Academic literature on the topic 'Instrumental variable (IV) methods'
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Journal articles on the topic "Instrumental variable (IV) methods"
Chesher, Andrew, and Adam M. Rosen. "What Do Instrumental Variable Models Deliver with Discrete Dependent Variables?" American Economic Review 103, no. 3 (May 1, 2013): 557–62. http://dx.doi.org/10.1257/aer.103.3.557.
Full textYuan, Junkun, Anpeng Wu, Kun Kuang, Bo Li, Runze Wu, Fei Wu, and Lanfen Lin. "Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition." ACM Transactions on Knowledge Discovery from Data 16, no. 4 (August 31, 2022): 1–20. http://dx.doi.org/10.1145/3494568.
Full textChalak, Karim. "INSTRUMENTAL VARIABLES METHODS WITH HETEROGENEITY AND MISMEASURED INSTRUMENTS." Econometric Theory 33, no. 1 (February 15, 2016): 69–104. http://dx.doi.org/10.1017/s0266466615000390.
Full textBanerjee, Souvik, and Anirban Basu. "Estimating Endogenous Treatment Effects Using Latent Factor Models with and without Instrumental Variables." Econometrics 9, no. 1 (March 17, 2021): 14. http://dx.doi.org/10.3390/econometrics9010014.
Full textBetz, Timm, Scott J. Cook, and Florian M. Hollenbach. "Spatial interdependence and instrumental variable models." Political Science Research and Methods 8, no. 4 (January 30, 2019): 646–61. http://dx.doi.org/10.1017/psrm.2018.61.
Full textMarshall, John. "Coarsening Bias: How Coarse Treatment Measurement Upwardly Biases Instrumental Variable Estimates." Political Analysis 24, no. 2 (2016): 157–71. http://dx.doi.org/10.1093/pan/mpw007.
Full textBishop, Kelly C., Sehba Husain-Krautter, Jonathan D. Ketcham, Nicolai V. Kuminoff, and Corbett Schimming. "Analyzing Individual-Level Secondary Data with Instrumental Variable Methods Is Useful for Studying the Effects of Air Pollution on Dementia." Journal of Alzheimer's Disease 79, no. 1 (January 5, 2021): 15–23. http://dx.doi.org/10.3233/jad-200497.
Full textFaradiba, Faradiba. "Determination of Climate Factors in Flood and Drought Disaster in Indonesia using Instrumental Variable (IV) Methods." JURNAL ILMU FISIKA | UNIVERSITAS ANDALAS 13, no. 1 (February 28, 2021): 54–61. http://dx.doi.org/10.25077/jif.13.1.54-61.2021.
Full textSaini, Vikram, and Lillie Dewan. "Instrument variable method based on nonlinear transformed instruments for Hammerstein system identification." Journal of Vibration and Control 24, no. 13 (February 22, 2017): 2802–14. http://dx.doi.org/10.1177/1077546317694770.
Full textMogstad, Magne, and Alexander Torgovitsky. "Identification and Extrapolation of Causal Effects with Instrumental Variables." Annual Review of Economics 10, no. 1 (August 2, 2018): 577–613. http://dx.doi.org/10.1146/annurev-economics-101617-041813.
Full textDissertations / Theses on the topic "Instrumental variable (IV) methods"
Abadie, Alberto 1968. "Semiparametric instrumental variable methods for causal response models." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/38857.
Full textIncludes bibliographical references.
This dissertation proposes new instrumental variable methods to identify, estimate and test for causal effects of endogenous treatments. These new methods are distinguished by the combination of nonparametric identifying assumptions and semiparametric estimators that provide a parsimoniuous summary of the results. The thesis consists of three essays presented in the form of chapters. The first chapter shows how to estimate linear and nonlinear causal response functions with covariates under weak (instrumental variable) identification restrictions. The second chapter (co-authored with Joshua Angrist and Guido Imbens) applies the identification results of the first chapter to estimate quantile causal response functions, so we can study the effect of the treatment on different parts of the distribution of the outcome variable. The third chapter of this dissertation looks again at distributional effects but focusing directly on the cumulative distribution functions of the potential outcomes with and without the treatment.
by Alberto Abadie.
Ph.D.
Winkelried, Diego. "Methods to improve the finite sample behaviour of instrumental variable estimators." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609238.
Full textArnarsdottir, Joanna, and Kristina Hansson. "Can Export Diversification Save sub-Saharan Africa from Extreme Weather? : An instrumental variable approach." Thesis, Uppsala universitet, Nationalekonomiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-434996.
Full textGoldsmith, Kimberley. "Instrumental variable and longitudinal structural equation modelling methods for causal mediation : the PACE trial of treatments for chronic fatigue syndrome." Thesis, King's College London (University of London), 2014. https://kclpure.kcl.ac.uk/portal/en/theses/instrumental-variable-and-longitudinal-structural-equation-modelling-methods-for-causal-mediation-the-pace-trial-of-treatments-for-chronic-fatigue-syndrome(413e5fb0-03b9-40bc-b993-0465b1bcbdee).html.
