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Displaying results 1 to 13 of 13.

  1. Identification of causal effects with multiple instruments
    problems and some solutions
    Author: Mogstad, Magne; Torgovitsky, Alexander; Walters, Christopher R.
    Published: March 2019
    Publisher:  National Bureau of Economic Research, Cambridge, MA

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    W 1 (25691)
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Print
    Series: Working paper series / National Bureau of Economic Research ; 25691
    Subjects: Kausalanalyse; IV-Schätzung
    Scope: 45 Seiten, Illustrationen
    Notes:

    Erscheint auch als Online-Ausgabe
  2. Nonparametric estimates of demand in the California health insurance exchange
    Author: Tebaldi, Pietro; Torgovitsky, Alexander; Yang, Hanbin
    Published: May 2019
    Publisher:  National Bureau of Economic Research, Cambridge, MA

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Source: Union catalogues
    Language: English
    Media type: Book
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    Series: Working paper series / National Bureau of Economic Research ; 25827
    Subjects: Gesetzliche Krankenversicherung; Diskrete Entscheidung; Schätzung; Logit-Modell; Kalifornien
    Scope: 27 Seiten, Illustrationen
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    Erscheint auch als Online-Ausgabe
  3. Combining matching and synthetic controls to trade off biases from extrapolation and interpolation
    Author: Kellogg, Maxwell; Mogstad, Magne; Pouliot, Guillaume; Torgovitsky, Alexander
    Published: January 2020
    Publisher:  National Bureau of Economic Research, Cambridge, MA

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Language: English
    Media type: Book
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    Series: Working paper series / National Bureau of Economic Research ; 26624
    Subjects: Kausalanalyse; Lineare Regression; Matching; Monte-Carlo-Simulation; Schätztheorie
    Scope: 28 Seiten, Illustrationen
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    Erscheint auch als Online-Ausgabe
  4. ivcrc
    an instrumental variables estimator for the correlated random coefficients model
    Author: Benson, David; Masten, Matthew A.; Torgovitsky, Alexander
    Published: June 10, 2020
    Publisher:  Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board, Washington, D.C.

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    Language: English
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    Series: Finance and economics discussion series ; 2020, 046
    Subjects: ivregress; Instrumental Variables; Correlated Random Coefficients; Heterogeneous Treatment Effects; Varying Coefficient Models; Returns to Schooling
    Scope: 1 Online-Ressource (circa 27 Seiten)
  5. Selection in Surveys
    Author: Dutz, Deniz; Huitfeldt, Ingrid; Lacouture, Santiago; Mogstad, Magne; Torgovitsky, Alexander; Van Dijk, Winnie L.
    Published: 2021
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    We evaluate how nonresponse affects conclusions drawn from survey data and consider how researchers can reliably test and correct for nonresponse bias. To do so, we examine a survey on labor market conditions during the COVID-19 pandemic that used... more

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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    We evaluate how nonresponse affects conclusions drawn from survey data and consider how researchers can reliably test and correct for nonresponse bias. To do so, we examine a survey on labor market conditions during the COVID-19 pandemic that used randomly assigned financial incentives to encourage participation. We link the survey data to administrative data sources, allowing us to observe a ground truth for participants and nonparticipants. We find evidence of large nonresponse bias, even after correcting for observable differences between participants and nonparticipants. We apply a range of existing methods that account for nonresponse bias due to unobserved differences, including worst-case bounds, bounds that incorporate monotonicity assumptions, and approaches based on parametric and nonparametric selection models. These methods produce bounds (or point estimates) that are either too wide to be useful or far from the ground truth. We show how these shortcomings can be addressed by modeling how nonparticipation can be both active (declining to participate) and passive (not seeing the survey invitation). The model makes use of variation from the randomly assigned financial incentives, as well as the timing of reminder emails. Applying the model to our data produces bounds (or point estimates) that are narrower and closer to the ground truth than the other methods

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
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    Series: NBER working paper series ; no. w29549
    Subjects: Erhebungstechnik; Coronavirus; Wirtschaftslage; Befragung; USA
    Scope: 1 Online-Ressource, illustrations (black and white)
    Notes:

    Hardcopy version available to institutional subscribers
  6. Selection in surveys
    Author: Dutz, Deniz; Huitfeldt, Ingrid; Lacouture, Santiago; Mogstad, Magne; Torgovitsky, Alexander; Van Dijk, Winnie L.
    Published: December 2021
    Publisher:  Statistics Norway, Research Department, Oslo

    We evaluate how nonresponse affects conclusions drawn from survey data and consider how researchers can reliably test and correct for nonresponse bias. To do so, we examine a survey on labor market conditions during the COVID-19 pandemic that used... more

