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Angrist, J., Pischke, J.-S. 2008. Mostly harmless econometrics: an empiricist’s companion. Princeton University Press, Princeton, NJ.Graduate level book with a somewhat irreverent (i.e., conversational) writing style, yet heavy on theorems and formulas. Begins with a conceptual overview of experimentation and causal inference, then covers ordinary least-squares regression and continues with a strong focus on instrumental variables. Also explores difference in differences, fixed effects, and discontinuity analyses, which are presented as special cases of regression applied as quasi-experiments. The book does not consider interactive effects, mediation, or multilevel issues that may be of interest to micro researchers.Campbell, D. T., & Stanley, J. C. 2015. Experimental and quasi-experimental designs for research. Ravenio Books.Comprehensive resources on all types of experiments. All begin with a discussion of conditions and parameters of causality and then offer discussion of various types of experiments and the various threats to validity therein.Kennedy, P. 2008. A Guide to Econometrics. Wiley-Blackwell.Textbook focused on the question of good estimates of coefficients and organized around ways to address violations of the assumptions of OLS regression. Each chapter is short and math-free followed by general notes and technical notes to fill in the details. Endogeneity is primarily handled in Chapters 9-11 and selection is discussed Chapter 17. The notes on measurement error in chapter 10 are particularly helpful. There is limited coverage of panel data and methods to estimate treatment effects.Wooldridge, J. M. 2002. Econometric Analysis of Cross Sectional and Panel Data. MIT Press.A comprehensive textbook of econometric methods with an emphasis on formulas, explicit assumptions and derivations. Chapter 4 gives an overview of endogeneity. Chapter 5 covers single equation instrumental variable techniques to address omitted variable and measurement error problems. Chapter 8 covers system of equation instrumental variable techniques to address simultaneity. Chapters 10 and 11 cover panel data with an emphasis on omitted variables and unobserved effects. Chapter 19 covers sample selection and Chapter 21 covers methods to estimate treatment effects.文章
Angrist, J. D., & Pischke, J.-S. 2010. The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of Economic Perspectives, 24(2): 3-30. Reviews the extent to which greater emphasis on experimental and quasi-experimental research designs in microeconomics studies has dramatically increased the credibility of empirical work. Point out how early studies suffer from a variety of endogeneity biases including omitted variable and reverse causality but that current econometrics is more "design based" (i.e., focus on design is similar to that of a true randomized experiment). Define quasi-experimental methods as: instrumental variables, regression discontinuity methods, and differences-indifferences-style policy analyses. Review a number of influential studies and designs.Angrist, J. D., Imbens, G. W., & Rubin, D. B. 1996. Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association, 91: 444-455.Widely cited and technical econometrics paper outlining the method for establishing a local average treatment effect, LATE); an instrumental variables estimate of the effect of treatment on the population of compliers. The term "compliers" comes from an analogy with randomized trials where some experimental subjects comply with the randomly assigned treatment protocol (e.g., take their medicine) but some do not (i.e., defiers or non-compliers). This method requires a number of critical assumptions that have been the subject of debate.Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. 