussa, Essa Chanie & Gerber, Nicolas & von Braun, Joachim, 2020. "Impact of voluntary community-based health insurance on child stunting: Evidence from rural Uganda," Social Science & Medicine, Elsevier, vol. 245(C).
  16. John Chao & Norman Swanson, 2004. "Estimation and Testing Using Jackknife IV in Heteroskedastic Regressions With Many Weak Instruments," Departmental Working Papers 200420, Rutgers University, Department of Economics.
  17. Ribeiro, André L.P. & Hotta, Luiz K., 2013. "An analysis of contagion among Asian countries using the canonical model of contagion," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 62-69.
  18. Wenjie Wang, 2012. "Bootstrapping Anderson-Rubin Statistic and J Statistic in Linear IV Models with Many Instruments," KIER Working Papers 810, Kyoto University, Institute of Economic Research.
  19. Chao, John C. & Swanson, Norman R. & Woutersen, Tiemen, 2023. "Jackknife estimation of a cluster-sample IV regression model with many weak instruments," Journal of Econometrics, Elsevier, vol. 235(2), pages 1747-1769.
  20. Anna Mikusheva & Liyang Sun, 2024. "Weak identification with many instruments," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages -28.
  21. Givens, David, 2013. "Defining governance matters: A factor analytic assessment of governance institutions," Journal of Comparative Economics, Elsevier, vol. 41(4), pages 1026-1053.
  22. Jerry A. Hausman & Whitney K. Newey & Tiemen Woutersen & John C. Chao & Norman R. Swanson, 2012. "Instrumental variable estimation with heteroskedasticity and many instruments," Quantitative Economics, Econometric Society, vol. 3(2), pages 211-255, July.
  23. Kaffo, Maximilien & Wang, Wenjie, 2017. "On bootstrap validity for specification testing with many weak instruments," Economics Letters, Elsevier, vol. 157(C), pages 107-111.
  24. Marine Carrasco & Mohamed Doukali, 2022. "Testing overidentifying restrictions with many instruments and heteroscedasticity using regularised jackknife IV," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 71-97.
  25. Anna Mikusheva & Liyang Sun, 2022. "Inference with Many Weak Instruments," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(5), pages 2663-2686.
  26. Menzel, Konrad, 2014. "Consistent estimation with many moment inequalities," Journal of Econometrics, Elsevier, vol. 182(2), pages 329-350.
  27. Anderson, T.W. & Kunitomo, Naoto & Matsushita, Yukitoshi, 2011. "On finite sample properties of alternative estimators of coefficients in a structural equation with many instruments," Journal of Econometrics, Elsevier, vol. 165(1), pages 58-69.
  28. 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.
  29. Ke-Li Xu & Jui-Chung Yang, 2015. "Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non-stationary Volatility," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 63-86, March.
  30. Channing Arndt & Sam Jones & Finn Tarp, 2009. "Aid and Growth: Have We Come Full Circle?," WIDER Working Paper Series DP2009-05, World Institute for Development Economic Research (UNU-WIDER).
  31. Dennis Lim & Wenjie Wang & Yichong Zhang, 2022. "A Conditional Linear Combination Test with Many Weak Instruments," Papers 2207.11137, arXiv.org, revised Apr 2023.
  32. Antonio Ciccone & Giovanni Peri, 2005. "Long-Run Substitutability Between More and Less Educated Workers: Evidence from U.S. States, 1950-1990," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 652-663, November.
  33. Liu, Xiaodong & Lee, Lung-fei, 2010. "GMM estimation of social interaction models with centrality," Journal of Econometrics, Elsevier, vol. 159(1), pages 99-115, November.
  34. Manuel Frondel & Colin Vance, 2018. "Drivers’ response to fuel taxes and efficiency standards: evidence from Germany," Transportation, Springer, vol. 45(3), pages 989-1001, May.
  35. Phillips, Peter C.B., 2014. "Optimal estimation of cointegrated systems with irrelevant instruments," Journal of Econometrics, Elsevier, vol. 178(P2), pages 210-224.
  36. Agan, Amanda & Doleac, Jennifer & Harvey, Anna, 2021. "Misdemeanor Prosecution," IZA Discussion Papers 14234, Institute of Labor Economics (IZA).
