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Raymond J G M Florax - One of the best experts on this subject based on the ideXlab platform.
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difference in differences techniques for spatial data local autocorrelation and spatial interaction
Economics Letters, 2015Co-Authors: Michael S Delgado, Raymond J G M FloraxAbstract:We consider treatment effect estimation via a difference-in-difference approach for spatial data with local spatial interaction such that the potential outcome of observed units depends on their own treatment as well as on the treatment status of proximate neighbors. We show that under standard assumptions (common trend and ignorability) a straightforward spatially explicit version of the benchmark Difference-In-Differences regression is capable of identifying both direct and indirect treatment effects. We demonstrate the finite sample performance of our spatial estimator via Monte Carlo simulations.
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difference in differences techniques for spatial data local autocorrelation and spatial interaction
2015Co-Authors: Michael S Delgado, Raymond J G M FloraxAbstract:We consider treatment effect estimation via a difference-in-difference approach for data with local spatial interaction such that the outcome of observed units depends on their own treatment as well as on the treatment status of proximate neighbors. We show that under standard assumptions (common trend and ignorability) a straightforward spatially explicit version of the benchmark Difference-In-Differences regression is capable of identifying both direct and indirect treatment effects. We demonstrate the finite sample performance of our spatial estimator via Monte Carlo simulations.
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Difference-In-Differences techniques for spatial data: Local autocorrelation and spatial interaction ☆
Economics Letters, 2015Co-Authors: Michael S Delgado, Raymond J G M FloraxAbstract:We consider treatment effect estimation via a difference-in-difference approach for spatial data with local spatial interaction such that the potential outcome of observed units depends on their own treatment as well as on the treatment status of proximate neighbors. We show that under standard assumptions (common trend and ignorability) a straightforward spatially explicit version of the benchmark Difference-In-Differences regression is capable of identifying both direct and indirect treatment effects. We demonstrate the finite sample performance of our spatial estimator via Monte Carlo simulations.
Guido W. Imbens - One of the best experts on this subject based on the ideXlab platform.
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Synthetic Difference in Differences
2019Co-Authors: Dmitry Arkhangelsky, Guido W. Imbens, Susan Athey, David A. Hirshberg, Stefan WagerAbstract:We present a new perspective on the Synthetic Control (SC) method as a weighted least squares regression estimator with time fixed effects and unit weights. This perspective suggests a generalization with two way (both unit and time) fixed effects, and both unit and time weights, which can be interpreted as a unit and time weighted version of the standard Difference In Differences (DID) estimator. We find that this new Synthetic Difference In Differences (SDID) estimator has attractive properties compared to the SC and DID estimators. Formally we show that our approach has double robustness properties: the SDID estimator is consistent under a wide variety of weighting schemes given a well-specified fixed effects model, and SDID is consistent with appropriately penalized SC weights when the basic fixed effects model is misspecified and instead the true data generating process involves a more general low-rank structure (e.g., a latent factor model). We also present results that justify standard inference based on weighted DID regression. Further generalizations include unit and time weighted factor models. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
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Synthetic Difference in Differences
2019Co-Authors: Dmitry Arkhangelsky, Guido W. Imbens, Susan Athey, David A. Hirshberg, Stefan WagerAbstract:We present a new perspective on the Synthetic Control (SC) method as a weighted least squares regression estimator with time fixed effects and unit weights. This perspective suggests a generalization with two way (both unit and time) fixed effects, and both unit and time weights, which can be interpreted as a unit and time weighted version of the standard Difference In Differences (DID) estimator. We find that this new Synthetic Difference In Differences (SDID) estimator has attractive properties compared to the SC and DID estimators. Formally we show that our approach has double robustness properties: the SDID estimator is consistent under a wide variety of weighting schemes given a well-specified fixed effects model, and SDID is consistent with appropriately penalized SC weights when the basic fixed effects model is misspecified and instead the true data generating process involves a more general low-rank structure (e.g., a latent factor model). We also present results that justify standard inference based on weighted DID regression. Further generalizations include unit and time weighted factor models.
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Synthetic Difference in Differences
arXiv: Methodology, 2018Co-Authors: Dmitry Arkhangelsky, Guido W. Imbens, Susan Athey, David A. Hirshberg, Stefan WagerAbstract:We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control methods. Relative to these methods, we find, both theoretically and empirically, that the proposed "synthetic difference in differences" estimator has desirable robustness properties, and that it performs well in settings where the conventional estimators are commonly used in practice. We study the asymptotic behavior of the estimator when the systematic part of the outcome model includes latent unit factors interacted with latent time factors, and we present conditions for consistency and asymptotic normality.
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Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption
2018Co-Authors: Susan Athey, Guido W. ImbensAbstract:In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment process. We show that under random assignment of the adoption date the standard Difference-In-Differences estimator is is an unbiased estimator of a particular weighted average causal effect. We characterize the proeperties of this estimand, and show that the standard variance estimator is conservative.
