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  1. Two-way Fixed Effects and Differences-in-Differences Estimators with Several Treatments
    Autor*in: Chaisemartin, Clément de; D'Haultfœuille, Xavier
    Erschienen: October 2022
    Verlag:  National Bureau of Economic Research, Cambridge, Mass

    We study two-way-fixed-effects regressions (TWFE) with several treatment variables. Under a parallel trends assumption, we show that the coefficient on each treatment identifies a weighted sum of that treatment's effect, with possibly negative... mehr

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    Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden
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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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    We study two-way-fixed-effects regressions (TWFE) with several treatment variables. Under a parallel trends assumption, we show that the coefficient on each treatment identifies a weighted sum of that treatment's effect, with possibly negative weights, plus a weighted sum of the effects of the other treatments. Thus, those estimators are not robust to heterogeneous effects and may be contaminated by other treatments' effects. When a treatment is omitted from the regression, we obtain a new omitted variable bias formula, where bias can arise even if the treatments are not correlated with each other, but can be smaller than in the TWFE regression with all treatments. We propose an alternative difference-in-differences estimator, robust to heterogeneous effects and immune to the contamination problem. In the application we consider, the TWFE regression identifies a highly non-convex combination of effects, with large contamination weights, and one of its coefficients significantly differs from our heterogeneity-robust estimator

     
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    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: NBER working paper series ; no. w30564
    Schlagworte: Kausalanalyse; Fixed-Effects-Modell; Differenz von Differenzen; Schätztheorie; Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Panel Data Models; Spatio-temporal Models
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  2. Identification in a Binary Choice Panel Data Model with a Predetermined Covariate
    Autor*in: Bonhomme, Stéphane; Dano, Kevin; Graham, Bryan S.
    Erschienen: March 2023
    Verlag:  National Bureau of Economic Research, Cambridge, Mass

    We study identification in a binary choice panel data model with a single predetermined binary covariate (i.e., a covariate sequentially exogenous conditional on lagged outcomes and covariates). The choice model is indexed by a scalar parameter θ,... mehr

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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    We study identification in a binary choice panel data model with a single predetermined binary covariate (i.e., a covariate sequentially exogenous conditional on lagged outcomes and covariates). The choice model is indexed by a scalar parameter θ, whereas the distribution of unit-specific heterogeneity, as well as the feedback process that maps lagged outcomes into future covariate realizations, are left unrestricted. We provide a simple condition under which θ is never point-identified, no matter the number of time periods available. This condition is satisfied in most models, including the logit one. We also characterize the identified set of θ and show how to compute it using linear programming techniques. While θ is not generally point-identified, its identified set is informative in the examples we analyze numerically, suggesting that meaningful learning about θ is possible even in short panels with feedback

     
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    Schriftenreihe: NBER working paper series ; no. w31027
    Schlagworte: Panel; Schätztheorie; Logit-Modell; Panel Data Models; Spatio-temporal Models
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  3. Difference-in-Differences with Unequal Baseline Treatment Status
    Autor*in: Tazhitdinova, Alisa; Vazquez-Bare, Gonzalo
    Erschienen: March 2023
    Verlag:  National Bureau of Economic Research, Cambridge, Mass

    We study a difference-in-differences (DiD) framework where groups experience unequal treatment statuses in the pre-policy change period. This approach is commonly employed in empirical studies but it contradicts the canonical model's assumptions. We... mehr

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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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    We study a difference-in-differences (DiD) framework where groups experience unequal treatment statuses in the pre-policy change period. This approach is commonly employed in empirical studies but it contradicts the canonical model's assumptions. We show that in such settings, the standard DiD approach fails to recover the average treatment effect (ATT), unless the treatment effect is immediate and constant over time. Furthermore, the usual parallel trends test is invalid, meaning one may find pre-trends when the parallel trends assumption holds, and vice versa. We discuss two solutions. First, we show that including a linear term trend will recover the ATT if the differences in trends are constant over time (both in unequal baseline and canonical DiD settings) but not otherwise. Second, estimation in reverse also recovers the ATT if the potential outcomes do not depend on past treatments and post-policy statuses are converging

     
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    Schriftenreihe: NBER working paper series ; no. w31063
    Schlagworte: Differenz von Differenzen; Kausalanalyse; Theorie; Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Panel Data Models; Spatio-temporal Models
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  4. A Local Projections Approach to Difference-in-Differences Event Studies
    Autor*in: Dube, Arindrajit; Girardi, Daniele; Jordà, Òscar; Taylor, Alan M.
    Erschienen: April 2023
    Verlag:  National Bureau of Economic Research, Cambridge, Mass

    Many of the challenges in the estimation of dynamic heterogeneous treatment effects can be resolved with local projection (LP) estimators of the sort used in applied macroeconometrics. This approach provides a convenient alternative to the more... mehr

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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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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Many of the challenges in the estimation of dynamic heterogeneous treatment effects can be resolved with local projection (LP) estimators of the sort used in applied macroeconometrics. This approach provides a convenient alternative to the more complicated solutions proposed in the recent literature on Difference-in-Differences (DiD). The key is to combine LPs with a flexible 'clean control' condition to define appropriate sets of treated and control units. Our proposed LP-DiD estimator is clear, simple, easy and fast to compute, and it is transparent and flexible in its handling of treated and control units. Moreover, it is quite general, including in its ability to control for pre-treatment values of the outcome and of other time-varying covariates. The LP-DiD estimator does not suffer from the negative weighting problem, and indeed can be implemented with any weighting scheme the investigator desires. Simulations demonstrate the good performance of the LP-DiD estimator in common settings. Two recent empirical applications illustrate how LP-DiD addresses the bias of conventional fixed effects estimators, leading to potentially different results

