Download or read book Estimation of Average Treatment Effects Using Panel Data when Treatment Effect Heterogeneity Depends on Unobserved Fixed Effects written by Shosei Sakaguchi and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a new panel data approach to identify and estimate the time-varying average treatment effect (ATE). The approach allows for treatment effect heterogeneity that depends on unobserved fixed effects. In the presence of this type of heterogeneity, existing panel data approaches identify the ATE for limited subpopulations only. In contrast, the proposed approach identifies and estimates the ATE for the entire population. The approach relies on the linear fixed effects specification of potential outcome equations and uses exogenous variables that are correlated with the fixed effects. I apply the approach to study the impact of a mother's smoking during pregnancy on her child's birth weight.

Download or read book Essays on Distributional Treatment Effects with Panel Data written by Brantly Mercer Callaway (IV.) and published by . This book was released on 2016 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Econometric Analysis of Cross Section and Panel Data second edition written by Jeffrey M. Wooldridge and published by MIT Press. This book was released on 2010-10-01 with total page 1095 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

Download or read book Difference in Differences Estimators of Intertemporal Treatment Effects written by Clément de Chaisemartin and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We study treatment-effect estimation, with a panel where groups may experience multiple changes of their treatment dose. We make parallel trends assumptions, but do not restrict treatment effect heterogeneity, unlike the linear regressions that have been used in such designs. We extend the event-study approach for binary-and-staggered treatments, by redefining the event as the first time a group's treatment changes. This yields an event-study graph, with reduced-form estimates of the effect of having been exposed to a weakly higher amount of treatment for l periods. We show that the reduced-form estimates can be combined into an economically interpretable cost-benefit ratio.

Download or read book The SAGE Handbook of Regression Analysis and Causal Inference written by Henning Best and published by SAGE. This book was released on 2013-12-20 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: ′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.

Download or read book Panel Data Econometrics with R written by Yves Croissant and published by John Wiley & Sons. This book was released on 2018-08-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.

Download or read book Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score written by Keisuke Hirano and published by . This book was released on 2000 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is independent of the potential outcomes given pretreatment variables, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the pre-treatment variables. Rosenbaum and Rubin (1983, 1984) show that adjusting solely for differences between treated and control units in a scalar function of the pre-treatment, the propensity score, also removes the entire bias associated with differences in pre-treatment variables. Thus it is possible to obtain unbiased estimates of the treatment effect without conditioning on a possibly high-dimensional vector of pre-treatment variables. Although adjusting for the propensity score removes all the bias, this can come at the expense of efficiency. We show that weighting with the inverse of a nonparametric estimate of the propensity score, rather than the true propensity score, leads to efficient estimates of the various average treatment effects. This result holds whether the pre-treatment variables have discrete or continuous distributions. We provide intuition for this result in a number of ways. First we show that with discrete covariates, exact adjustment for the estimated propensity score is identical to adjustment for the pre-treatment variables. Second, we show that weighting by the inverse of the estimated propensity score can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score. Finally, we make a connection to results to other results on efficient estimation through weighting in the context of variable probability sampling.

Download or read book Two Way Fixed Effects and Difference in Differences Estimators with Heterogeneous Treatment Effects and Imperfect Parallel Trends written by Clément de Chaisemartin and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two-way fixed effects (TWFE) regressions with period and group fixed effects are widely used to estimate policies' effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. Researchers have long thought that TWFE estimators are equivalent to differences-in-differences (DID) estimators, that rely on a partly testable parallel trends assumption. In two-groups two-periods designs where a treatment group is untreated at both dates and a treatment group becomes treated at the second period, the treatment coefficient in a TWFE is indeed equivalent to a DID. Motivated by this fact, researchers have also estimated TWFE regressions in more complicated designs with many groups and periods, variation in treatment timing, treatments switching on and off, and/or non-binary treatments, confident that there as well, TWFE was giving them an estimation method that only relied on a partly testable parallel trends assumption. Two recent strands of literature have shattered that confidence. First, it has recently been shown that even if parallel trends holds, TWFE may produce misleading estimates, if the policy's effect is heterogeneous between groups or over time, as is often the case. The realization that one of the most commonly used empirical methods in the quantitative social sciences relies on an often-implausible assumption has spurred a flurry of methodological papers. Some of them have diagnosed this issue and analyzed its origins. Other papers have proposed alternative estimators relying on parallel trends conditions, like TWFE estimators, but robust to heterogeneous effects, unlike TWFE estimators. Hereafter, those alternative estimators are referred to as heterogeneity-robust DID estimators. Second, in a recent paper, Roth (2022) has shown that tests of the parallel trends assumption often lack statistical power, and may fail to detect differential trends between treated and control locations that are often large enough to account for a significant share of the policy's estimated effect. This realization has spurred a growing interest among practitioners for a second strand of literature, that has proposed alternative estimation methods relying on weaker assumptions than parallel trends. Examples include estimators relying on a conditional parallel trends assumption (see, e.g., Abadie, 2005), estimators assuming bounded differential trends (see, e.g., Manski and Pepper, 2018; Rambachan and Roth, 2023), estimators assuming a factor model with interactive fixed effects (see, e.g., Bai, 2003) and synthetic control estimators (see, e.g., Abadie et al., 2010), and estimators assuming grouped patterns of heterogeneity (see,e.g., Bonhomme and Manresa, 2015).This textbook aims to provide an overview of these two strands of literature, as well as other panel data methods routinely used for causal inference by practitionners.