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 619
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    We evaluate how nonresponse affects conclusions drawn from survey data and consider how researchers can reliably test and correct for nonresponse bias. To do so, we examine a survey on labor market conditions during the COVID-19 pandemic that used randomly assigned financial incentives to encourage participation. We link the survey data to administrative data sources, allowing us to observe a ground truth for participants and nonparticipants. We find evidence of large nonresponse bias, even after correcting for observable differences between participants and nonparticipants. We apply a range of existing methods that account for nonresponse bias due to unobserved differences, including worst-case bounds, bounds that incorporate monotonicity assumptions, and approaches based on parametric and nonparametric selection models. These methods produce bounds (or point estimates) that are either too wide to be useful or far from the ground truth. We show how these shortcomings can be addressed by modeling how nonparticipation can be both active (declining to participate) and passive (not seeing the survey invitation). The model makes use of variation from the randomly assigned financial incentives, as well as the timing of reminder emails. Applying the model to our data produces bounds (or point estimates) that are narrower and closer to the ground truth than the other methods.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/250138
    Series: Discussion papers / Statistics Norway, Research Department ; 971
    Subjects: survey; nonresponse; nonresponse bias
    Scope: 1 Online-Ressource (circa 95 Seiten), Illustrationen
  7. When is TSLS Actually LATE?
    Author: Blandhol, Christine; Bonney, John; Mogstad, Magne; Torgovitsky, Alexander
    Published: 2022
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    Linear instrumental variable estimators, such as two-stage least squares (TSLS), are commonly interpreted as estimating positively weighted averages of causal effects, referred to as local average treatment effects (LATEs). We examine whether the... more

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    Universitätsbibliothek Freiburg
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    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    Linear instrumental variable estimators, such as two-stage least squares (TSLS), are commonly interpreted as estimating positively weighted averages of causal effects, referred to as local average treatment effects (LATEs). We examine whether the LATE interpretation actually applies to the types of TSLS specifications that are used in practice. We show that if the specification includes covariates, which most empirical work does, then the LATE interpretation does not apply in general. Instead, the TSLS estimator will in general reflect treatment effects for both compliers and always/never-takers, and some of the treatment effects for the always/never-takers will necessarily be negatively weighted. We show that the only specifications that have a LATE interpretation are "saturated" specifications that control for covariates nonparametrically, implying that such specifications are both sufficient and necessary for TSLS to have a LATE interpretation, at least without additional parametric assumptions. This result is concerning because, as we document, empirical researchers almost never control for covariates nonparametrically, and rarely discuss or justify parametric specifications of covariates. We develop a decomposition that quantifies the extent to which the usual LATE interpretation fails. We apply the decomposition to four empirical analyses and find strong evidence that the LATE interpretation of TSLS is far from accurate for the types of specifications actually used in practice

     
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    Source: Union catalogues
    Language: English
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    Format: Online
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    Series: NBER working paper series ; no. w29709
    Subjects: Kausalanalyse; Kleinste-Quadrate-Methode; IV-Schätzung; Nichtparametrische Schätzung; Schätztheorie
    Scope: 1 Online-Ressource, illustrations (black and white)
    Notes:

    Hardcopy version available to institutional subscribers
  8. What Drives (Gaps in) Scientific Study Participation? Evidence from a COVID-19 Antibody Survey
    Author: Dutz, Deniz; Greenstone, Michael; Hortaçsu, Ali; Lacouture, Santiago; Mogstad, Magne; Shaikh, Azeem M.; Torgovitsky, Alexander; Van Dijk, Winnie L.
    Published: January 2023
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    Underrepresentation of minority and poor households in scientific studies undermines policy decisions and public health. We study data from a serological study that randomized participation incentives. Participation is low (6% at $0, 17% at $100, 29%... more

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    Universitätsbibliothek Freiburg
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    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
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    Underrepresentation of minority and poor households in scientific studies undermines policy decisions and public health. We study data from a serological study that randomized participation incentives. Participation is low (6% at $0, 17% at $100, 29% at $500) and unequal: minority and poor households are underrepresented at low incentive levels. We develop a framework for disentangling non-contact and ``participation hesitancy'' in explaining non-participation. We find that underrepresentation occurs because poor and minority households are more hesitant, not because they are harder to contact. The $500 incentive appears to overcome differences in hesitancy and restore representativeness along observable dimensions

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: NBER working paper series ; no. w30880
    Subjects: Forschung; Coronavirus; Erhebungstechnik; Minderheit; Armut; Soziale Lage; USA; Survey Methods; Survey Methods; Sampling Methods; Health; Health and Inequality; General; Innovation and Invention: Processes and Incentives
    Scope: 1 Online-Ressource, illustrations (black and white)
    Notes:

    Hardcopy version available to institutional subscribers
  9. Policy evaluation with multiple instrumental variables
    Author: Mogstad, Magne; Torgovitsky, Alexander; Walters, Christopher R.
    Published: July 2020
    Publisher:  National Bureau of Economic Research, Cambridge, MA

