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Treatment effects and panel data

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  • Lechner, Michael

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Abstract

It is a major achievement of the econometric treatment effect literature to clarify under which conditions causal effects are non-parametrically identified. The first part of this chapter focuses on the static treatment model. In this part, I show how panel data can be used to improve the credibility of matching and instrumental variable estimators. In practice, these gains come mainly from the availability of outcome variables measured prior to treatment. Such outcome variables also foster the use of alternative identification strategies, in particular so-called difference-in-difference estimation. In addition to improving the credibility of static causal models, panel data may allow credibly estimating dynamic causal models, which is the main theme of the second part of this chapter.

Suggested Citation

  • Lechner, Michael, 2013. "Treatment effects and panel data," Economics Working Paper Series 1314, University of St. Gallen, School of Economics and Political Science.
  • Handle: RePEc:usg:econwp:2013:14
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    File URL: http://ux-tauri.unisg.ch/RePEc/usg/econwp/EWP-1314.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Pablo Lavado & Gonzalo Rivera, 2016. "Identifying Treatment Effects with Data Combination and Unobserved Heterogeneity," Working Papers 2016-79, Peruvian Economic Association.
    2. Pablo Lavado, "undated". "Identifying Treatment Effects and Counterfactual Distributions using Data Combination with Unobserved Heterogeneity," Working Papers 13-25, Departamento de Economía, Universidad del Pacífico.
    3. Pablo Lavado & Gonzalo Rivera, 2015. "Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity," Working Papers 15-14, Centro de Investigación, Universidad del Pacífico.
    4. Kongstad, L.P. & Mellace, G. & Olsen, K.R., 2016. "Can the use of Electronic Health Records in General Practice reduce hospitalizations for diabetes patients? Evidence from a natural experiment," Health, Econometrics and Data Group (HEDG) Working Papers 16/25, HEDG, c/o Department of Economics, University of York.

    More about this item

    Keywords

    Matching; instrumental variables; local average treatment effects; difference-in-difference estimation; dynamic treatment effects;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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