Download or read book Three Essays on the Estimation of Average Treatment Effects in Quasi Experimental Panel Data written by Kathleen T. Li and published by . This book was released on 2018 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifying average treatment effects (ATE) from quasi-experimental panel data has become one of the most important yet challenging endeavors for social scientists. The difficulty lies in accurately estimating the counterfactual outcomes for the potentially treated units in the absence of treatment. Perhaps the most popular method to estimate average treatment effects is the Difference-in-Differences (DID) method. The key assumption of the DID method is that outcomes of the treated units would have followed a path parallel to the control units in the absence of treatment and violation of this ``parallel lines" assumption will result in biased estimates. This dissertation consists of three essays, which either build on existing methods (essay 1 and 3) or propose a new method (essay 2) that can be used even when the ``parallel lines" assumption of DID does not hold. In essay 1, we derive the asymptotic distribution of the HCW method, which is computationally simple as it only involves least squares regressions. However, in cases where treatment and control units are positively correlated, the HCW method may have less predictive efficiency than other methods such as the synthetic control and modified synthetic control method, which impose the restriction that weights are non-negative. The popular synthetic control method additionally imposes the restriction that the weights sum to one, which can be a helpful regularization condition when there are many control units. In essay 3, we provide the inference theory for both the synthetic control and modified synthetic control method through projection theory and propose a computational algorithm using subsampling to compute the confidence intervals. In order to apply the HCW method, synthetic control method and modified synthetic control method, the number of control units needs to be smaller than the pre-treatment sample size. In essay 2, we propose the augmented DID method, which can be used where there are many treatment and control units, but is less flexible than the three aforementioned methods. In short, this dissertation provides several methods and their inference procedures to identify average treatment effects. Which method should be used when depends on the structure of the data.

Download or read book Trimmed Mean Group Estimation of Average Treatment Effects in Ultra Short T Panels Under Correlated Heterogeneity written by M. Hashem Pesaran and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Under correlated heterogeneity, the commonly used two-way fixed effects estimator is biased and can lead to misleading inference. This paper proposes a new trimmed mean group (TMG) estimator which is consistent at the irregular rate of n 1/3 even if the time dimension of the panel is as small as the number of its regressors. Extensions to panels with time effects are provided, and a Hausman-type test of correlated heterogeneity is proposed. Small sample properties of the TMG estimator (with and without time effects) are investigated by Monte Carlo experiments and shown to be satisfactory and perform better than other trimmed estimators proposed in the literature. The proposed test of correlated heterogeneity is also shown to have the correct size and satisfactory power. The utility of the TMG approach is illustrated with an empirical application.