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    W 1 (27546)
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    Series: Working paper series / National Bureau of Economic Research ; 27546
    Subjects: Kausalanalyse; IV-Schätzung
    Scope: 32 Seiten, Illustrationen
    Notes:

    Erscheint auch als Online-Ausgabe
  10. Instrumental Variables with Unobserved Heterogeneity in Treatment Effects
    Author: Mogstad, Magne; Torgovitsky, Alexander
    Published: September 2024
    Publisher:  National Bureau of Economic Research, Cambridge, Mass

    This chapter synthesizes and critically reviews the modern instrumental variables (IV) literature that allows for unobserved heterogeneity in treatment effects (UHTE). We start by discussing why UHTE is often an essential aspect of IV applications in... more

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    Universitätsbibliothek Freiburg
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    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
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    This chapter synthesizes and critically reviews the modern instrumental variables (IV) literature that allows for unobserved heterogeneity in treatment effects (UHTE). We start by discussing why UHTE is often an essential aspect of IV applications in economics and we explain the conceptual challenges raised by allowing for it. Then we review and survey two general strategies for incorporating UHTE. The first strategy is to continue to use linear IV estimators designed for classical constant (homogeneous) treatment effect models, acknowledge their likely misspecification, and attempt to reverse engineer an attractive interpretation in the presence of UHTE. This strategy commonly leads to interpretations of linear IV that involve local average treatment effects (LATEs). We review the various ways in which the use and justification of LATE interpretations have expanded and contracted since their introduction in the early 1990s. The second strategy is to forward engineer new estimators that explicitly allow for UHTE. This strategy has its roots in the Gronau-Heckman selection model of the 1970s, ideas from which have been revitalized through marginal treatment effects (MTE) analysis. We discuss implementation of MTE methods and draw connections with related control function and bounding methods that are scattered throughout the econometric and causal inference literature

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: NBER working paper series ; no. w32927
    Subjects: IV-Schätzung; Kausalanalyse; Schätztheorie; Instrumental Variables (IV) Estimation
    Scope: 1 Online-Ressource, illustrations (black and white)
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    Hardcopy version available to institutional subscribers
  11. Sensitivity analysis in semiparametric likelihood models
    Author: Chen, Xiaohong; Tamer, Elie T.; Torgovitsky, Alexander
    Published: 2011
    Publisher:  Cowles Foundation for Research in Economics, Yale Univ., New Haven, Conn.

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    VS 29 (1836)
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    Language: English
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    Format: Online
    Series: Cowles Foundation discussion paper ; 1836
    Scope: Online-Ressource ( 58 S., 662,47 KB), graph. Darst.
  12. Using instrumental variables for inference about policy relevant treatment effects
    Author: Mogstad, Magne; Santos, Andres; Torgovitsky, Alexander
    Published: July 2017
    Publisher:  National Bureau of Economic Research, Cambridge, MA

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Series: Working paper series / National Bureau of Economic Research ; 23568
    Subjects: Kausalanalyse
    Scope: 92 Seiten, Illustrationen
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    Erscheint auch als Online-Ausgabe
  13. Instrumental variables estimation of a generalized correlated random coefficients model
    Author: Masten, Matthew; Torgovitsky, Alexander
    Published: 2014
    Publisher:  Centre for Microdata Methods and Practice, London

    We study identification and estimation of the average treatment effect in a correlated random coefficients model that allows for first stage heterogeneity and binary instruments. The model also allows for multiple endogenous variables and... more

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    We study identification and estimation of the average treatment effect in a correlated random coefficients model that allows for first stage heterogeneity and binary instruments. The model also allows for multiple endogenous variables and interactions between endogenous variables and covariates. Our identification approach is based on averaging the coefficients obtained from a collection of ordinary linear regressions that condition on different realizations of a control function. This identification strategy suggests a transparent and computationally straightforward estimator of a trimmed average treatment effect constructed as the average of kernel-weighted linear regressions. We develop this estimator and establish its √n-consistency and asymptotic normality. Monte Carlo simulations show excellent finite-sample performance that is comparable in precision to the standard two-stage least squares estimator. We apply our results to analyze the effect of air pollution on house prices, and find substantial heterogeneity in first stage instrument effects as well as heterogeneity in treatment effects that is consistent with household sorting.

     
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    Language: English
    Media type: Book
    Format: Online
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    hdl: 10419/97380
    Series: Cemmap working paper / Centre for Microdata Methods and Practice ; 02/14
    Subjects: correlated random coefficients; instrumental variables; unobserved heterogeneity; semiparametrics; hedonic models; residential sorting; valuation of clean air
    Scope: Online-Ressource (42 S.), graph. Darst.