2010. On making causal claims: A review and recommendations. The Leadership Quarterly, 21: 1086-1120. Aimed at micro researchers; the main argument is that causal claims can be made with non-experimental data under certain conditions. Gives a detailed introductory overview of various threats to validity, i.e., endogeneity) and statistical and design approaches that address these concerns. Evaluates 120 management articles; find that a majority, 66%-90%) did not adequately address threats to validity.Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. 2014. Causality and Endogeneity: problems and solutions. The Oxford Handbook of Leadership and Organizations, 93.A micro-oriented review of endogeneity couched in leadership examples. Presents a straightforward explanation of what endogeneity is, why it biases estimates, various threats to validity, i.e., omitting a regressor, measurement error, common method variance, omitting fixed effects, omitting selection, and simultaneity). Then explores various methods to test causal claims in non-experimental contexts with a primary focus on 2SLS.Antonakis, J., Bastardoz, N., & Rönkkö, M. 2019. On ignoring the random effects assumption in multilevel models: Review, critique, and recommendations. Organizational Research Methods, 1094428119877457.Targeted extension of Antonakis et al., 2010) to multilevel modelling techniques with both micro and macro applications. Show that endogeneity threats are introduced into multilevel models when researchers interested in estimating the “within” effect (i.e., the effect of a Level 1 regressor on a Level 1 outcome) do not correctly model the unobserved variation due to the hierarchical structure of the data. Endogeneity and the accompanying biased results arise out of violations of the random effects assumption (see Wooldridge, 2013), which is a testable assumption that is not generally adequately addressed in management articles, demonstrated through a review of a random sample of 204 micro and macro articles). Provide detailed explanations and techniques along with extensive appendices including links to an explanatory video, mathematical derivations, a decision chart for different analyses, and Stata and R code for simulations with different degrees of endogeneity.Arellano, M., & Bond, S. 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58: 277-297.Derivation of a consistent generalized method of moments (GMM) estimator for dynamic panel data models. Uses lagged values of the dependent variable as instruments after a first-differences transformation. The core idea is that the model is specified as a system of equations, one per time period, where the instruments applicable to each equation differ, for instance, in later time periods (additional lagged values of the instruments are available). This the basis of the xtabond and xtdpd command in Stata and the pgmm procedure in R.Bascle, G. 2008. Controlling for endogeneity with instrumental variables in strategic management research. Strategic Organization, 6: 285-327.Overview of endogeneity aimed at macro scholars and building off of Hamilton and Nickerson (2003) and Shaver (1998). Defines three situations that lead to endogeneity, i.e., errors-in-variables, omitted variables, simultaneity) and proposes that selecting between the Heckman two-step procedure and instrumental variables estimations should depend on type of endogeneity present and the emergence of specification issues or violations of specific assumptions, which are reviewed in detail. Provides STATA commands for tests and methods reviewed in the article.Bliese, P. D., Schepker, D. J., Essman, S. M., & Ployhart, R. E. 2019. Bridging Methodological Divides Between Macro-and Microresearch: Endogeneity and Methods for Panel Data. Journal of Management, 0149206319868016.Overview of panel data methods meant to bridge the gap in techniques and terminology between micro and macro researchers. Endogeneity is not a primary topic of the paper, but is discussed as it applies to panel data through two forms of unobserved heterogeneity: 1) across the units of observation and 2) over time. The conclusion is macro and micro researchers tend to use different analytic tools which very in effectiveness for addressing these forms of endogeneity. Provides an in-depth overview of various analytic tools as they apply to modeling frameworks concerning different types of research questions. Review 142 articles that used panel data in leading management journals in 2017 and divide analysis across micro and macro applications.Blundell, R., & Bond, S. 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87: 115-143.Based on a potential weakness in the Arellano and Bond (1991) approach, that the lagged levels are often rather poor instruments for first differenced variables, this paper builds off of Arellano & Bover, 1995 to present a modification of the estimator that includes lagged levels as well as lagged differences. This addition requires additional restrictions on the initial conditions of the process generating the outcome. This is the basis of the xtdpdsys command in Stata.Blundell R & Dias MC. 2009. Alternative approaches to evaluation in empirical microeconomics. Journal of Human Resources, 44:565–640A descriptive (i.e., non-technical) review of some of the most popular methods used in econometric studies: social experiments, natural experiments, matching methods, instrumental variable methods, discontinuity design methods, and control function methods. Focus on selection models and various parameters of interest including the average treatment effect (ATE) the average effect on individual assigned to treatment (ATT) and the average effect on nonparticipants (ATNT). An overview of technical applications and in-depth reviews of exemplar applications are provided.Certo, S. T., Withers, M. C., & Semadeni, M. 2017. A tale of two effects: Using longitudinal data to compare within‐and between‐firm effects. Strategic Management Journal, 38: 1536-1556.Focuses on the distinction between sample selection and other causes of endogeneity. Reviews 63 articles (SMJ 2005-2014) and notes inconsistencies in how scholars implement and report results, specifically in that the stated endogeneity concern does not align with the type of endogeneity (i.e., sample-induced endogeneity) addressed with Heckman models. Provide detailed descriptions of the sources of endogeneity in such cases and guidelines for when and how to use Heckman models.Chatterji, A. K., Findley, M., Jensen, N. M., Meier, S., & Nielson, D. 2016. Field experiments in strategy research. Strategic Management Journal, 37: 116-132.Propose wider use of experiments in strategy research to address concerns for causality. Discuss advantages and challenges associated with the research design. Propose two strategy-specific types of experiments: strategy field experiments which align with the general conception of a field experiment in that individuals are randomly assigned to groups which receive a particular treatment X and assess changes in an outcome Y and process field experiments in which a researcher manipulates a third variable M to assess the treatment-outcome relationship, XàY. Report results from two field experiments to answer substantive questions.Clougherty, J. A., Duso, T., & Muck, J. 2016. Correcting for self-selection based endogeneity in management research: Review, recommendations and simulations. Organizational Research Methods, 19: 286-347.Focus on self-selection endogeneity in both micro and macro domains. Explain selection bias as a special case of omitted-variable bias as the selection process can be represented by an excluded variable; failure to include that variable is captured in the error term which correlates with the endogenous choice construct. Present selection-based endogeneity as occurring through two forms: sample-selection and self-selection and give an in-depth review of the self-selection endogeneity problem. Review papers in SMJ, 2002-2014, and report that while the number of papers addressing selection effects has risen, the estimation strategies and reporting standards are so poor as to make findings uninformative. Use simulations to evaluate different analytical strategies and recommend that researchers take care in choosing analytical methods as the exact nature of the endogenous self-selection - endogenous treatment (which only involves an intercept effect) and endogenous switching (which involves slope coefficients for the other explanatory variables in addition to the intercept effect) affects the outcome of ultimate interest.Conley, T.G., Hansen, C.B. and Rossi, P.E. 2012. Plausibly exogenous. Review of Economics and Statistics, 94: 260-272.Focus on instrumental variable techniques and the exclusion restriction in particular (i.e., the IV correlates with the endogenous predictor and is only related to the outcome of interest through the endogenous predictor). Define plausibly, or approximately exogenous instruments as those where the relationship between the instrument and error term is near zero, instead of exactly zero. Motivated by the fact that 2SLS is sensitive to violation of the exclusion condition, especially when instruments are weak; so providing evidence that strong instruments can yield informative results even when deviating from the exact exclusion restriction is useful. Present four derivations and methods for inference and provide empirical examples to demonstrate.Dehejia, R. and Wahba, S. 2002. ‘Propensity score matching methods for non-experimental causal studies’. Review of Economics and Statistics, 84: 151–61.Foundational article on propensity score matching, see also Dehejia and Wahba (1999). Discusses the use of propensity score-matching methods to correct for sample selection endogeneity due to observable differences between the “treatment” and comparison groups in nonexperimental settings. Propose that this approach is preferable in situations when, 1) there are few units in the nonexperimental comparison group that are comparable to the treatment units, and, 2) there is a high number of pretreatment characteristics available on which to match treatment and comparison groups.Elwert, F., & Winship, C. 2014. Endogenous selection bias: The problem of conditioning on a collider variable. Annual Review of Sociology, 40: 31-53.Argue that selection bias can be difficult to identify. Use causal graphs to illustrate all forms of selection bias that can occur with non-random samples. Gives multiple examples from sociology.Hamilton, B. H., & Nickerson, J. A. 2003. Correcting for endogeneity in strategic management research. Strategic Organization, 1: 51-78.Show that despite the centrality of endogenous choices in our literature, few papers published in Strategic Management Journal (1990-2001) correct for endogeneity. Shows that many strategy research questions are based on estimating the effect of a discrete, endogenous choice on future performance. Explains how the Heckman treatment effect model addresses this kind of question and then shows that panel data may allow for estimation with fewer assumptions.Heckman, J. 1979. Sample selection bias as a specification error. Econometrica, 47: 153–61.Foundational article that characterizes nonrandomly selected sample as an omitted variable problem. Derives a two-stage estimator and shows that it is consistent. The first stage is a model of inclusion in the sample. The invers Mill’s ratio calculated from the first stage is used as a control in the second stage.Heckman, J. 1997. Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources, 441-462.Argues that instrumental variable methods of estimating treatment effects are built on an assumption that all individuals would receive the same benefit from the treatment. If unobserved variables predict how much an individual will benefit, not just whether they will participate, then instrumental variable methods do not yield “economically interesting” parameters.Imbens, G.W. 2004. Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics, 86: 4-29. Reviews a number of methods used to estimate treatment effects including matching, propensity score and weighting methods. None of these methods address endogeneity (since exogeneity is assumed) and yet significant estimation issues remain.Imbens, G. W., & Wooldridge, J. M. 2009. Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47: 5-86.A comprehensive review of the econometric challenges of evaluating programs or policy changes. Many of methods discussed are applicable to organizational research questions.Krause, M. S., & Howard, K. I. (2003). What random assignment does and does not do. Journal of Clinical Psychology, 59(7), 751-766.Discusses the advantages and limitations of using study designs which employ random assignment, particularly as it relates to avoiding confounded estimates at the heart of omitted variables endogeneity.Li, M. 2013a Social network and social capital in leadership and management research: A review of causal methods. The Leadership Quarterly, 24: 638-665A survey of methods used in research on social networks and social capital. Demonstrates that common procedures for network sampling and analysis introduce multiple endogeneity concerns. A review of studies shows that scholars are not using rigorous methods to address for endogeneity.Li, M. 2013b. Using the propensity score method to estimate causal effects: A review and practical guide. Organizational Research Methods, 16: 188-226.Introduces and explains the propensity score matching method. Notes that this method cannot adjust for bias caused by unobserved drivers of selection because it is based on the strongly ignorable assumption. However, the method can be used to create treated and control groups that have similar observable covariate distributions.McNeish, D., & Kelley, K. 2019. Fixed effects models versus mixed effects models for clustered data: Reviewing the approaches, disentangling the differences, and making recommendations. Psychological Methods, 24: 20.Notes that psychology has traditionally ignored endogeneity concerns but argues that this must change as research is becoming more observational and less experimental. Discusses the differences between mixed effects and mixed effects models in addressing endogeneity and omitted confounders.Reeb, D., Sakakibara, M., & Mahmood, I. P. 2012. From the editors: Endogeneity in international business research.Provide an explanation of the endogeneity problem in international business research and give guidelines for achieving the goal of research that approximates a randomized-controlled experiment.Roberts, M. R., & Whited, T. M. 2013. Endogeneity in empirical corporate finance1. In Handbook of the Economics of Finance, Vol. 2, pp. 493-572. Elsevier.Extensive overview of the sources of endogeneity and methods that either rely on an exogenous instrument or event for identification (instrumental variable, difference-in-differences, regression discontinuity) or that rely on other assumptions (matching, panel methods, higher order moment estimators).Rocha, V., Van Praag, M., Folta, T. B., & Carneiro, A. 2019. Endogeneity in strategy-performance analysis: An application to initial human capital strategy and new venture performance. Organizational Research Methods, 22: 740-764.Argue that many organizational decisions cannot be adequately modeled as single choices. Instead, many managers must make simultaneous choices and interdependent choices. Research that focuses on just a single decision, even if it accounts for the endogeneity of the decision, may be biased if it ignores related decisions.Rosenbaum, P. R., & Rubin, D. B. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika, 70: 41-55.The first articulation of the propensity score matching methods as a way to reduce biased estimates of a treatment effect. The method is built on the assumption of strongly ignorable treatment assignment.Semadeni, M., Withers, M. C., & Trevis Certo, S. 2014. The perils of endogeneity and instrumental variables in strategy research: Understanding through simulations. Strategic Management Journal, 35: 1070-1079.Reports the analysis of simulated data with an endogenous regressor. When endogeneity is ignored, OLS coefficient estimates are biased. The use of instrumental variables can eliminate bias. However, instrumental variable estimation reduces statistical power and the use of weak instruments can result in estimates that are inferior to OLS regression.Shaver, J. M. 1998. Accounting for endogeneity when assessing strategy performance: Does entry mode choice affect FDI survival. Management Science, 44: 571-585.One of the first papers to recognize the endogeneity of strategic choices as an important bias. Argues that the choice between acquisition and greenfield is endogenous. An observed survival advantage of greenfield entry disappears when the self-selection of the choice is accounted for.Shaver, J. M. 2005. Testing for mediating variables in management research: Concerns, implications, and alternative strategies. Journal of Management, 31: 330-353.Argues that tests of mediation in field data are often biased by endogeneity. Demonstrates an alternative estimation procedure based on instrumental variables using SEM.Shaver, J. M. 2019a. Causal Identification Through a Cumulative Body of Research in the Study of Strategy and Organizations. Journal of Management, 0149206319846272.Highlights why causal identification is important in organizational research but also why the nature of the available data make identification difficult. Suggests that, in addition to improving research practices, progress will require building evidence of causality through multiple empirical studies.Shaver, J. M. 2019b. Interpreting Interactions in Linear Fixed-Effect Regression Models: When Fixed-Effect Estimates Are No Longer Within-Effects. Strategy Science, 4: 25-40.Fixed effect models are one way to account for unobserved heterogeneity. However, this paper demonstrates that the desirable properties of fixed effects models are eliminated when interaction terms are introduced.Stock, J. & Yogo, M. 2002. Testing for weak instruments in linear IV regression. In Andrews, D. W. K. and Stock, J. (eds.), Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg. Cambridge: Cambridge University Press, 80–108.Weak instruments are variables that are not strong predictors of the endogenous variable. It is well established that weak instrumental variables produced biased coefficient estimates. This paper proposes a quantitative test for weak instruments in models with multiple endogenous regressors.Winship, C., & Morgan, S.L. 1999. The estimation of causal effects from observational data. Annual Review of Sociology, 25: 659–706Start with a review of methods that can be used to establish causal effects from cross-sectional data and then presents estimators that make use of longitudinal data. Focus on interrupted time series design as a potentially useful method for sociology research.Wolfolds, S. E., & Siegel, J. 2019. Misaccounting for endogeneity: The peril of relying on the Heckman two‐step method without a valid instrument. Strategic Management Journal, 40: 432-462.Focus on selection bias as a source of endogeneity and evaluation of the valid exclusion condition in the selection equation for Heckman models, which was reported in only 54 of 165 papers in top journals. Use simulations to show that when using a valid instrument or selection on observables, the Heckman method generally performs better than OLS, regardless of the distribution of the error terms. Yet when the exclusion condition is not met (i.e., one does not have a valid instrument that affects selection but not the outcome), the distribution of errors affects whether the Heckman or OLS method is more accurate. Except in the case when errors are distributed according to a bivariate normal or bivariate lognormal distribution, the Heckman method performs significantly worse than OLS.Source: Hill AD, Johnson SG, Greco LM, O’Boyle EH, Walter SL. Endogeneity: A Review and Agenda for the Methodology-Practice Divide Affecting Micro and Macro Research. Journal of Management. 2021;47(1):105-143. doi:10.1177/0149206320960533