Full textLahlou, Mehdi, and Sebastian Sandstedt. "Where There’s Smoke, There’s Fire : An Analysis of the Riksbank’s Interest Setting Policy." Thesis, Stockholms universitet, Nationalekonomiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-143163.
Full textVilela, Lucas Pimentel. "Wald tests for IV regression with weak instruments." reponame:Repositório Institucional do FGV, 2013. http://hdl.handle.net/10438/11222.
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This dissertation deals with the problem of making inference when there is weak identification in models of instrumental variables regression. More specifically we are interested in one-sided hypothesis testing for the coefficient of the endogenous variable when the instruments are weak. The focus is on the conditional tests based on likelihood ratio, score and Wald statistics. Theoretical and numerical work shows that the conditional t-test based on the two-stage least square (2SLS) estimator performs well even when instruments are weakly correlated with the endogenous variable. The conditional approach correct uniformly its size and when the population F-statistic is as small as two, its power is near the power envelopes for similar and non-similar tests. This finding is surprising considering the bad performance of the two-sided conditional t-tests found in Andrews, Moreira and Stock (2007). Given this counter intuitive result, we propose novel two-sided t-tests which are approximately unbiased and can perform as well as the conditional likelihood ratio (CLR) test of Moreira (2003).
Esta dissertação trata do problema de inferência na presença de identificação fraca em modelos de regresso com variáveis instrumentais. Mais especificamente em testes de hipóteses com relação ao parâmetro da variável endógena quando os instrumentos são fracos. O principal foco é nos testes condicionais unilaterais baseados nas estatísticas de razão de máxima verossimilhança, score e Wald. Resultados teóricos e numéricos mostram que o teste t condicional unilateral baseado no estimador de mínimos quadrados em dois estágios tem uma boa performance mesmo na presença de instrumentos fracamente correlacionados com a variável endógena. A abordagem condicional corrige uniformemente o tamanho do teste t e quando a estatística F populacional é tão pequena quanto dois, o poder do teste é próximo ao power envelope tanto de testes similares quanto de não similares. Tal resultado é surpreendente visto a má performance dos testes t’s condicionais bilaterais relatada em (6, Andrews, Moreira and Stock (2007)). Dado esse resultado aparentemente contra intuitivo, apresentamos novos testes t’s condicionals bilaterais que são aproximadamente não viesados e performam, em alguns casos, tão bem quanto o teste condicional baseado na estatística de razão de verossimilhança de ( 19 , Moreira (2003)).
Brunot, Mathieu. "Identification of rigid industrial robots - A system identification perspective." Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/20776/1/BRUNOT_Mathieu_20776.pdf.
Full textKluberg, Sheryl. "Evaluating South African policies for linkage to and retention in HIV care using quasi-experimental methods." Thesis, 2017. https://hdl.handle.net/2144/27163.
Full textSrna, Jan. "Závislost načasování odchodu do důchodu na existenci vnoučat: evidence s využitím dat SHARE." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-447175.
Full textBooks on the topic "Instrumental variable (IV) methods"
Wickens, M. R. The estimation of linear models with future rational expectations by efficient and instrumental variable methods. Southampton: Department of Economics, University of Southampton, 1985.
Find full textBhattacharya, R. N. Proxy and instrumental variable methods in a regression model with one of the regressors missing. Leicester: University of Leicester, Department of Economics, 1992.
Find full textBook chapters on the topic "Instrumental variable (IV) methods"
Laurain, Vincent, Marion Gilson, and Hugues Garnier. "Refined Instrumental Variable Methods for Hammerstein Box-Jenkins Models." In System Identification, Environmental Modelling, and Control System Design, 27–47. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-974-1_2.
Full textGilson, Marion, Hugues Garnier, Peter C. Young, and Paul Van den Hof. "Instrumental Variable Methods for Closed-loop Continuous-time Model Identification." In Identification of Continuous-time Models from Sampled Data, 133–60. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-161-9_5.
Full textNakashima, Masaharu. "Unconditionally Stable Explicit Difference Schemes for the Variable Coefficients Parabolic Differential Equation (IV)." In Numerical Methods and Applications, 536–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36487-0_61.
Full textAtzeni, Gianfranco, Luca G. Deidda, Marco Delogu, and Dimitri Paolini. "Drop-Out Decisions in a Cohort of Italian Universities." In Teaching, Research and Academic Careers, 71–103. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07438-7_4.
Full textManzi, Hilda, and Joseph P. Gweyi-Onyango. "Agro-ecological Lower Midland Zones IV and V in Kenya Using GIS and Remote Sensing for Climate-Smart Crop Management." In African Handbook of Climate Change Adaptation, 965–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_35.