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design based analysis in difference in differences settings with staggered adoption
Research Papers, 2018Co-Authors: Susan Athey, Guido W. ImbensAbstract:In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment process. We show that under random assignment of the adoption date the standard Difference-In-Differences estimator is an unbiased estimator of a particular weighted average causal effect. We characterize the properties of this estimand, and show that the standard variance estimator is conservative.
Reinhard Madlener - One of the best experts on this subject based on the ideXlab platform.
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the impact of wind farm visibility on property values a spatial difference in differences analysis
Energy Economics, 2016Co-Authors: Yasin Sunak, Reinhard MadlenerAbstract:Today's investment decisions in large-scale onshore wind projects in Germany are no longer determined only by the investment's economic benefit, but also by concerns associated to social acceptance. Despite a mostly positive attitude towards the expansion of wind power, local public concerns often stem from the belief that the proximity to large-scale wind farms may lead to a decrease in property prices. In particular, the change in landscape caused by the construction of a wind farm may have an adverse impact on the view from some properties, and thus may negatively affect their price. To investigate the potential devaluation of properties in Germany due to wind farms, we use a quasi-experimental technique and apply a spatial Difference-In-Differences approach to various wind farm sites in the federal state of North Rhine-Westphalia. We adopt a quantitative visual impact assessment approach to account for the adverse environmental effects caused by the wind turbines. To properly account for spatial dependence and unobserved variables biases, we apply augmented spatial econometric models. The estimates indicate that the asking price for properties whose view was strongly affected by the construction of wind turbines decreased by about 9–14%. In contrast, properties with a minor or marginal view on the wind turbines experienced no devaluation.
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local impacts of wind farms on property values a spatial difference in differences analysis
Energy & the Economy 37th IAEE International Conference June 15-18 2014, 2014Co-Authors: Yasin Sunak, Reinhard MadlenerAbstract:Today’s investment decisions in large-scale onshore wind projects in Germany are no longer determined only by the investment’s economic benefit, but also by concerns associated to social acceptance. Despite a mostly positive attitude towards the expansion of wind power, local public concerns often stem from the belief that the proximity to large-scale wind farms may lead to a decrease in property prices. In particular, the change in landscape caused by the construction of a wind farm may have an adverse impact on the view from some properties, and thus may negatively affect their price. To investigate the potential devaluation of properties in Germany due to wind farms, we use a quasi-experimental technique and apply a spatial Difference-In-Differences approach to various wind farm sites in the federal state of North Rhine-Westphalia. We adopt a quantitative visual impact assessment approach to account for the adverse environmental effects caused by the wind turbines. To properly account for spatial dependence and unobserved variables biases, we apply augmented spatial econometric models. The estimates indicate that the asking price for properties whose view was strongly affected by the construction of wind turbines decreased by about 10-17%. In contrast, properties with a minor or marginal view on the wind turbines experienced no devaluation.
Christopher Taber - One of the best experts on this subject based on the ideXlab platform.
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Difference-In-Differences Method in Comparative Effectiveness Research: Utility with Unbalanced Groups
Applied health economics and health policy, 2016Co-Authors: Huanxue Zhou, Christopher Taber, Steve ArconaAbstract:Background Comparative effectiveness research (CER) often includes observational studies utilizing administrative data. Multiple conditioning methods can be used for CER to adjust for group differences, including Difference-In-Differences (DiD) estimation.
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Inference with “Difference in Differences” with a Small Number of Policy Changes
Review of Economics and Statistics, 2011Co-Authors: Timothy G. Conley, Christopher TaberAbstract:Abstract In Difference-In-Differences applications, identification of the key parameter often arises from changes in policy by a small number of groups. In contrast, typical inference assumes that the number of groups changing policy is large. We present an alternative inference approach for a small (finite) number of policy changers, using information from a large sample of nonchanging groups. Treatment effect point estimators are not consistent, but we can consistently estimate their asymptotic distribution under any point null hypothesis about the treatment. Thus, treatment point estimators can be used as test statistics, and confidence intervals can be constructed using test statistic inversion.
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Inference with "Difference in Differences" with a Small Number of Policy Changes
2005Co-Authors: Timothy G. Conley, Christopher TaberAbstract:Difference in differences methods have become very popular in applied work. This paper provides a new method for inference in these models when there are a small number of policy changes. This situation occurs in many implementations of these estimators. Identification of the key parameter typically arises when a group "changes" some particular policy. The asymptotic approximations that are typically employed assume that the number of cross sectional groups, N, times the number of time periods, T, is large. However, even when N or T is large, the number of actual policy changes observed in the data is often very small. In this case, we argue that point estimators of treatment effects should not be thought of as being consistent and that the standard methods that researchers use to perform inference in these models are not appropriate. We develop an alternative approach to inference under the assumption that there are a finite number of policy changes in the data, using asymptotic approximations as the number of non-changing groups gets large. In this situation we cannot obtain a consistent point estimator for the key treatment effect parameter. However, we can consistently estimate the finite-sample distribution of the treatment effect estimator, up to the unknown parameter itself. This allows us to perform hypothesis tests and construct confidence intervals. For expositional and motivational purposes, we focus on the difference in differences case, but our approach should be appropriate more generally in treatment effect models which employ a large number of controls, but a small number of treatments. We demonstrate the use of the approach by analyzing the effect of college merit aide programs on college attendance. We show that in some cases the standard approach can give misleading results.