  37. Lim, Dennis & Wang, Wenjie & Zhang, Yichong, 2024. "A conditional linear combination test with many weak instruments," Journal of Econometrics, Elsevier, vol. 238(2).
  38. Kazuhiko Hayakawa, 2006. "Efficient GMM Estimation of Dynamic Panel Data Models Where Large Heterogeneity May Be Present," Hi-Stat Discussion Paper Series d05-130, Institute of Economic Research, Hitotsubashi University.
  39. Huntington-Klein Nick, 2020. "Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 182-208, January.
  40. Wang, Wenjie, 2021. "Wild Bootstrap for Instrumental Variables Regression with Weak Instruments and Few Clusters," MPRA Paper 106227, University Library of Munich, Germany.
  41. Kussel, Gerhard & Frondel, Manuel, 2016. "Switching Response to Power Prices: Evidence from German Households," VfS Annual Conference 2016 (Augsburg): Demographic Change 145728, Verein für Socialpolitik / German Economic Association.
  42. Mehmet Caner, 2005. "Higher Order Expansions in GMM with Nearly Weak and Many Nearly Weak Instruments," Working Paper 209, Department of Economics, University of Pittsburgh, revised Jan 2005.
  43. Doko Tchatoka, Firmin Sabro & Dufour, Jean-Marie, 2008. "Instrument endogeneity and identification-robust tests: some analytical results," MPRA Paper 29613, University Library of Munich, Germany.
  44. Annamaria Conti & Jerry Thursby & Marie Thursby, 2013. "Patents as Signals for Startup Financing," Journal of Industrial Economics, Wiley Blackwell, vol. 61(3), pages 592-622, September.
  45. Nam-Hyun Kim & Winfried Pohlmeier, 2015. "A Regularization Approach to Biased Two-Stage Least Squares Estimation," Working Paper series 15-22, Rimini Centre for Economic Analysis.
  46. Qingliang Fan & Wei Zhong, 2018. "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(3), pages 388-399, July.
  47. 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.
  48. Dakyung Seong, 2022. "Binary response model with many weak instruments," Papers 2201.04811, arXiv.org, revised Jun 2024.
  49. 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.
  50. Alessandro Gambini & Alberto Zazzaro, 2013. "Long-lasting bank relationships and growth of firms," Small Business Economics, Springer, vol. 40(4), pages 977-1007, May.
  51. Zhentao Shi, 2016. "Estimation of Sparse Structural Parameters with Many Endogenous Variables," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1582-1608, December.
  52. Manuel Frondel and Gerhard Kussel, 2019. "Switching on Electricity Demand Response: Evidence for German Households," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
  53. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
  54. Andrew Shephard & Xu Cheng & Alejándro Sanchez-Becerra, 2023. "How to weight in moments matchings: A new approach and applications to earnings dynamics," CeMMAP working papers 13/23, Institute for Fiscal Studies.
  55. 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.
  56. Cizek, P., 2009. "Generalized Methods of Trimmed Moments," Discussion Paper 2009-25, Tilburg University, Center for Economic Research.
  57. Phillips, Peter C.B., 2003. "Vision And Influence In Econometrics: John Denis Sargan," Econometric Theory, Cambridge University Press, vol. 19(3), pages 495-511, June.
  58. Stanislav Anatolyev, 2007. "Inference about predictive ability when there are many predictors," Working Papers w0096, New Economic School (NES).
  59. 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.
  60. Ashish Patel & Dipender Gill & Paul Newcombe & Stephen Burgess, 2023. "Conditional inference in cis‐Mendelian randomization using weak genetic factors," Biometrics, The International Biometric Society, vol. 79(4), pages 3458-3471, December.
  61. Stanislav Anatolyev, 2013. "Instrumental variables estimation and inference in the presence of many exogenous regressors," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 27-72, February.
  62. Caner, Mehmet, 2014. "Near exogeneity and weak identification in generalized empirical likelihood estimators: Many moment asymptotics," Journal of Econometrics, Elsevier, vol. 182(2), pages 247-268.
  63. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
  64. Phillips, Peter C.B. & Gao, Wayne Yuan, 2017. "Structural inference from reduced forms with many instruments," Journal of Econometrics, Elsevier, vol. 199(2), pages 96-116.