Download or read book Panel Methods for Finance written by Marno Verbeek and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-10-25 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial data are typically characterised by a time-series and cross-sectional dimension. Accordingly, econometric modelling in finance requires appropriate attention to these two – or occasionally more than two – dimensions of the data. Panel data techniques are developed to do exactly this. This book provides an overview of commonly applied panel methods for financial applications, including popular techniques such as Fama-MacBeth estimation, one-way, two-way and interactive fixed effects, clustered standard errors, instrumental variables, and difference-in-differences. Panel Methods for Finance: A Guide to Panel Data Econometrics for Financial Applications by Marno Verbeek offers the reader: Focus on panel methods where the time dimension is relatively small A clear and intuitive exposition, with a focus on implementation and practical relevance Concise presentation, with many references to financial applications and other sources Focus on techniques that are relevant for and popular in empirical work in finance and accounting Critical discussion of key assumptions, robustness, and other issues related to practical implementation

Download or read book Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity written by Pablo Lavado and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers identification of treatment effects when the outcome variables and covari-ates are not observed in the same data sets. Ecological inference models, where aggregate out-come information is combined with individual demographic information, are a common example of these situations. In this context, the counterfactual distributions and the treatment effects are not point identified. However, recent results provide bounds to partially identify causal effects. Unlike previous works, this paper adopts the selection on unobservables assumption, which means that randomization of treatment assignments is not achieved until time fixed unobserved heterogeneity is controlled for. Panel data models linear in the unobserved components are con-sidered to achieve identification. To assess the performance of these bounds, this paper provides a simulation exercise.

Download or read book Marginal Treatment Effects in Difference in Differences written by Pedro Picchetti and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Difference-in-Differences (DiD) is a popular method used to evaluate the effect of a treatment. In its most simple version a control group remains untreated at two periods, whereas the treatment group becomes fully treated at the second period. However, it is not uncommon in applications of the method that the treatment rate only increases more in the treatment group. This article presents identification results for the marginal treatment effect (MTE) in such fuzzy designs. We show that we can modify the standard identifying assumptions in DiD designs with covariates to identify the MTE in models with essential heterogeneity. We propose two different procedures for the estimation of the MTE that rely on different assumptions regarding the potential outcomes model and prove their asymptotical normality. Furthermore, we derive a doubly-robust estimator for the local average treatment effect (LATE) which augments the two-way fixed effects regression model with a control function and unit-specific weights that rise from the propensity score. We assert the desirable finite-sample properties through simulation studies of a linear MTE model. Finally, we use our results to investigate heterogeneity on the returns to primary school attendance in Indonesia.

Download or read book Longitudinal and Panel Data written by Edward W. Frees and published by Cambridge University Press. This book was released on 2004-08-16 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.

Download or read book Microeconometrics written by A. Colin Cameron and published by Cambridge University Press. This book was released on 2005-05-09 with total page 1058 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.

Download or read book Identification of Population Average Treatment Effects Using Nonlinear Instrumental Variables Estimators written by Cole Garrett Chapman and published by . This book was released on 2014 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear two-stage residual inclusion (2SRI) estimators have become increasingly favored over traditional linear two-stage least squares (2SLS) methods for instrumental variables analysis of empirical models with inherently nonlinear dependent variables. Rising adoption of nonlinear 2SRI is largely attributable to simulation evidence showing that nonlinear 2SRI generates consistent estimates of population average treatment effects in nonlinear models, while 2SLS and nonlinear 2SPS do not. However, while it is believed that consistency of 2SRI for population average treatment effects is a general result, current evidence is limited to simulations performed under unique and restrictive settings with regards to treatment effect heterogeneity and conditions underlying treatment choices. This research contributes by describing existing simulation evidence and investigating the ability to generate absolute estimates of population average treatment effects (ATE) and local average treatment effects (LATE) using common IV estimators using Monte Carlo simulation methods across 10 alternative scenarios of treatment effect heterogeneity and sorting-on-the-gain. Additionally, estimates for the effect of ACE/ARBs on 1-year survival for Medicare beneficiaries with acute myocardial infarction are generated and compared across alternative linear and nonlinear IV estimators. Simulation results show that, while 2SLS generates unbiased and consistent estimates of LATE across all scenarios, nonlinear 2SRI generates unbiased estimates of ATE only under very restrictive settings. If marginal patients are unique in terms of treatment effectiveness, then nonlinear 2SRI cannot be expected to generate unbiased or consistent estimates of ATE unless all factors related to treatment effect heterogeneity are fully measured.

Download or read book Panel Data Econometrics written by Mike Tsionas and published by Academic Press. This book was released on 2019-06-19 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made. - Provides a vast array of empirical applications useful to practitioners from different application environments - Accompanied by extensive case studies and empirical exercises - Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings - Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts