You are welcome to download the code and data files (subject to availability) for any of the published papers listed below. However, I would appreciate it if you could kindly notify me and my coauthors if you plan to use them for your own research. Thank you!
Embedded Files
Published & Forthcoming Papers
(Listed in Order of Acceptance Date)Published & Forthcoming Papers
43. Silvia Sarpietro, Yuya Sasaki, & Yulong Wang
"Non-Existent Moments of Earnings Growth"
Journal of Applied Econometrics, forthcoming.
(First Circulation: 2022 - arXiv:2203.08014)
Code & Data for Replication (Coming Soon)
Abstract: This study addresses the limitations of traditional earnings risk measures, which often rely on moments such as variance, skewness, and kurtosis. For heavy-tailed distributions, these moments may not exist, challenging such analyses. We propose robust conditional Pareto exponents as novel measures of earnings risk, with accompanying estimation and inference methods. Using UK NESPD and US PSID data, we find: (1) Moments often fail to exist; (2) Tail risk rises over the life cycle; (3) Job stayers face higher tail risk; and (4) These patterns persist in both the 2007–2008 recession and the 2015–2016 growth period.
42. Yuya Sasaki & Takuya Ura
"Slow Movers in Panel Data"
Econometric Theory, forthcoming.
(First Circulation: 2021 - arXiv:2110.12041)
Code & Data for Replication (Coming Soon)
Abstract: Panel data often contain stayers (units with no within-variations) and slow movers (units with little within-variations). While their presence would not jeopardize the analysis of constant coefficient models, it can impose challenges in the analysis of random coefficient models. The literature proposes an estimator for the average partial effects (APE) that may potentially work robustly in this difficult setting, yet no formal theory has been developed. The literature is also silent about inference for the APE in the presence of stayers and many slow movers. We contribute to this state of the art as follows. First, we develop an asymptotic theory to guarantee that such an estimator is indeed consistent in the presence of stayers and slow movers. Second, we propose its standard error to enable inference for the APE. Third, we relax the existing assumption to allow for ‘many’ slow movers. Fourth, we generalize the existing estimator. Fifth, we establish that this generalized estimator can achieve larger extents of bias reduction and hence faster convergence rates. Simulation studies demonstrate that the conventional 95% confidence interval covers the true value of the APE with37-93% frequencies whereas our proposed one achieves 93-96% coverage frequencies. Using the U.S. Panel Study of Income Dynamics, we find that estimates of the marginal propensity to consume based on our generalized estimator remarkably differ in values from those of the existing estimators. Moreover, the generalized estimator achieves more than three times as small standard errors as those of the existing robust estimator.
41. Yuya Sasaki and Yulong Wang
"Extreme Quantile Treatment Effects under Endogeneity"
Journal of Business & Economic Statistics, forthcoming.
(First Circulation: 2024 - arXiv:2409.03979)
Code & Data for Replication (Coming Soon)
Abstract: We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental variables design and regression discontinuity design, among others. By leveraging regular variation and subsampling, the method ensures good performance even in extreme tails, where data may be sparse. Simulation studies confirm the theoretical properties of our approach. Applying our method to assess the effects of 401(k) participation on net savings, we find significantly negative QTEs for the highest quantiles of the income distribution contrasting with the previously reported positive effects in the intermediate quantiles.
40. Yuya Sasaki & Yulong Wang (2024)
"On Uniform Confidence Intervals for the Tail Index and the Extreme Quantile"
Journal of Econometrics, 244 (1), 105865.
(First Circulation: 2022 - arXiv:2210.05074)
Code & Data for Replication
Abstract: This paper presents two results concerning uniform confidence intervals for the tail index and the extreme quantile. First, we show that there exists a lower bound of the length for confidence intervals that satisfy the correct uniform coverage over a nonparametric family of tail distributions. Second, in light of the impossibility result, we construct honest confidence intervals that are uniformly valid by incorporating the worst-case bias in the nonparametric family. The proposed method is applied to simulated data and real data of financial time series.
39. Harold D. Chiang, Bruce E. Hansen, & Yuya Sasaki
"Standard Errors for Two-Way Clustering with Serially Correlated Time Effects"
Review of Economics and Statistics, forthcoming.
(First Circulation: 2022 - arXiv:2201.11304)
Abstract: We propose improved standard errors and an asymptotic theory for two-way clustered panels. Our theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods. Our asymptotic distribution theory is the first which allows for this level of inter-dependence. Under weak conditions, we demonstrate that OLS is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically standard normal. The results extend to two-way fixed-effect models; we argue that two-way clustering is still necessary even if two-way fixed effects are included. Simulation and empirical illustration are provided.
38. Yuya Sasaki & Takuya Ura
"Welfare Analysis via Marginal Treatment Effects"
Econometric Theory, forthcoming.
(First Circulation: 2020 - arXiv:2012.07624)
Code & Data for Replication
Abstract: We consider a causal structure with endogeneity, i.e., unobserved confoundedness, where an instrumental variable is available. In this setting, we show that the mean social welfare function can be identified and represented via the marginal treatment effect as the operator kernel. This representation result can be applied to a variety of statistical decision rules for treatment choice, including plug-in rules, Bayes rules, and empirical welfare maximization rules. Focusing on the application of the empirical welfare maximization framework, we provide convergence rates of the worst-case average welfare loss (regret).
37. Yuya Sasaki (2024)
"On "Imputation of Counterfactual Outcomes When the Errors Are Predictable": Viewing the PUP as the DID and the LDV"
Journal of Business & Economic Statistics, 42 (4), pp. 1133-1136.
Abstract: I discuss the practical unbiased predictor (PUP; Gonçalves and Ng) from the viewpoint of the literature on identification in event studies. The PUP can be seen as the prediction based on a generalized estimand that encompasses both the difference-in-differences (DID) and the lagged dependent variable (LDV). This feature of the PUP allows for a doubly robust property that the identification is achieved when either the parallel trend assumption or the LDV assumption holds at the expense of richer data. Furthermore, in this case, the bracketing property implies that the PUP identifying the true causal effect is bounded below by the LDV and above by the DID.
36. Xinwei Ma, Yuya Sasaki, & Yulong Wang
"Testing Limited Overlap"
Econometric Theory, forthcoming.
(First Circulation: 2022 - Presented at University of Colorado - Boulder)
Code & Data for Replication
Abstract: xtreme propensity scores arise in observational studies when treated and control units have very different characteristics. This is commonly referred to as limited overlap. In this paper, we propose a formal statistical test that helps assess the degree of limited overlap. Rejecting the null hypothesis in our test indicates either no or very mild degree of limited overlap and hence reassures that standard treatment effect estimators will be well behaved. One distinguishing feature of our test is that it only requires the use of a few extreme propensity scores, which is in stark contrast to other methods that require consistent estimates of some tail index. Without the need to extrapolate using observations far away from the tail, our procedure is expected to exhibit excellent size properties, a result that is also borne out in our simulation study.
35. Tong Li & Yuya Sasaki (2024)
"Identification of Heterogeneous Elasticities in Gross-Output Production Functions"
Journal of Econometrics, 238 (2), 105637.
(First Circulation: 2017 - arXiv:1711.10031)
Code & Data for Replication
Abstract: This paper presents the identification of heterogeneous elasticities in gross-output production functions with non-separable unobserved productivity. We propose that the ex-ante flexible input cost shares identify the heterogeneous output elasticities with respect to flexible inputs for each firm. Applying the proposed method to a panel of firms in the food production industry in Chile, we find that the extent of heterogeneity in the output elasticities with respect to intermediate inputs has significantly declined from 1985 to 1995, with its distribution tending to concentrate toward zero as time progresses.
34. Yuya Sasaki & Yulong Wang (2024)
"Extreme Changes in Changes"
Journal of Business & Economic Statistics, 42 (2), pp. 812-824.
(First Circulation: 2022 - arXiv:2211.14870)
Abstract: Policy analysts are often interested in treating the units with extreme outcomes, such as infants with extremely low birth weights. Existing changes-in-changes (CIC) estimators are tailored to middle quantiles and do not work well for such subpopulations. This article proposes a new CIC estimator to accurately estimate treatment effects at extreme quantiles. With its asymptotic normality, we also propose a method of statistical inference, which is simple to implement. Based on simulation studies, we propose to use our extreme CIC estimator for extreme quantiles, while the conventional CIC estimator should be used for intermediate quantiles. Applying the proposed method, we study the effects of income gains from the 1993 EITC reform on infant birth weights for those in the most critical conditions. This article is accompanied by a Stata command.
33. Ji Hyung Lee, Yuya Sasaki, Alexis Akira Toda, & Yulong Wang (2024)
"Tuning Parameter-Free Nonparametric Density Estimation from Tabulated Summary Data"
Journal of Econometrics, 238 (1), 105568.
(First Circulation: 2022 - arXiv:2204.05480)
Code & Data for Replication
Abstract: Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns. Motivated by this practical feature, we propose a novel nonparametric density estimation method from tabulated summary data based on maximum entropy and prove its strong uniform consistency. Unlike existing kernel-based estimators, our estimator is free from tuning parameters and admits a closed-form density that is convenient for post-estimation analysis. We apply the proposed method to the tabulated summary data of the U.S. tax returns to estimate the income distribution.
32. Jooyoung Cha, Harold D. Chiang, & Yuya Sasaki
"Inference in High-Dimensional Regression Models without the Exact or Lp Sparsity"
Review of Economics and Statistics, forthcoming.
(First Circulation: 2021 - arXiv:2108.09520)
Abstract: This paper proposes a new method of inference in high-dimensional regression models and high-dimensional IV regression models. Estimation is based on a combined use of the orthogonal greedy algorithm, high-dimensional Akaike information criterion, and double/debiased machine learning. The method of inference for any low-dimensional subvector of high-dimensional parameters is based on a root-N asymptotic normality, which is shown to hold without requiring the exact sparsity condition or the Lp sparsity condition. Simulation studies demonstrate superior finite-sample performance of this proposed method over those based on the LASSO or the random forest, especially under less sparse models. We illustrate an application to production analysis with a panel of Chilean firms.
31. Harold D. Chiang, Jiatong Li & Yuya Sasaki (2025)
"Algorithmic Subsampling under Multiway Clustering"
Econometric Theory, 41 (1), 79-122.
(First Circulation: 2021 - arXiv:2103.00557)
Abstract: This paper proposes a novel method of algorithmic subsampling (data sketching) for multiway cluster dependent data. We establish a new uniform weak law of large numbers and a new central limit theorem for the multiway algorithmic subsample means. Consequently, we discover an additional advantage of the algorithmic subsampling that it allows for robustness against potential degeneracy, and even non-Gaussian degeneracy, of the asymptotic distribution under multiway clustering. Simulation studies support this novel result, and demonstrate that inference with the algorithmic subsampling entails more accuracy than that without the algorithmic subsampling. Applying these basic asymptotic theories, we derive the consistency and the asymptotic normality for the multiway algorithmic subsampling generalized method of moments estimator and for the multiway algorithmic subsampling M-estimator. We illustrate an application to scanner data.
30. Yuya Sasaki, Yuya Takahashi, Yi Xin, & Yingyao Hu (2023)
"Dynamic Discrete Choice Models with Incomplete Data: Sharp Identification"
Journal of Econometrics, 236 (1), 105461.
(First Circulation: 2015 - Presented at MOVE-Barcelona GSE Summer Forum)
Code & Data for Replication
Abstract: In many empirical studies, the states that are relevant for economic agents to make decisions may not be included in the data to which researchers have access. This problem often arises in the context of monotone industries. In this paper, we develop the sharp identified sets of structural parameters and counterfactuals for dynamic discrete choice models when empirical data do not cover realizations of relevant states. We use simulation studies to confirm the theoretical property of the sharpness. Applying the proposed method to the annual Toyo Keizai database, we study the behaviors of Japanese firms on foreign direct investments in China without observing the future states after Chinese economy slows down.
29. Xavier D’Haultfoeuille, Stefan Hoderlein, & Yuya Sasaki (2023)
"Nonparametric Difference-in-Differences in Repeated Cross Sections with Continuous Treatments"
Journal of Econometrics, 234 (2), pp. 664-690.
(First Circulation: 2013 - Cemmap CWP40/13)
Code & Data for Replication
Abstract: This paper studies the identification of causal effects of a continuous treatment using a new difference-in-difference strategy. Our approach allows for endogeneity of the treatment, and employs repeated cross-sections. It requires an exogenous change over time which affects the treatment in a heterogeneous way, stationarity of the distribution of unobservables and a rank invariance condition on the time trend. On the other hand, we do not impose any functional form restrictions or an additive time trend, and we are invariant to the scaling of the dependent variable. Under our conditions, the time trend can be identified using a control group, as in the binary difference-indifferences literature. In our scenario, however, this control group is defined by the data. We then identify average and quantile treatment effect parameters. We develop corresponding nonparametric estimators and study their asymptotic properties. Finally, we apply our results to the effect of disposable income on consumption.
28. Yuya Sasaki, Takuya Ura, & Yichong Zhang (2022)
"Unconditional Quantile Regression with High Dimensional Data"
Quantitative Economics, 13 (3), pp. 955-978.
Wiley Top Cited Article 2022-2023 in Quantitative Economics
(First Circulation: 2020 - arXiv:2007.13659)
Code & Data for Replication
Abstract: Credible counterfactual analysis requires high-dimensional controls. This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel doubly robust score for double/debiased estimation and inference for the unconditional quantile regression (Firpo, Fortin, and Lemieux, 2009) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference for the Lasso double/debiased estimator, and develop asymptotic theories to guarantee that the bootstrap works. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that i) marginal effects of counterfactually extending the duration of the exposure to the Job Corps program are globally positive across quantiles regardless of definitions of the treatment and outcome variables, and that ii) these counterfactual effects are larger for higher potential earners than lower potential earners regardless of whether we define the outcome as the level or its logarithm.
27. Harold D. Chiang, Joel Rodrigue, & Yuya Sasaki (2023)
"Post-Selection Inference in Three-Dimensional Panel Data"
Econometric Theory, 39 (3), pp. 623-658.
(First Circulation: 2019 - arXiv:1904.00211)
Code & Data for Replication
Abstract: Three-dimensional panel models are widely used in empirical analysis. Researchers use various combinations of fixed effects for three-dimensional panels while the correct specification is unknown. When one imposes a parsimonious model and the true model is rich in complexity, the fitted model inevitably incurs the consequences of misspecification including potential bias. When a richly specified model is employed and the true model is parsimonious, then the consequences typically include a poor fit with larger standard errors than necessary. It is therefore useful for researchers to have good model selection techniques that assist in determining the ‘true’ model or a satisfactory approximation. In this light, Lu, Miao and Su (2021) propose methods of model selection. We advance this literature by proposing a method of post-selection inference for regression parameters. Despite our use of the lasso technique as the means of model selection, our assumptions allow for many and even all fixed effects to be nonzero. This property is important to avoid a degenerate distribution of fixed effects which often reflect economic sizes of countries in gravity analyses of trade. Using an international trade database, we document evidence that our key assumption of approximately sparse fixed effects is plausibly satisfied for gravity analyses of trade. We also establish the uniform size control over alternative data generating processes of fixed effects. Simulation studies demonstrate that the proposed method is less biased than under-fitting fixed effect estimators, is more efficient than over-fitting fixed effect estimators, and robustly allows for inference that is as accurate as the oracle estimator.
26. Yuya Sasaki & Yulong Wang (2023)
"Diagnostic Testing of Finite Moment Conditions for the Consistency and Root-N Asymptotic Normality of the GMM and M Estimators"
Journal of Business & Economic Statistics, 41 (2), pp. 339-348.
(First Circulation: 2020 - arXiv:2006.02541)
Abstract: Common econometric analyses based on point estimates, standard errors, and confidence intervals presume the consistency and the root-n asymptotic normality of the GMM or M estimators. However, their key assumptions that data entail finite moments may not be always satisfied in applications. This paper proposes a method of diagnostic testing for these key assumptions with applications to both simulated and real data sets.
25. Taisuke Otsu, Martin Pesendorfer, Yuya Sasaki, & Yuya Takahashi (2022)
"Estimation of (Static or Dynamic) Games under Equilibrium Multiplicity"
International Economic Review, 63 (3), pp. 1165-1188.
(First Circulation: 2020 - LSE STICED)
Abstract: We propose a multiplicity-robust estimation method for (static or dynamic) games. The method allows for distinct behaviors and strategies across markets by treating market specific behaviors as correlated latent variables, with their conditional probability measure treated as an infinite-dimensional nuisance parameter. Instead of solving the intermediate problem which requires optimization over the infinite dimensional set, we consider the equivalent dual problem which entails optimization over only a finite-dimensional Euclidean space. This property allows for a practically feasible characterization of the identified region for the structural parameters. We apply the estimation method to newspaper market previously studied in Gentzkow et al. (2014) to characterize the identified region of marginal costs.
24. Harold D. Chiang, Kengo Kato, & Yuya Sasaki (2023)
"Inference for High-Dimensional Exchangeable Arrays"
Journal of the American Statistical Association, 118 (543) pp. 1595-1605.
(First Circulation: 2020 - arXiv:2009.05150)
Code & Data for Replication
Abstract: We consider inference for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes. For both exchangeable arrays, we first derive high-dimensional central limit theorems over the rectangles and subsequently develop novel multiplier bootstraps with theoretical guarantees. These theoretical results rely on new technical tools such as Hoeffding-type decomposition and maximal inequalities for the degenerate components in the Hoeffiding-type decomposition for the exchangeable arrays. We exhibit applications of our methods to uniform confidence bands for density estimation under joint exchangeability and penalty choice for ℓ1-penalized regression under separate exchangeability. Extensive simulations demonstrate precise uniform coverage rates. We illustrate by constructing uniform confidence bands for international trade network densities.
23. Yuya Sasaki & Takuya Ura (2023)
"Estimation and Inference for Policy Relevant Treatment Effects"
Journal of Econometrics, 234 (2), pp. 394-450.
(First Circulation: 2018 - arXiv:1805.11503)
Abstract: The policy relevant treatment effect (PRTE) measures the average effect of switching from a status-quo policy to a counterfactual policy. Estimation of the PRTE involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects. These preliminary estimators can affect the asymptotic distribution of the PRTE estimator in complicated and intractable manners. In this light, we propose an orthogonal score for double debiased estimation of the PRTE, whereby the asymptotic distribution of the PRTE estimator is obtained without any influence of preliminary parameter estimators as far as they satisfy mild requirements of convergence rates. To our knowledge, this paper is the first to develop limit distribution theories for inference about the PRTE.
22. Harold D. Chiang, Kengo Kato, Yukun Ma, & Yuya Sasaki (2022)
"Multiway Cluster Robust Double/Debiased Machine Learning"
Journal of Business & Economic Statistics, 40 (3), pp. 1046-1056.
(First Circulation: 2019 - arXiv:1909.03489)
Please Note: Correction of Table 2
Code & Data for Replication
Abstract: This paper investigates double/debiased machine learning (DML) under multiway clustered sampling environments. We propose a novel multiway cross fitting algorithm and a multiway DML estimator based on this algorithm. We also develop a multiway cluster robust standard error formula. Simulations indicate that the proposed procedure has favorable finite sample performance. Applying the proposed method to market share data for demand analysis, we obtain larger two-way cluster robust standard errors than non-robust ones.
21. Yuya Sasaki & Takuya Ura (2022)
"Estimation and Inference for Moments of Ratios with Robustness against Large Trimming Bias"
Econometric Theory, 38 (1), pp. 66-112.
(First Circulation: 2017 - arXiv:1709.00981)
Abstract: Empirical researchers often trim observations with small denominator A when they estimate moments of the form E[B/A]. Large trimming is a common practice to mitigate variance, but it incurs large trimming bias. This paper provides a novel method of correcting large trimming bias. If a researcher is willing to assume that the joint distribution between A and B is smooth, then a large trimming bias may be estimated well. With the bias correction, we also develop a valid and robust inference result for E[B/A].
20. Yuya Sasaki & Yulong Wang (2022)
"Fixed-k Inference for Conditional Extremal Quantiles"
Journal of Business & Economic Statistics, 40 (2), pp. 829-837.
(First Circulation: 2019 - arXiv:1909.00294)
Abstract: We develop a new extreme value theory for repeated cross-sectional and longitudinal/panel data to construct asymptotically valid confidence intervals (CIs) for conditional extremal quantiles from a fixed number k of nearest-neighbor tail observations. As a by-product, we also construct CIs for extremal quantiles of coefficients in linear random coefficient models. For any fixed k, the CIs are uniformly valid without parametric assumptions over a set of nonparametric data generating processes associated with various tail indices. Simulation studies show that our CIs exhibit superior small-sample coverage and length properties than alternative nonparametric methods based on asymptotic normality. Applying the proposed method to Natality Vital Statistics, we study factors of extremely low birth weights. We find that signs of major effects are the same as those found in preceding studies based on parametric models, but with different magnitudes.
19. Xavier D’Haultfoeuille, Stefan Hoderlein, & Yuya Sasaki (2024)
"Testing and Relaxing the Exclusion Restriction in the Control Function Approach"
Journal of Econometrics ( Themed Issue Honoring Whitney Newey ), 240 (2), 105075.
(First Circulation: 2014 - Presented at Warwick U)
Abstract: The control function approach which employs an instrumental variable excluded from the outcome equation is a very common solution to deal with the problem of endogeneity in nonseparable models. Exclusion restrictions, however, are frequently controversial. We first argue that, in a nonparametric triangular structure typical of the control function literature, one can actually test this exclusion restriction provided the instrument satisfies a local irrelevance condition. Second, we investigate identification without such exclusion restrictions, i.e., if the "instrument" that is independent of the unobservables in the outcome equation also directly affects the outcome variable. In particular, we show that identification of average causal effects can be achieved in the two most common special cases of the general nonseparable model: linear random coefficients models and single index models.
18. Kengo Kato, Yuya Sasaki, & Takuya Ura (2021)
"Robust Inference in Deconvolution"
Quantitative Economics, 12 (1), pp. 109-142.
(First Circulation: 2018 - arXiv:1808.09375)
Abstract: Kotlarski's identity has been widely used in applied economic research based on repeated-measurement or panel models with latent variables. However, how to conduct inference for these models has been an open question for two decades. This paper addresses this open problem by constructing a novel confidence band for the density function of a latent variable in repeated measurement error model. The confidence band builds on our finding that we can rewrite Kotlarski's identity as a system of linear moment restrictions. Our approach is robust in that we do not require the completeness. The confidence band controls the asymptotic size uniformly over a class of data generating processes, and it is consistent against all fixed alternatives. Simulation studies support our theoretical results.
17. Arie Beresteanu & Yuya Sasaki (2021)
"Quantile Regression with Interval Data"
Econometric Reviews ( Special Issue Honoring Cheng Hsiao ), 40 (5), pp. 562-583.
(First Circulation: 2017 - arXiv:1710.07575)
Abstract: This paper investigates the identification of quantiles and quantile regression parameters when observations are set valued. We define the identification set of quantiles of random sets in a way that extends the definition of quantiles for regular random variables. We then give sharp characterization of this set by extending concepts from random set theory. For quantile regression parameters, we show that the identification set is characterized by a system of conditional moment inequalities. This characterization extends that of parametric quantile regression for regular random variables. Estimation and inference theories are developed for continuous cases, discrete cases, nonparametric conditional quantiles, and parametric quantile regressions. A fast computational algorithm of set linear programming is proposed. Monte Carlo experiments support our theoretical properties.
16. Heng Chen, Harold D. Chiang, & Yuya Sasaki (2020)
"Quantile Treatment Effects in Regression Kink Designs"
Econometric Theory, 36 (6), pp. 1167-1191.
(First Circulation: 2017 - arXiv:1703.05109)
Abstract: The literature on regression kink designs develops identification results for average effects of continuous treatments (Nielsen et al., 2010, American Economic Journal: Economic Policy 2, 185–215; Card et al., 2015, Econometrica 83, 2453–2483), average effects of binary treatments (Dong, 2018, Jump or Kink? Identifying Education Effects by Regression Discontinuity Design without the Discontinuity), and quantile-wise effects of continuous treatments (Chiang and Sasaki, 2019, Journal of Econometrics 210, 405–433), but there has been no identification result for quantile-wise effects of binary treatments to date. In this article, we fill this void in the literature by providing an identification of quantile treatment effects in regression kink designs with binary treatment variables. For completeness, we also develop large sample theories for statistical inference, present a practical guideline on estimation and inference, conduct simulation studies, and provide an empirical illustration.
15. Yingyao Hu, Guofang Huang, & Yuya Sasaki (2020)
"Estimating Production Functions with Robustness against Errors in the Proxy Variable"
Journal of Econometrics, 215 (2), pp. 375-398.
(First Circulation: 2011 - Cemmap CWP35/11)
Abstract: This paper proposes a new approach to the identification and estimation of production functions. It extends the literature on the structural estimation of production functions, which dates back to the seminal work of Olley and Pakes (1996), by relaxing the scalar-unobservable assumption about the proxy variables. The key additional assumption needed in the identification argument is the existence of two conditionally independent proxy variables. The proposed generalized method of moment (GMM) estimator is flexible and straightforward to apply. The method is applied to study how rapidly firms in the Chilean food-product industry adjust their inputs in response to shocks to their productivity.
14. Kengo Kato & Yuya Sasaki (2019)
"Uniform Confidence Bands for Nonparametric Errors-in-Variables Regression"
Journal of Econometrics, 213 (2), pp. 516-555.
(First Circulation: 2017 - arXiv:1702.03377)
Abstract: This paper develops a method to construct uniform confidence bands for a nonparametric regression function where a predictor variable is subject to a measurement error. We allow for the distribution of the measurement error to be unknown, but assume the availability of validation data or repeated measurements on the latent predictor variable. The proposed confidence band builds on the deconvolution kernel estimation and a novel application of the multiplier bootstrap method. We establish asymptotic validity of the proposed confidence band. To our knowledge, this is the first paper to derive asymptotically valid uniform confidence bands for nonparametric errors-in-variables regression.
13. Harold D. Chiang, Yu-Chin Hsu, & Yuya Sasaki (2019)
"Robust Uniform Inference for Quantile Treatment Effects in Regression Discontinuity Designs"
Journal of Econometrics, 211 (2), pp. 589-618.
(First Circulation: 2017 - arXiv:1702.04430)
Abstract: The practical importance of inference with robustness against large bandwidths for causal effects in regression discontinuity and kink designs is widely recognized. Existing robust methods cover many cases, but do not handle uniform inference for CDF and quantile processes in fuzzy designs. In this light, this paper extends the literature by developing a unified framework of inference with robustness against large bandwidths that applies to uniform inference for quantile treatment effects in fuzzy designs, as well as all the other cases. We present Monte Carlo simulation studies and an empirical application for evaluations of the Oklahoma pre-K program.
12. Yingyao Hu, Robert Moffitt, & Yuya Sasaki (2019)
"Semiparametric Estimation of the Canonical Permanent-Transitory Model of Earnings Dynamics"
Quantitative Economics, 10 (4), pp. 1495-1536.
(First Circulation: 2015 - Presented at ES World Congress)
Abstract: This paper presents identification and estimation results for a flexible state space model. Our modification of the canonical model allows the permanent component to follow a unit root process and the transitory component to follow a semiparametric model of a higher‐order autoregressive‐moving‐average (ARMA) process. Using panel data of observed earnings, we establish identification of the nonparametric joint distributions for each of the permanent and transitory components over time. We apply the identification and estimation method to the earnings dynamics of U.S. men using the Panel Survey of Income Dynamics (PSID). The results show that the marginal distributions of permanent and transitory earnings components are more dispersed, more skewed, and have fatter tails than the normal and that earnings mobility is much lower than for the normal. We also find strong evidence for the existence of higher‐order ARMA processes in the transitory component, which lead to much different estimates of the distributions of and earnings mobility in the permanent component, implying that misspecification of the process for transitory earnings can affect estimated distributions of the permanent component and estimated earnings dynamics of that component. Thus our flexible model implies earnings dynamics for U.S. men different from much of the prior literature.
11. Harold D. Chiang & Yuya Sasaki (2019)
"Causal Inference by Quantile Regression Kink Designs"
Journal of Econometrics, 210 (2), pp. 405-433.
(First Circulation: 2016 - arXiv:1605.09773)
Abstract: The quantile regression kink design (QRKD) is proposed by empirical researchers, but its causal interpretation remains unknown. We show that the QRKD estimand measures a weighted average of heterogeneous marginal effects at respective conditional quantiles of outcome given a designed kink point. We also derive limit processes for the QRKD estimator to conduct statistical inference on heterogeneous treatment effects using the QRKD. Applying our methods to the Continuous Wage and Benefit History Project (CWBH) data, we find heterogeneous positive causal effects of unemployment insurance benefits on unemployment durations. These effects are larger for individuals with longer unemployment durations.
10. Kengo Kato & Yuya Sasaki (2018)
"Uniform Confidence Bands in Deconvolution with Unknown Error Distribution"
Journal of Econometrics, 207 (1), pp. 129-161.
(First Circulation: 2016 - arXiv:1608.02251)
Abstract: This paper develops a method to construct uniform confidence bands in deconvolution when the error distribution is unknown. Simulation studies demonstrate the performance of the multiplier bootstrap confidence band in the finite sample. We apply our method to the Outer Continental Shelf (OCS) Auction Data and draw confidence bands for the density of common values of mineral rights on oil and gas tracts. We also present an application of our main theoretical result specifically to additive fixed-effect panel data models, and we draw confidence bands for the density of the total factor productivity in a manufacturing industry in Chile.
9. Irene Botosaru & Yuya Sasaki (2018)
"Nonparametric Heteroskedasticity in Persistent Panel Processes: An Application to Earnings Dynamics"
Journal of Econometrics, 203 (2), pp. 283-396.
(First Circulation: 2014 - Presented at U Tokyo Conference)
Abstract: This paper considers a dynamic panel model where a latent state variable follows a unit root process with nonparametric heteroskedasticity. We develop constructive nonparametric identification and estimation of the skedastic function. Applying this method to the Panel Survey of Income Dynamics (PSID) in the framework of earnings dynamics, we found that workers with lower pre-recession permanent earnings had higher earnings risk during the three most recent recessions.
8. Yingyao Hu & Yuya Sasaki (2018)
"Closed-Form Identification of Dynamic Discrete Choice Models with Proxies for Unobserved State Variables"
Econometric Theory, 34 (1), pp. 166-185.
(First Circulation: 2013 - Presented at George Washington U)
Abstract: Proxies for unobserved skills and technologies are increasingly available in empirical data. For dynamic discrete choice models of forward-looking agents where a continuous state variable is unobserved but its proxy is available, we derive closed-form identification of the structure by explicitly solving integral equations. In the first step, we derive closed-form identification of Markov components, including the conditional choice probabilities and the law of state transition. In the second step, we plug in these first-step identifying formulas to obtain primitive structural parameters of dynamically optimizing agents.
7. Yuya Sasaki & Yi Xin (2017)
"Unequal Spacing in Dynamic Panel Data: Identification and Estimation"
Journal of Econometrics, 196 (2), pp. 320-330.
(First Circulation: 2014 - Presented at NY Camp Econometrics)
Abstract: We propose conditions under which parameters of fixed-effect dynamic models are identified with unequally spaced panel data. Under predeterminedness, weak stationarity, and empirically testable rank conditions, AR(1) parameters are identified given the availability of “two pairs of two consecutive time gaps”, which generalizes “two pairs of two consecutive time periods”. This result extends to models with multiple covariates, higher-order autoregressions, and partial linearity. Applying our method to the NLS Original Cohorts: Older Men, where personal interviews took place in 1966, 67, and 69, we analyze the earnings dynamics in the old time, and compare the results with more recent ones.
6. Yingyao Hu & Yuya Sasaki (2017)
"Identification of Paired Nonseparable Measurement Error Models"
Econometric Theory, 33 (4), 955-979.
(First Circulation: 2012 - Presented at GNYMAEQ)
Abstract: This paper studies the paired nonseparable measurement error models, where two measurements, X and Y, are produced by mutually independent unobservables, U, V, and W, through the system, X = g(U,V) and Y = h(U,W). We propose restrictions to identify the marginal distribution of the common component U and the conditional distributions of X and Y given U. Applying this method to twin panel data, we find the following robust reporting patterns for years of education: (1) self reports are accurate only when the true years of education are 16 or 18, typically corresponding to advanced university degrees in the US education system; (2) sibling reports are accurate whenever the true years of education are 12, 14, 16, and 18, which are typical diploma years.
5. Ryutah Kato & Yuya Sasaki (2017)
"On Using Linear Quantile Regressions for Causal Inference"
Econometric Theory, 33 (3), 664-690.
(First Circulation: 2016 - Econometric Theory)
Abstract: We show that the slope parameter of the linear quantile regression measures a weighted average of the local slopes of the conditional quantile function. Extending this result, we also show that the slope parameter measures a weighted average of the partial effects for a general structural function. Our results support the use of linear quantile regressions for causal inference in the presence of nonlinearity and multivariate unobserved heterogeneity. The same conclusion applies to linear regressions.
4. Yuya Sasaki (2015)
"Heterogeneity and Selection in Dynamic Panel Data"
Journal of Econometrics, 188 (1), pp. 236-249.
(First Circulation: 2011 - Presented at NASM)
Abstract: The data generating process (DGP) for generic dynamic panel data consists of a law of state dynamics g, a selection or attrition rule h, and an initial condition F. I study nonparametric identifiability of this complete DGP (g,h,F) using short unbalanced panel data, allowing for nonseparability between observed states and unobserved heterogeneity in each of g, h and F. For T⩾3, the DGP is identified by using a proxy variable. For T⩾6, the three additional periods construct a proxy, and thus the DGP is identified without an auxiliary variable.
3. Sophocles Mavroeidis, Yuya Sasaki, & Ivo Welch (2015)
"Estimation of Heterogeneous Autoregressive Parameters with Short Panel Data"
Journal of Econometrics, 188 (1), pp. 219-235.
(First Circulation: 2012 - Presented at AMES)
Abstract: This paper presents a maximum likelihood approach to estimation of cross sectional distributions of heterogeneous autoregressive (AR) parameters with short panel data. We construct a panel likelihood by integrating the unknown cross sectional density of heterogeneous AR parameters with respect to a known time-series data generating kernel. The solution to this extremal criterion recovers the unknown density of heterogeneous AR parameters. Applying our method to a model of employment dynamics with the firm-level data of Arellano and Bond (1991), we find that adjustment rates of employment are significantly heterogeneous across firms.
2. Yingyao Hu & Yuya Sasaki (2015)
"Closed-Form Estimation of Nonparametric Models with Non-Classical Measurement Errors"
Journal of Econometrics, 185 (2), pp. 392-408.
(First Circulation: 2013 - Presented at NASM)
Abstract: This paper proposes closed-form estimators for nonparametric regressions using two measurements with non-classical errors. One (administrative) measurement has location-/scale-normalized errors, but the other (survey) measurement has endogenous errors with arbitrary location and scale. For this setting of data combination, we derive closed-form identification of nonparametric regressions, and practical closed-form estimators that perform well with small samples. Applying this method to NHANES III, we study how obesity explains health care usage. Clinical measurements and self reports of BMI are used as two measurements with normalized errors and endogenous errors, respectively. We robustly find that health care usage increases with obesity.
1. Yuya Sasaki (2015)
"What Do Quantile Regressions Identify for General Structural Functions?"
Econometric Theory, 31 (5), pp. 1102-1116.
(First Circulation: 2014 - Econometric Theory)
Abstract: This paper shows what quantile regressions identify for general structural functions. Under fairly mild conditions, the quantile partial derivative identifies a weighted average of heterogeneous structural partial effects among the subpopulation of individuals at the conditional quantile of interest. This result justifies the use of quantile regressions as means of measuring heterogeneous causal effects for a general class of structural functions with multiple unobservables.
Articles on Statistical Software
Articles on Statistical Software
iii. Yingyao Hu, Guofang Huang, & Yuya Sasaki
"robustpf: Software for Robust Estimation of Production Functions"
The Stata Journal, 23 (1), pp. 86-96.
Abstract: We introduce a new command, robustpf, to estimate parameters of Cobb-Douglas production functions. This method is robust against two potential problems. First, it is robust against optimization errors in firms' input choice, unobserved idiosyncratic cost shocks, and/or measurement errors in proxy variables. In particular, this feature relaxes the conventional assumption of scalar unobservables. Second, it is also robust against the functional dependence problem of static input choice, which is known today as a cause of identification failure. The main method is proposed by Hu, Huang, and Sasaki (2020).
ii. Yuya Sasaki & Yi Xin (2022)
"xtusreg: Software for Dynamic Panel Regression under Irregular Time Spacing"
The Stata Journal, 22 (3), pp. 713-724
Abstract: We introduce a new command, xtusreg, which estimates parameters of fixed-effect dynamic panel regression models under unequal time spacing. After reviewing the method, we examine the finite sample performance of the command using simulated data. We also illustrate with National Longitudinal Survey Original Cohorts Older Men whose personal interviews took place in unequally spaced years of 1966, 1967, 1969, 1971, 1976, 1981, and 1990. The methods underlying xtusreg are those discussed by Sasaki and Xin (2017).
i. Yuya Sasaki & Takuya Ura (2022)
"Average Treatment Effect Estimates Robust to the 'Limited Overlap' Problem: robustate"
The Stata Journal, 22 (2), pp. 344-354.
Abstract: We introduce a new command, robustate, that executes the inverseprobability weighting estimation and inference for the average treatment effect with robustness against limited overlap (that is, weak satisfaction of the common support condition). This command produces estimates, standard errors, p-values, and confidence intervals for the average treatment effect. The utility of the command is demonstrated with both simulated and real data of right heart catheterization. These illustrations show that the proposed estimator implemented by the robustate command indeed exhibits more robustness against limited overlap than the traditional inverse-probability weighting estimator. The main method of the command is proposed in Sasaki and Ura (2022, Econometric Theory 38: 66–112).
Papers under Revision/Resubmission
Papers under Revision/Resubmission
Hao Dong & Yuya Sasaki (1st Version 2022)
"Inference on Buffer-Stock Saving"
Abstract: Abstract. This paper proposes a density-weighted average derivative estimator based on two noisy measures of a latent regressor. Both measures have classical errors with possibly asymmetric distributions. We show that the proposed estimator achieves the root-n rate of convergence, and derive its asymptotic normal distribution for statistical inference. Simulation studies demonstrate excellent small-sample performance supporting the root-n asymptotic normality. Based on the proposed estimator, we construct a formal test on the sub-unity of the marginal propensity to consume out of permanent income (MPCP) under a nonparametric consumption model and a permanent-transitory model of income dynamics with nonparametric distribution. Applying the test to four recent waves of U.S. Panel Study of Income Dynamics (PSID), we reject the null hypothesis of the unit MPCP in favor of a sub-unit MPCP, supporting the buffer-stock model of saving.
Nan Liu, Yanbo Liu, and Yuya Sasaki (1st Version 2024)
"Estimation and Inference for Causal Functions with Multiway Clustered Data"
Abstract: This paper proposes methods of estimation and uniform inference for a general class of causal functions, such as the conditional average treatment effects and the continuous treatment effects, under multiway clustering. The causal function is identified as a conditional expectation of an adjusted (Neyman-orthogonal) signal that depends on high-dimensional nuisance parameters. We propose a two-step procedure where the first step uses machine learning to estimate the high-dimensional nuisance parameters. The second step projects the estimated Neyman-orthogonal signal onto a dictionary of basis functions whose dimension grows with the sample size. For this two-step procedure, we propose both the full-sample and the multiway cross-fitting estimation approaches. A functional limit theory is derived for these estimators. To construct the uniform confidence bands, we develop a novel resampling procedure, called the multiway cluster-robust sieve score bootstrap, that extends the sieve score bootstrap (Chen and Christensen, 2018) to the novel setting with multiway clustering. Extensive numerical simulations showcase that our methods achieve desirable finite-sample behaviors. We apply the proposed methods to analyze the causal relationship between mistrust levels in Africa and the historical slave trade. Our analysis rejects the null hypothesis of uniformly zero effects and reveals heterogeneous treatment effects, with significant impacts at higher levels of trade volumes.
Yechan Park and Yuya Sasaki (1st Version 2024)
"The Informativeness of Combined Experimental and Observational Data under Dynamic Selection"
Abstract: This paper addresses the challenge of estimating the Average Treatment Effect on the Treated Survivors (ATETS; Vikstrom et al., 2018) in the absence of long-term experimental data, utilizing available long-term observational data instead. We establish two theoretical results. First, it is impossible to obtain informative bounds for the ATETS with no model restriction and no auxiliary data. Second, to overturn this negative result, we explore as a promising avenue the recent econometric developments in combining experimental and observational data (e.g., Athey et al., 2020, 2019); we indeed find that exploiting short-term experimental data can be informative without imposing classical model restrictions. Furthermore, building on Chesher and Rosen (2017), we explore how to systematically derive sharp identification bounds, exploiting both the novel data-combination principles and classical model restrictions. Applying the proposed method, we explore what can be learned about the long-run effects of job training programs on employment without long-term experimental data.
Harold D. Chiang, Yukun Ma, Joel Rodrigue & Yuya Sasaki (1st Version 2021)
"Double/Debiased Machine Learning for Dyadic Data"
Abstract: This paper presents novel methods and theories for estimation and inference about parameters in econometric models using machine learning of nuisance parameters when data are dyadic. We propose a dyadic cross fitting method to remove over-fitting biases under arbitrary dyadic dependence. Together with the use of Neyman orthogonal scores, this novel cross fitting method enables root-n consistent estimation and inference robustly against dyadic dependence. We illustrate an application of our general framework to high-dimensional network link formation models. With this method applied to empirical data of international economic networks, we reexamine determinants of free trade agreements (FTA) viewed as links formed in the dyad composed of world economies. We document that standard methods may lead to misleading conclusions for numerous classic determinants of FTA formation due to biased point estimates or standard errors which are too small.
Working Papers
Working Papers
Tobias Adrian, Yuya Sasaki, and Yulong Wang (1st Version 2025)
"Robust Econometrics for Growth-at-Risk"
Abstract: The Growth-at-Risk (GaR) framework has garnered attention in recent econometric literature, yet current approaches implicitly assume a constant Pareto exponent. We introduce novel and robust econometrics to estimate the tails of GaR based on a rigorous theoretical framework and establish validity and effectiveness. Simulations demonstrate consistent outperformance relative to existing alternatives in terms of predictive accuracy. We perform a long-term GaR analysis that provides accurate and insightful predictions, effectively capturing financial anomalies better than current methods.
Yuya Sasaki and Ariell Zimran (1st Version 2025)
"Using Discrepancies to Correct for False Matches in Historical Linked Data"
Paper
Abstract: We propose a method to correct estimates from historical linked data for bias arising from false matches. We estimate the rate of false matching from the extent to which information that should agree across a linked sample disagrees. We can then correct for the bias that false matches induce. Our methods enable researchers to correct estimates of both population moments and regression coefficients for false matches, where the probability of a false match may be constant or related to discrete variables of interest. We illustrate the properties of these estimators via simulation and demonstrate them using linked US census data.
Yuya Sasaki (1st Version 2025)
"GMM and M Estimation under Network Dependence"
Abstract: This paper presents GMM and M estimators and their asymptotic properties for network-dependent data. To this end, I build on Kojevnikov, Marmer, and Song (2021) and develop a novel uniform law of large numbers (ULLN), which is essential to ensure desired asymptotic behaviors of nonlinear estimators (e.g., Newey and McFadden, 1994, Section 2). Using this ULLN, I establish the consistency and asymptotic normality of both GMM and M estimators. For practical convenience, complete estimation and inference procedures are also provided.
Yechan Park and Yuya Sasaki (1st Version 2024)
"Matching ≤ Hybrid ≤ Difference in Differences"
Abstract: Since LaLonde's (1986) seminal paper, there has been ongoing interest in estimating treatment effects using pre- and post-intervention data. Scholars have traditionally used experimental benchmarks to evaluate the accuracy of alternative econometric methods, including Matching, Difference-in-Differences (DID), and their hybrid forms (e.g., Heckman et al., 1998b; Dehejia and Wahba, 2002; Smith and Todd, 2005). We revisit these methodologies in the evaluation of job training and educational programs using four datasets (LaLonde, 1986; Heckman et al., 1998a; Smith and Todd, 2005; Chetty et al., 2014a; Athey et al., 2020), and show that the inequality relationship, Matching ≤ Hybrid ≤ DID, appears as a consistent norm, rather than a mere coincidence. We provide a formal theoretical justification for this puzzling phenomenon under plausible conditions such as negative selection, by generalizing the classical bracketing (Angrist and Pischke, 2009, Section 5). Consequently, when treatments are expected to be non-negative, DID tends to provide optimistic estimates, while Matching offers more conservative ones. Keywords: bias, difference in differences, educational program, job training program, matching.
Yuya Sasaki, Jing Tao, and Yulong Wang (1st Version 2024)
"High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media"
Abstract: Motivated by the empirical power law of the distributions of credits (e.g., the number of "likes") of viral posts in social media, we introduce the high-dimensional tail index regression and methods of estimation and inference for its parameters. We propose a regularized estimator, establish its consistency, and derive its convergence rate. To conduct inference, we propose to debias the regularized estimate, and establish the asymptotic normality of the debiased estimator. Simulation studies support our theory. These methods are applied to text analyses of viral posts in X (formerly Twitter) concerning LGBTQ+.
Harold D. Chiang, Ryutah, Kato, and Yuya Sasaki (1st Version 2024)
"Extremal Quantiles of Intermediate Orders under Two-Way Clustering"
Abstract: This paper investigates extremal quantiles under two-way cluster dependence. We demonstrate that the limiting distribution of the unconditional intermediate order quantiles in the tails converges to a Gaussian distribution. This is remarkable as two-way cluster dependence entails potential non-Gaussianity in general, but extremal quantiles do not suffer from this issue. Building upon this result, we extend our analysis to extremal quantile regressions of intermediate order.
Yechan Park and Yuya Sasaki (1st Version 2024)
"A Bracketing Relationship for Long-Term Policy Evaluation with Combined Experimental and Observational Data"
Abstract: Combining short-term experimental data with observational data enables credible long-term policy evaluation. The literature offers two key but non-nested assumptions, namely the latent unconfoundedness (LU; Athey et al., 2020) and equi-confounding bias (ECB; Ghassami et al., 2022) conditions, to correct observational selection. Committing to the wrong assumption leads to biased estimation. To mitigate such risks, we provide a novel bracketing relationship (cf. Angrist and Pischke, 2009) repurposed for the setting with data combination: the LU-based estimand and the ECB-based estimand serve as the lower and upper bounds, respectively, with the true causal effect lying in between if either assumption holds. For researchers further seeking point estimates, our Lalonde-style exercise suggests the conservatively more robust LU-based lower bounds align closely with the hold-out experimental estimates for educational policy evaluation. We investigate the economic substantives of these findings through the lens of a nonparametric class of selection mechanisms and sensitivity analysis. We uncover as key the sub-martingale property and sufficient-statistics role (Chetty, 2009) of the potential outcomes of student test scores (Chetty et al., 2011, 2014).
Harold D. Chiang, Yuya Sasaki & Yulong Wang (1st Version 2023)
"Genuinely Robust Inference for Clustered Data"
Abstract: Conventional methods of cluster-robust inference are inconsistent in the presence of unignorably large clusters. We formalize this claim by establishing a necessary and sufficient condition for the consistency of the conventional methods. We find that this condition for the consistency is rejected for a majority of empirical research papers. In this light, we propose a novel score subsampling method which is robust even under the condition that fails the conventional method. Simulation studies support these claims. With real data used by an empirical paper, we showcase that the conventional methods conclude significance while our proposed method concludes insignificance.
Yukun Ma, Pedro H.C. Sant'Anna, Yuya Sasaki & Takuya Ura (1st Version 2023)
"Doubly Robust Estimators with Weak Overlap"
Abstract: In this paper, we derive a new class of doubly robust estimators for treatment effect estimands that is also robust against weak covariate overlap. Our proposed estimator relies on trimming observations with extreme propensity scores and uses a bias correction device for trimming bias. Our framework accommodates many research designs, such as unconfoundedness, local treatment effects, and difference-in-differences. Simulation exercises illustrate that our proposed tools indeed have attractive finite sample properties, which are aligned with our theoretical asymptotic results.
Brantly Callaway, Tong Li, Joel Rodrigue, Yuya Sasaki & Yong Tan (1st Version 2023)
"Regulation, Emissions and Productivity: Evidence from China's Eleventh Five-Year Plan"
Paper
Abstract: China’s eleventh Five-Year Plan (11th FYP) outlined some of the most aggressive sulfur dioxide (SO2) reduction targets in Chinese history. This paper quantifies the impact that these targets, along with the associated incentive and enforcement mechanisms, had on firm-level environmental performance. Extending well-known distributional estimators to characterize dynamic firm-level responses, we find significant evidence of large emissionsintensity declines among the upper quantiles of the emissions distribution, modest evidence of increases among the lower quantiles of the emissions-distribution and no change among the middle quantiles. Declines in firm-level emissions-intensity are consistent with the simultaneous adoption of emissions-mitigating technology, while rising emissions-intensity among lower quantiles is consistent with decreasing returns to abatement. In aggregate, we do not find evidence that the 11th FYP significantly reduced aggregate manufacturing SO2 emissions. We do find evidence that the 11th FYP increased aggregate manufacturing productivity by as much as 6 percentage points.
Harold D. Chiang & Yuya Sasaki (1st Version 2023)
"On Using The Two-Way Cluster-Robust Standard Errors"
Abstract: Thousands of papers have reported two-way cluster-robust (TWCR) standard errors. However, the recent econometrics literature points out the potential non-gaussianity of two-way cluster sample means, and thus invalidity of the inference based on the TWCR standard errors. Fortunately, simulation studies nonetheless show that the gaussianity is rather common than exceptional. This paper provides theoretical support for this encouraging observation. Specifically, we derive a novel central limit theorem for two-way clustered triangular arrays that justifies the use of the TWCR under very mild and interpretable conditions. We, therefore, hope that this paper will provide a theoretical justification for the legitimacy of most, if not all, of the thousands of those empirical papers that have used the TWCR standard errors. We provide a guide in practice as to when a researcher can employ the TWCR standard errors.
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