  65. Daniel A. Ackerberg & Paul J. Devereux, 2009. "Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 91(2), pages 351-362, May.
  66. MacKinnon, James G., 2023. "Fast cluster bootstrap methods for linear regression models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 52-71.
  67. Tom Boot & Johannes W. Ligtenberg, 2023. "Identification- and many instrument-robust inference via invariant moment conditions," Papers 2303.07822, arXiv.org, revised Sep 2023.
  68. Arel-Bundock, Vincent, 2013. "A solution to the weak instrument bias in 2SLS estimation: Indirect inference with stochastic approximation," Economics Letters, Elsevier, vol. 120(3), pages 495-498.
  69. Okui, Ryo, 2011. "Instrumental variable estimation in the presence of many moment conditions," Journal of Econometrics, Elsevier, vol. 165(1), pages 70-86.
  70. Matsushita, Yukitoshi & Otsu, Taisuke, 2024. "A jackknife Lagrange multiplier test with many weak instruments," LSE Research Online Documents on Economics 116392, London School of Economics and Political Science, LSE Library.
  71. Bai, Zefeng, 2021. "Does robo-advisory help reduce the likelihood of carrying a credit card debt? Evidence from an instrumental variable approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
  72. Peter C. B. Phillips, 2017. "Reduced forms and weak instrumentation," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 818-839, October.
  73. John C. Chao & Norman R. Swanson, 2005. "Consistent Estimation with a Large Number of Weak Instruments," Econometrica, Econometric Society, vol. 73(5), pages 1673-1692, September.
  74. Antoine, Bertille & Lavergne, Pascal, 2014. "Conditional moment models under semi-strong identification," Journal of Econometrics, Elsevier, vol. 182(1), pages 59-69.
  75. Guggenberger, Patrik & Smith, Richard J., 2008. "Generalized empirical likelihood tests in time series models with potential identification failure," Journal of Econometrics, Elsevier, vol. 142(1), pages 134-161, January.
  76. Helmut Farbmacher & Rebecca Groh & Michael Muhlegger & Gabriel Vollert, 2024. "Revisiting the Many Instruments Problem using Random Matrix Theory," Papers 2408.08580, arXiv.org.
  77. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "A specification test for the strength of instrumental variables," Papers 2302.14396, arXiv.org.
  78. Tetsuya Kaji, 2019. "Theory of Weak Identification in Semiparametric Models," Papers 1908.10478, arXiv.org, revised Aug 2020.
  79. Chao, John C. & Hausman, Jerry A. & Newey, Whitney K. & Swanson, Norman R. & Woutersen, Tiemen, 2014. "Testing overidentifying restrictions with many instruments and heteroskedasticity," Journal of Econometrics, Elsevier, vol. 178(P1), pages 15-21.
  80. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
  81. 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.
  82. Guy Tchuente, 2016. "Estimation of social interaction models using regularization," Studies in Economics 1607, School of Economics, University of Kent.
  83. Matsushita, Yukitoshi & Otsu, Taisuke, 2020. "Jackknife empirical likelihood: small bandwidth, sparse network and high-dimension asymptotic," LSE Research Online Documents on Economics 106488, London School of Economics and Political Science, LSE Library.
  84. Amann, Juergen & Cantore, Nicola & Calí, Massimiliano & Todorov, Valentin & Cheng, Charles Fang Chin, 2021. "Switching it up: The effect of energy price reforms in Oman," World Development, Elsevier, vol. 142(C).
  85. John C. Chao & Norman R. Swanson, 2003. "Asymptotic Normality of Single-Equation Estimators for the Case with a Large Number of Weak Instruments," Departmental Working Papers 200312, Rutgers University, Department of Economics.
  86. Huntington-Klein Nick, 2020. "Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 182-208, January.
  87. Balan, David J. & Knack, Stephen, 2012. "The correlation between human capital and morality and its effect on economic performance: Theory and evidence," Journal of Comparative Economics, Elsevier, vol. 40(3), pages 457-475.