Christian Graff - One of the best experts on this subject based on the ideXlab platform.
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Why estimating relative differences by Ln(A/B) in percentage and why naming it geometric difference
2015Co-Authors: Christian GraffAbstract:Between stimuli or performances A and B, the ratio’s natural logarithm Ln(A/B) best expresses their relative difference, especially given as a percentage. I name it "geometric difference" as opposed to the "arithmetic difference" (A-B), by analogy with arithmetic and geometric progressions and arithmetic and geometric means.
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Expressing relative differences (in percent) by the difference of natural logarithms
Journal of Mathematical Psychology, 2014Co-Authors: Christian GraffAbstract:Most psychophysical investigations measure stimuli or performance in Système International units and use relative differences between them for comparison. In this theoretical note, we propose the ratio’s natural logarithm or the difference between the Napierian logarithms, as a desirable measure of relative differences between two psychophysical quantities. It challenges the more frequently used (x2−x1)/x1(x2−x1)/x1, (x2−x1)/x2(x2−x1)/x2, as well as (x2−x1)/xM(x2−x1)/xM, where x1,x2x1,x2, and xMxM are the initial value in a change, the larger value, and a mean value between x1x1 and x2x2, respectively. As for the three aforementioned expressions, it can be conveniently expressed as a percentage. For two physical measures, x1x1 and View the MathML sourcex2(x1>0;x2>0), the difference between natural logarithms View the MathML sourceDNL=Ln(x2)−Ln(x1)=Ln(x2/x1) sits between (x2−x1)/x2(x2−x1)/x2 and (x2−x1)/x1(x2−x1)/x1; it is actually the mean value of (x2−x1)/x(x2−x1)/x for all xx values between x1x1 and x2x2. Unlike other estimates, it satisfies all three of the following properties: symmetry, i.e. Δ(x1;x2)=−Δ(x2;x1)Δ(x1;x2)=−Δ(x2;x1); agreement between inverted units, such as hertz and second, i.e. Δ(x1;x2)=−Δ(k/x1;k/x2)Δ(x1;x2)=−Δ(k/x1;k/x2) thus |Δ(x1;x2)|=|Δ(k/x1;k/x2)||Δ(x1;x2)|=|Δ(k/x1;k/x2)|; and additivity “à la Chasles”, i.e. Δ(x1;x2)+Δ(x2;x3)=Δ(x1;x3)Δ(x1;x2)+Δ(x2;x3)=Δ(x1;x3). Finally, it complies with the Weber–Fechner and Stevens laws.
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Expressing relative differences (in percent) by the difference of natural logarithms
Journal of Mathematical Psychology, 2014Co-Authors: Christian GraffAbstract:Most psychophysical investigations measure stimuli or performance in Systeme International units and use relative differences between them for comparison. In this theoretical note, we propose the ratio’s natural logarithm or the difference between the Napierian logarithms, as a desirable measure of relative differences between two psychophysical quantities. It challenges the more frequently used (x2−x1)/x1(x2−x1)/x1, (x2−x1)/x2(x2−x1)/x2, as well as (x2−x1)/xM(x2−x1)/xM, where x1,x2x1,x2, and xMxM are the initial value in a change, the larger value, and a mean value between x1x1 and x2x2, respectively. As for the three aforementioned expressions, it can be conveniently expressed as a percentage. For two physical measures, x1x1 and x2(x1>0;x2>0), the difference between natural logarithms DNL=Ln(x2)−Ln(x1)=Ln(x2/x1) sits between (x2−x1)/x2(x2−x1)/x2 and (x2−x1)/x1(x2−x1)/x1; it is actually the mean value of (x2−x1)/x(x2−x1)/x for all xx values between x1x1 and x2x2. Unlike other estimates, it satisfies all three of the following properties: symmetry, i.e. Δ(x1;x2)=−Δ(x2;x1)Δ(x1;x2)=−Δ(x2;x1); agreement between inverted units, such as hertz and second, i.e. Δ(x1;x2)=−Δ(k/x1;k/x2)Δ(x1;x2)=−Δ(k/x1;k/x2) thus |Δ(x1;x2)|=|Δ(k/x1;k/x2)||Δ(x1;x2)|=|Δ(k/x1;k/x2)|; and additivity “a la Chasles”, i.e. Δ(x1;x2)+Δ(x2;x3)=Δ(x1;x3)Δ(x1;x2)+Δ(x2;x3)=Δ(x1;x3). Finally, it complies with the Weber–Fechner and Stevens laws.