     
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    Schriftenreihe: NBER working paper series ; no. w31184
    Schlagworte: Ereignisstudie; Suchtheorie; Ökonometrie; Econometric and Statistical Methods and Methodology: General; Panel Data Models; Spatio-temporal Models; Econometric Modeling
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  5. Estimating the Spatial Amplification of Damage Caused by Degradation in the Amazon
    Autor*in: Araujo, Rafael; Assunção, Juliano J.; Hirota, Marina; Scheinkman, José Alexandre
    Erschienen: August 2023
    Verlag:  National Bureau of Economic Research, Cambridge, Mass

    The Amazon rainforests have been undergoing unprecedented levels of human-induced disturbances. In addition to local impacts, such changes are likely to cascade following the eastern-western atmospheric flow generated by trade winds. We propose a... mehr

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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    The Amazon rainforests have been undergoing unprecedented levels of human-induced disturbances. In addition to local impacts, such changes are likely to cascade following the eastern-western atmospheric flow generated by trade winds. We propose a model of spatial and temporal interactions created by this flow to estimate the spread of local disturbances to downwind locations along atmospheric trajectories. The spatial component captures cascading effects propagated by neighboring regions while the temporal component captures persistence. All these network effects can be described by a single matrix, acting as a spatial multiplier that amplifies local disturbances. This matrix can be used to easily map where the damage of an initial forest disturbance is amplified and propagated to. We identify regions that are likely to cause the largest impact throughout the basin, and those that are the most vulnerable to shocks caused by remote deforestation. On average, the presence of cascading effects mediated by winds doubles the impact of an initial damage. However, there is heterogeneity in this impact. While damage in some regions does not propagate, in others amplification may reach 250%

     
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    Schriftenreihe: NBER working paper series ; no. w31550
    Schlagworte: Entwaldung; Umweltbelastung; Räumliche Verteilung; Regionalökonomik; Amazonasgebiet; Panel Data Models; Spatio-temporal Models; Climate; Natural Disasters and Their Management; Global Warming; Ecological Economics: Ecosystem Services; Biodiversity Conservation; Bioeconomics; Industrial Ecology
    Umfang: 1 Online-Ressource, illustrations (black and white)
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  6. Financial Machine Learning
    Autor*in: Kelly, Bryan T.; Xiu, Dacheng
    Erschienen: July 2023
    Verlag:  National Bureau of Economic Research, Cambridge, Mass

    We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both... mehr

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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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    We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed

     
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  7. Regularization from Economic Constraints
    A New Estimator for Marginal Emissions
    Autor*in: Holland, Stephen P.; Mansur, Erin; Verdier, Valentin; Yates, Andrew J.
    Erschienen: January 2024
    Verlag:  National Bureau of Economic Research, Cambridge, Mass

    Environmental policy is increasingly concerned with measuring emissions resulting from local changes to electricity consumption. These marginal emissions are challenging to measure because electricity grids encompass multiple locations and the... mehr

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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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    Environmental policy is increasingly concerned with measuring emissions resulting from local changes to electricity consumption. These marginal emissions are challenging to measure because electricity grids encompass multiple locations and the information available to identify the effect of each location's consumption on grid-wide emissions is limited. We formalize this as a high-dimensional aggregation problem: The effect of electricity consumption on emissions can be estimated precisely for each electricity generating unit (generator), but not precisely enough to obtain reliable estimates of marginal emissions by summing these effects across all generators in a grid. We study how two economic constraints can address this problem: electricity supply equals demand and an assumption of monotonicity. We show that these constraints can be used to formulate a 'naturally regularized' estimator, which implements an implicit penalization that does not need to be directly tuned. Under an additional assumption of sparsity, we show that our new estimator solves the high-dimensional aggregation problem, i.e., it is consistent for marginal emissions where the usual regression estimator would not be. We also develop an asymptotically valid method for inference to accompany our estimator. When applied to the U.S. electricity grid with 13 separate consumption regions, our method yields plausible patterns of marginal generation across fuel types and geographic location. Our estimates of region-level marginal emissions are precise, account for imports/exports between regions, and allow for all fuel types to potentially be on the margin

     
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    Schriftenreihe: NBER working paper series ; no. w32065
    Schlagworte: Elektrizität; Privater Konsum; Treibhausgas-Emissionen; Messung; Schätzung; Panel; Erneuerbare Energie; Umweltpolitik; USA; Panel Data Models; Spatio-temporal Models; Alternative Energy Sources; Government Policy; Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
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  8. Causal Models for Longitudinal and Panel Data
    A Survey
    Autor*in: Arkhangelsky, Dmitry; Imbens, Guido
    Erschienen: December 2023
    Verlag:  National Bureau of Economic Research, Cambridge, Mass

    This survey discusses the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, with an emphasis on practical advice for empirical... mehr

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    Universitätsbibliothek Freiburg
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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    This survey discusses the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, with an emphasis on practical advice for empirical researchers. It pays particular attention to heterogeneity in the causal effects, often in situations where few units are treated. The literature has extended earlier work on difference-in-differences or two-way-fixed-effect estimators and more generally incorporated factor models or interactive fixed effects. It has also developed novel methods using synthetic control approaches

     
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    Schriftenreihe: NBER working paper series ; no. w31942
    Schlagworte: Kausalanalyse; Panel; Theorie; Panel Data Models; Spatio-temporal Models
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