  88. repec:zbw:rwirep:0445 is not listed on IDEAS
  89. Maurice Bun & Frank Windmeijer, 2010. "A comparison of bias approximations for the 2SLS estimator," CeMMAP working papers CWP07/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  90. Han, Chirok, 2008. "Detecting invalid instruments using L1-GMM," Economics Letters, Elsevier, vol. 101(3), pages 285-287, December.
  91. Kirill S. Evdokimov & Michal Kolesár, 2018. "Inference in Instrumental Variable Regression Analysis with Heterogeneous Treatment Effects," Working Papers 2018-16, Princeton University. Economics Department..
  92. Anatolyev, Stanislav & Gospodinov, Nikolay, 2011. "Specification Testing In Models With Many Instruments," Econometric Theory, Cambridge University Press, vol. 27(2), pages 427-441, April.
  93. repec:wyi:journl:002148 is not listed on IDEAS
  94. Muhammad Qasim, 2024. "A weighted average limited information maximum likelihood estimator," Statistical Papers, Springer, vol. 65(5), pages 2641-2666, July.
  95. Kapetanios, George & Marcellino, Massimiliano, 2010. "Cross-sectional averaging and instrumental variable estimation with many weak instruments," Economics Letters, Elsevier, vol. 108(1), pages 36-39, July.
  96. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.
  97. Cattaneo, Matias D. & Crump, Richard K. & Jansson, Michael, 2012. "Optimal inference for instrumental variables regression with non-Gaussian errors," Journal of Econometrics, Elsevier, vol. 167(1), pages 1-15.
  98. Keisuke Hirano & Jack R. Porter, 2015. "Location Properties of Point Estimators in Linear Instrumental Variables and Related Models," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 720-733, December.
  99. Hansen, Christian & Hausman, Jerry & Newey, Whitney, 2008. "Estimation With Many Instrumental Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 398-422.
  100. Yukitoshi Matsushita & Taisuke Otsu, 2019. "Jackknife, small bandwidth and high-dimensional asymptotics," STICERD - Econometrics Paper Series 605, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  101. D. S. Poskitt & C. L. Skeels, 2004. "Approximating the Distribution of the Instrumental Variables Estimator when the Concentration Parameter is Small," Monash Econometrics and Business Statistics Working Papers 19/04, Monash University, Department of Econometrics and Business Statistics.
  102. Kun Xu & Yanyuan Ma & Liqun Wang, 2015. "Instrument Assisted Regression for Errors in Variables Models with Binary Response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 104-117, March.
  103. Cai, Zongwu & Fang, Ying & Su, Jia, 2012. "Reducing asymptotic bias of weak instrumental estimation using independently repeated cross-sectional information," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 180-185.
  104. Eric Gautier & Christiern Rose, 2022. "Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments," Papers 2211.02249, arXiv.org, revised Nov 2022.
  105. Yoonseok Lee & Ryo Okui, 2009. "A Specification Test for Instrumental Variables Regression with Many Instruments," Cowles Foundation Discussion Papers 1741, Cowles Foundation for Research in Economics, Yale University.
  106. 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.
  107. Licheng Xu & Xiaodong Du, 2022. "Land certification, rental market participation, and household welfare in rural China," Agricultural Economics, International Association of Agricultural Economists, vol. 53(1), pages 52-71, January.
  108. Hsu, Shih-Hsun & Kuan, Chung-Ming, 2011. "Estimation of conditional moment restrictions without assuming parameter identifiability in the implied unconditional moments," Journal of Econometrics, Elsevier, vol. 165(1), pages 87-99.
  109. Carrasco, Marine & Tchuente, Guy, 2015. "Regularized LIML for many instruments," Journal of Econometrics, Elsevier, vol. 186(2), pages 427-442.
  110. Yoonseok Lee & Yu Zhou, 2015. "Averaged Instrumental Variables Estimators," Center for Policy Research Working Papers 180, Center for Policy Research, Maxwell School, Syracuse University.
  111. Martins, Luis F. & Gabriel, Vasco J., 2014. "Linear instrumental variables model averaging estimation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 709-724.
  112. Laszlo, Sonia, 2008. "Education, Labor Supply, and Market Development in Rural Peru," World Development, Elsevier, vol. 36(11), pages 2421-2439, November.
  113. Xu, Licheng & Du, Xiaodong, 2020. "Land certification, rental market participation, and income dynamics in rural China," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304247, Agricultural and Applied Economics Association.
  114. Manuel Frondel & Colin Vance, 2013. "Fuel Taxes versus Efficiency Standards – An Instrumental Variable Approach," Ruhr Economic Papers 0445, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  115. Michal Kolesár, 2013. "Estimation in an Instrumental Variables Model With Treatment Effect Heterogeneity," Working Papers 2013-2, Princeton University. Economics Department..
  116. D.S. Poskitt & C.L. Skeels, 2005. "Small Concentration Asymptotics and Instrumental Variables Inference," Department of Economics - Working Papers Series 948, The University of Melbourne.
  117. Calhoun, Gray, 2011. "Hypothesis testing in linear regression when k/n is large," Journal of Econometrics, Elsevier, vol. 165(2), pages 163-174.
  118. T. W. Anderson & Naoto Kunitomo & Yukitoshi Matsushita, 2008. "On the Asymptotic Optimality of the LIML Estimator with Possibly Many Instruments," CIRJE F-Series CIRJE-F-542, CIRJE, Faculty of Economics, University of Tokyo.
  119. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
  120. 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.
  121. Carlos Velasco & Xuexin Wang, 2021. "Instrumental variable estimation via a continuum of instruments with an application to estimating the elasticity of intertemporal substitution in consumption," Working Papers 2024-09-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
  122. James H. Stock & Motohiro Yogo, 2002. "Testing for Weak Instruments in Linear IV Regression," NBER Technical Working Papers 0284, National Bureau of Economic Research, Inc.
  123. Imbens, Guido W., 2014. "Instrumental Variables: An Econometrician's Perspective," IZA Discussion Papers 8048, Institute of Labor Economics (IZA).
  124. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
  125. Bekker, Paul A. & Crudu, Federico, 2015. "Jackknife instrumental variable estimation with heteroskedasticity," Journal of Econometrics, Elsevier, vol. 185(2), pages 332-342.
  126. Wang, Wenjie & Doko Tchatoka, Firmin, 2018. "On Bootstrap inconsistency and Bonferroni-based size-correction for the subset Anderson–Rubin test under conditional homoskedasticity," Journal of Econometrics, Elsevier, vol. 207(1), pages 188-211.
  127. Yankuo Qiao, 2023. "To delegate or not to delegate? On the quality of voluntary corporate financial disclosure," Review of Managerial Science, Springer, vol. 17(7), pages 2215-2250, October.
  128. Channing Arndt & Sam Jones & Finn Tarp, 2009. "Aid and Growth: Have We Come Full Circle?," Discussion Papers 09-22, University of Copenhagen. Department of Economics.
  129. Caner, Mehmet & Yıldız, Neşe, 2012. "CUE with many weak instruments and nearly singular design," Journal of Econometrics, Elsevier, vol. 170(2), pages 422-441.
  130. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  131. Julien Forder & Juliette Malley & Ann‐Marie Towers & Ann Netten, 2014. "Using Cost‐Effectiveness Estimates From Survey Data To Guide Commissioning: An Application To Home Care," Health Economics, John Wiley & Sons, Ltd., vol. 23(8), pages 979-992, August.
  132. Wang, Wenjie, 2020. "On Bootstrap Validity for the Test of Overidentifying Restrictions with Many Instruments and Heteroskedasticity," MPRA Paper 104858, University Library of Munich, Germany.
  133. Jochmans, Koen, 2023. "Many (Weak) Judges in Judge-Leniency Designs," TSE Working Papers 23-1481, Toulouse School of Economics (TSE).
  134. Guay, Alain & Guerre, Emmanuel & Lazarová, Štěpána, 2013. "Robust adaptive rate-optimal testing for the white noise hypothesis," Journal of Econometrics, Elsevier, vol. 176(2), pages 134-145.
  135. Chao, John C. & Swanson, Norman R. & Hausman, Jerry A. & Newey, Whitney K. & Woutersen, Tiemen, 2012. "Asymptotic Distribution Of Jive In A Heteroskedastic Iv Regression With Many Instruments," Econometric Theory, Cambridge University Press, vol. 28(1), pages 42-86, February.