Full textManzi, Hilda, and Joseph P. Gweyi-Onyango. "Agro-ecological Lower Midland Zones IV and V in Kenya Using GIS and Remote Sensing for Climate-Smart Crop Management." In African Handbook of Climate Change Adaptation, 1–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42091-8_35-1.
Full textBishop, Kelly C., Sehba Husain-Krautter, Jonathan D. Ketcham, Nicolai V. Kuminoff, and Corbett Schimming. "Analyzing Individual-Level Secondary Data with Instrumental Variable Methods Is Useful for Studying the Effects of Air Pollution on Dementia." In Advances in Alzheimer’s Disease. IOS Press, 2021. http://dx.doi.org/10.3233/aiad210044.
Full text"Methods for instrumental variable analysis." In Mendelian Randomization, 59–88. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b18084-9.
Full textSTOICA, PETRE, BENJAMIN FRIEDLANDER, and TORSTEN SÖDERSTRÖM. "Instrumental Variable Methods for ARMA Models." In Control and Dynamic Systems, 79–150. Elsevier, 1987. http://dx.doi.org/10.1016/b978-0-12-012725-2.50006-0.
Full textChesher, Andrew, and Adam M. Rosen. "Generalized instrumental variable models, methods, and applications." In Handbook of Econometrics, 1–110. Elsevier, 2020. http://dx.doi.org/10.1016/bs.hoe.2019.11.001.
Full textConference papers on the topic "Instrumental variable (IV) methods"
McKelvey, Tomas. "Frequency Domain System Identification With Instrumental Variable Based Subspace Algorithm." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/vib-4252.
Full textCheng, Debo, Jiuyong Li, Lin Liu, Jiji Zhang, Thuc Duy Le, and Jixue Liu. "Ancestral Instrument Method for Causal Inference without Complete Knowledge." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/671.
Full textSayed, Ali H. "Time-variant structured matrices: an application to instrumental variable methods." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Franklin T. Luk. SPIE, 1994. http://dx.doi.org/10.1117/12.190863.
Full textGilson, M., H. Garnier, and P. Van den Hof. "Instrumental variable methods for continuous-time model identification in closed-loop." In Proceedings of the 2004 American Control Conference. IEEE, 2004. http://dx.doi.org/10.23919/acc.2004.1383898.
Full textHa, Huong, and James S. Welsh. "Model order selection for continuous time instrumental variable methods using regularization." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402323.
Full textLaurain, V., M. Gilson, and H. Garnier. "Refined instrumental variable methods for identifying hammerstein models operating in closed loop." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400896.
Full textLaurain, V., M. Gilson, H. Garnier, and P. C. Young. "Refined instrumental variable methods for identification of Hammerstein continuous-time Box-Jenkins models." In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4738853.
Full textAbbas, Hossam S. "Closed-Loop Identification of Input-Output LPV Models using Refined Instrumental Variable Methods." In Intelligent Systems and Control. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.744-044.
Full textSalem, Thouraya, Manel Chetoui, and Mohamed Aoun. "Instrumental variable based methods for continuous-time linear parameter varying system identification with fractional models." In 2016 24th Mediterranean Conference on Control and Automation (MED). IEEE, 2016. http://dx.doi.org/10.1109/med.2016.7536043.
Full textLaurain, V., M. Gilson, R. Toth, and H. Garnier. "Identification of LPV output-error and Box-Jenkins models via optimal refined instrumental variable methods." In 2010 American Control Conference (ACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/acc.2010.5530665.
Full textReports on the topic "Instrumental variable (IV) methods"
Rosen, Adam, and Andrew Chesher. Generalized Instrumental Variable Models, Methods, and Applications. The IFS, August 2019. http://dx.doi.org/10.1920/wp.cem.2019.4119.
Full textChesher, Andrew, and Adam Rosen. Generalized instrumental variable models, methods, and applications. The IFS, July 2018. http://dx.doi.org/10.1920/wp.cem.2018.4318.
Full textVandenBerg, R. D., P. B. Kabanov, K E Dewing, and E. A. Atkinson. Geological and geochemical data from the Canadian Arctic Islands, part XVIII: XRF and TOC data, and formation tops in exploration wells from the Devonian clastic wedge and underlying strata, Northwest Territories and Nunavut. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329642.
Full textYogev, David, Ricardo Rosenbusch, Sharon Levisohn, and Eitan Rapoport. Molecular Pathogenesis of Mycoplasma bovis and Mycoplasma agalactiae and its Application in Diagnosis and Control. United States Department of Agriculture, April 2000. http://dx.doi.org/10.32747/2000.7573073.bard.
Full textCastellano, Mike J., Abraham G. Shaviv, Raphael Linker, and Matt Liebman. Improving nitrogen availability indicators by emphasizing correlations between gross nitrogen mineralization and the quality and quantity of labile soil organic matter fractions. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7597926.bard.
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