  136. Whitney K. Newey & Frank Windmeijer, 2005. "GMM with many weak moment conditions," CeMMAP working papers CWP18/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  137. Andreas Pick, 2007. "Financial contagion and tests using instrumental variables," DNB Working Papers 139, Netherlands Central Bank, Research Department.
  138. Johannes W. Ligtenberg, 2023. "Inference in IV models with clustered dependence, many instruments and weak identification," Papers 2306.08559, arXiv.org, revised Mar 2024.
  139. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
  140. T. W. Anderson & Naoto Kunitomo & Yukitoshi Matsushita, 2006. "A New Light from Old Wisdoms : Alternative Estimation Methods of Simultaneous Equations with Possibly Many Instruments," CIRJE F-Series CIRJE-F-399, CIRJE, Faculty of Economics, University of Tokyo.
  141. Francis,David C. & Karalashvili,Nona & Murrell,Peter, 2022. "Transactional Governance Structures : New Cross-Country Data and an Application tothe Effect of Uncertainty," Policy Research Working Paper Series 10118, The World Bank.
  142. Guy Tchuente, 2019. "Weak Identification and Estimation of Social Interaction Models," Papers 1902.06143, arXiv.org.
  143. Brick, Ivan E. & Palia, Darius, 2007. "Evidence of jointness in the terms of relationship lending," Journal of Financial Intermediation, Elsevier, vol. 16(3), pages 452-476, July.
  144. 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 of Labor Economics (IZA).
  145. Peter C. B. Phillips, 2022. "An Econometrician amongst Statisticians: T. W. Anderson," Cowles Foundation Discussion Papers 2333, Cowles Foundation for Research in Economics, Yale University.
  146. Anderson, T.W. & Kunitomo, Naoto & Matsushita, Yukitoshi, 2010. "On the asymptotic optimality of the LIML estimator with possibly many instruments," Journal of Econometrics, Elsevier, vol. 157(2), pages 191-204, August.
  147. Gérard P. Cachon & Santiago Gallino & Marcelo Olivares, 2019. "Does Adding Inventory Increase Sales? Evidence of a Scarcity Effect in U.S. Automobile Dealerships," Management Science, INFORMS, vol. 65(4), pages 1469-1485, April.
  148. Zhonghua Liu & Ting Ye & Baoluo Sun & Mary Schooling & Eric Tchetgen Tchetgen, 2023. "Mendelian randomization mixed‐scale treatment effect robust identification and estimation for causal inference," Biometrics, The International Biometric Society, vol. 79(3), pages 2208-2219, September.
  149. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "The First-stage F Test with Many Weak Instruments," Papers 2302.14423, arXiv.org, revised Sep 2024.
  150. Mehmet Caner, 2006. "Near Exogeneity and Weak Identification in Generlized Empirical Likelihood estimators : Fixed and Many Moment Asymptotics," Working Paper 212, Department of Economics, University of Pittsburgh, revised Jan 2006.
  151. Kolesár, Michal, 2018. "Minimum distance approach to inference with many instruments," Journal of Econometrics, Elsevier, vol. 204(1), pages 86-100.
  152. Qinqin Hu & Lu Lin, 2022. "Feature Screening in High Dimensional Regression with Endogenous Covariates," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 949-969, October.
  153. Marine Carrasco & Guy Tchuente, 2016. "Regularization Based Anderson Rubin Tests for Many Instruments," Studies in Economics 1608, School of Economics, University of Kent.
  154. Rigdon, Joseph & Berkowitz, Seth A. & Seligman, Hilary K. & Basu, Sanjay, 2017. "Re-evaluating associations between the Supplemental Nutrition Assistance Program participation and body mass index in the context of unmeasured confounders," Social Science & Medicine, Elsevier, vol. 192(C), pages 112-124.
  155. repec:wyi:journl:002137 is not listed on IDEAS
  156. Frondel, Manuel & Vance, Colin, 2013. "Fuel Taxes versus Efficiency Standards – An Instrumental Variable Approach," Ruhr Economic Papers 445, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  157. Eriksen, Michael D., 2010. "Homeownership subsidies and the marriage decisions of low-income households," Regional Science and Urban Economics, Elsevier, vol. 40(6), pages 490-497, November.
  158. Chirok Han & Hyoungjong Kim, 2023. "Dynamic panel GMM estimators with improved finite sample properties using parametric restrictions for dimension reduction," Empirical Economics, Springer, vol. 64(6), pages 2589-2610, June.
  159. Tom Boot & Didier Nibbering, 2024. "Inference on LATEs with covariates," Papers 2402.12607, arXiv.org.
  160. Florens, Jean-Pierre & Van Bellegem, Sébastien, 2015. "Instrumental variable estimation in functional linear models," Journal of Econometrics, Elsevier, vol. 186(2), pages 465-476.
  161. Claudia R. Williamson & Carrie B. Kerekes, 2011. "Securing Private Property: Formal versus Informal Institutions," Journal of Law and Economics, University of Chicago Press, vol. 54(3), pages 537-572.
  162. Antoine, Bertille & Renault, Eric, 2024. "GMM with Nearly-Weak Identification," Econometrics and Statistics, Elsevier, vol. 30(C), pages 36-59.
  163. Poskitt, D.S. & Skeels, C.L., 2007. "Approximating the distribution of the two-stage least squares estimator when the concentration parameter is small," Journal of Econometrics, Elsevier, vol. 139(1), pages 217-236, July.
  164. Wang, Wenjie & Zhang, Yichong, 2024. "Wild bootstrap inference for instrumental variables regressions with weak and few clusters," Journal of Econometrics, Elsevier, vol. 241(1).
  165. Antonio Ciccone & Giovanni Peri, 2003. "Skills’ Substitutability and Technological Progress: U.S. States 1950-1990," CESifo Working Paper Series 1024, CESifo.
  166. Liu, Xiaodong, 2012. "On the consistency of the LIML estimator of a spatial autoregressive model with many instruments," Economics Letters, Elsevier, vol. 116(3), pages 472-475.
  167. Van Bellegem, Sébastien & Florens, Jean-Pierre, 2014. "Instrumental variable estimation in functional linear models," LIDAM Discussion Papers CORE 2014056, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  168. Namhyun Kim & Winfried Pohlmeier, 2016. "A Note on the Regularized Approach to Biased 2SLS Estimation with Weak Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(6), pages 915-924, December.
  169. Sølvsten, Mikkel, 2020. "Robust estimation with many instruments," Journal of Econometrics, Elsevier, vol. 214(2), pages 495-512.
  170. T. W. Anderson & Naoto Kunitomo & Yukitoshi Matsushita, 2008. "On Finite Sample Properties of Alternative Estimators of Coefficients in a Structural Equation with Many Instruments," CIRJE F-Series CIRJE-F-577, CIRJE, Faculty of Economics, University of Tokyo.
  171. Guggenberger, Patrik & Ramalho, Joaquim J.S. & Smith, Richard J., 2012. "GEL statistics under weak identification," Journal of Econometrics, Elsevier, vol. 170(2), pages 331-349.
  172. Bun, Maurice J.G. & Windmeijer, Frank, 2011. "A comparison of bias approximations for the two-stage least squares (2SLS) estimator," Economics Letters, Elsevier, vol. 113(1), pages 76-79, October.
  173. Vance, Colin & Frondel, Manuel, 2015. "From fuel taxation to efficiency standards: A wrong turn in European climate protection?," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113171, Verein für Socialpolitik / German Economic Association.
  174. Dobbelaere, Sabien, 2004. "Ownership, firm size and rent sharing in Bulgaria," Labour Economics, Elsevier, vol. 11(2), pages 165-189, April.
  175. Marcellino, Massimiliano & Kapetanios, George & Khalaf, Lynda, 2015. "Factor based identification-robust inference in IV regressions," CEPR Discussion Papers 10390, C.E.P.R. Discussion Papers.
  176. repec:bla:ecorec:v:91:y:2015:i::p:1-24 is not listed on IDEAS
  177. Lee, Nayoung & Moon, Hyungsik Roger & Zhou, Qiankun, 2017. "Many IVs estimation of dynamic panel regression models with measurement error," Journal of Econometrics, Elsevier, vol. 200(2), pages 251-259.
  178. Thomas Wiemann, 2023. "Optimal Categorical Instrumental Variables," Papers 2311.17021, arXiv.org, revised May 2024.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.