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关于面板数据模型，1.面板数据方法免费课程, 文章, 数据和代码全在这里, 优秀学人好好收藏学习！2.面板数据中标准误的估计方法, 你确定用对了吗? 我们来比较一番！3.疫情期计量课程免费开放！面板数据, 因果推断, 时间序列分析与Stata应用，4.用Stata做面板数据分析, 操作代码应有尽有，5.面板数据为什么好？读了这篇你才会明白，6.GMM和工具变量在面板数据中的运用，7.面板数据聚类, 因子分析和主成分分析咋做? 8.伪面板回归是什么, 诺贝尔经济学家推荐使用，9.面板数据中介效应的计算程序, 打开面板这扇门，10.面板数据模型操作指南, 不得不看的16篇文章，11.2SLS第一阶段输出, 截面或面板数据及统计值都行，12.面板数据模型操作指南, 不得不看的16篇文章，13.面板数据中heckman方法和程序, 动态, 0-1面板和内生性选择都行，14.面板数据是怎样处理内生性的,一篇让人豁然明朗的文章，15.面板数据计量方法全局脉络和程序使用指南篇，16.面板数据密度图和时间趋势图韩城攻略和常见操作，17.面板数据里处理多重高维固定效应的神器, 还可用工具变量处理内生性，18.reg3, 多元回归, 面板数据, 方差分析, 异方差和自相关检验和修正的Stata程序Handbook，19.面板数据的DID估计，透彻解读，20.非线性面板模型中内生性解决方案以及Stata命令，21.面板数据、工具变量选择和HAUSMAN检验的若干问题，22.把动态面板命令讲清楚了，对Stata的ado详尽解释，23.动态面板回归和软件操作，单位根和协整检验（Dynamic Panel Data）
关于DID相关文章0.双重差分DID方法免费课程, 文章, 数据和代码全在这里, 优秀学人必须收藏学习！1.DID运用经典文献，强制性许可:来自对敌贸易法的证据，2.连续DID经典文献, 土豆成就了旧世界的文明，3.截面数据DID讲述, 截面做双重差分政策评估的范式，4.RDD经典文献, RDD模型有效性稳健性检验，5.事件研究法用于DID的经典文献"环境规制"论文数据和程序，6.广义DID方法运用得非常经典的JHE文献，7.DID的经典文献"强制许可"论文数据和do程序，8.传销活动对经济发展影响, AER上截面数据分析经典文，9.多期DID的经典文献big bad banks数据和do文件，10.因果推断IV方法经典文献，究竟是制度还是人力资本促进了经济的发展？11.AER上因果关系确立, 敏感性检验, 异质性分析和跨数据使用经典文章，12.第二篇因果推断经典，工作中断对工人随后生产效率的影响？，13.密度经济学:来自柏林墙的自然实验, 最佳Econometrica论文，14.AER上以DID, DDD为识别策略的劳动和健康经济学，15.一个使用截面数据的政策评估方法, 也可以发AER，16.多期DID模型的经典文献，big bad banks讲解"，17.多期DID的经典文献big bad banks数据和do文件，18.非线性DID, 双重变换模型CIC, 分位数DID，19.模糊(Fuzzy)DID是什么？如何用数据实现呢？20.多期DID的big bad banks中文翻译版本及各细节讲解，21.DID中行业/区域与时间趋势的交互项, 共同趋势检验, 动态政策效应检验等，22.截面数据DID操作程序指南, 一步一步教你做，23.DID的研究动态和政策评估中应用的文献综述，24.连续DID经典文献, 土豆成就了旧世界的文明，25.DID双重差分方法, 一些容易出错的地方，26.连续DID, DDD和比例DID, 不可观测选择偏差，27.加权DID, IPW-DID实证程序百科全书式的宝典，28.DID和DDD, 一个简明介绍, 双重和三重差分模型，29.DID过程中总结的地图展示技巧，30.DID的平行趋势假定检验程序和coefplot的其他用法，31.截面DID, 各种固定效应, 安慰剂检验, 置换检验, 其他外部冲击的处理，32.实践中双重差分法DID暗含的假设，33.过去三十年, RCT, DID, RDD, LE, ML, DSGE等方法的“高光时刻”路线图，34.计量院士首次用DID方法分析, 中国封城对新冠病毒扩散的影响！，35.截面DID, 各种固定效应, 安慰剂检验, 置换检验, 其他外部冲击的处理，36.诺奖夫妇的中国学生, “DID小公主”的成名之作, 茶叶价格与中国失踪女性之谜！，37.前沿: 反向DID, 反向双重差分法DDR全解析, 辅以实证示例！38.英诺丁汉大学校长为你讲解逐年PSM匹配-DID方法的操作, 并配上自己写的一篇范文！39.逐年PSM匹配后再DID识别因果的实证范文, 这就是逐年PSM-DID的操作范式！40.用事件研究法进行因果识别如何做? 有什么好处? 与DID结合起来潜力无穷！41.Abadie半参数双重差分DID估计量, 使你的平行趋势假设更加可信！42.弹性DID, DID的终极大法, 关于DID各方法总结太赞了！43.二重差分法分析（DID），44.比DID更加灵活的DDID政策效应评估方，45.DID思路和操作，一篇相关实证文献，46.二重差分法深度分析（DID），三重差分兼论，47.面板数据的DID估计，透彻解读，48.PSM-DID, DID, RDD, Stata程序百科全书式的宝典，49.关于DID的所有解读, 资料, 程序, 数据, 文献和各种变形都在这里，50.分位数DID, PSMDID, 政策前协变量平衡性检验操作步骤和案例，51.PSM-DID, DID实证完整程序百科全书式的宝典，52.逐年匹配的PSM-DID操作策略, 多时点panel政策评估利器，53.广义DID, DID最大法宝, 无所不能的政策评估工具，54.渐进DID专治各种渐进性政策的良药, 可试一试疗效，55.双重差分DID的种类细分, 不得不看的20篇文章，56.找不到IV, RD和DID该怎么办? 这有一种备选方法，57.在教育领域使用IV, RDD, DID, PSM多吗? 使用具体References，58.DID和IV操纵空间大吗? 一切皆为P-hacking，59.第一篇中文DID实证论文长啥样? 60.世界上第一篇DID实证论文长啥样?，61.关于双重差分法DID的32篇精选Articles专辑！62.空间双重差分法(spatial DID)最新实证papers合辑！63.空间DID双重差分方法的文献, spatial DID，64.多期三重差分法和双重差分法的操作指南，65.多期双重差分法,政策实施时间不同的处理方法，66.三重差分法运行和示例，67.如何设计双重差分法DID: 各种政策研究的最佳指南!关于匹配方法相关文章1. PSM倾向匹配Stata操作详细步骤和代码，干货十足，2.处理效应模型选择标准，NNM和PSM，赠书活动，3.PSM和马氏匹配已淘汰, '遗传匹配'成因果推断匹配之王，4.PSM, RDD, Heckman, Panel模型的操作程序, selective文章精华系列，5.广义PSM,连续政策变量因果识别的不二利器，6.PSM-DID, DID, RDD, Stata程序百科全书式的宝典，7.在教育领域使用IV, RDD, DID, PSM多吗? 使用具体References，8.分位数DID, PSMDID, 政策前协变量平衡性检验操作步骤和案例，9.逐年匹配的PSM-DID操作策略, 多时点panel政策评估利器，10.执行PSM的标准操作步骤, 不要再被误导了，11.PSM匹配后如何保留配对样本? 1:1, 1:4或更多情况呢？12.逐年PSM匹配后再DID识别因果的实证范文, 这就是逐年PSM-DID的操作范式！13.英诺丁汉大学校长为你讲解逐年PSM匹配-DID方法的操作, 并配上自己写的一篇范文！14.内生性问题和倾向得分匹配, 献给准自然试验的厚礼，15.粗化精确匹配CEM文献推荐, 程序步骤可复制，16.DID, 合成控制, 匹配, RDD四种方法比较, 适用范围和特征，17.匹配方法(matching)操作指南, 值得收藏的16篇文章，18.中国工业企业数据库匹配160大步骤的完整程序和相应数据，19.Match匹配估计做敏感性检验的最新方法, 让不可观测变量基础上的选择无处遁形，20.无需检查协变量平衡性的CEM匹配, 到底有多神气和与众不同，21.因果推断中的匹配方法:最全回顾和前景展望，22.内生性问题和倾向得分匹配, 献给准自然试验的厚礼，23.倾向值匹配与因果推论,史上最全面精妙的锦囊，24.匹配还是不匹配？这真是个值得考虑的问题，25.匹配比OLS究竟好在哪里？这是一个问题，26.倾向匹配分析深度（Propsensity matching analysis），27.倾向得分匹配PSM, 你真的用对了吗? 对主流期刊86篇文章分析与总结！28.中文刊上用倾向得分匹配PSM和内生转换模型ESM的实证文章有哪些？不看至少需要收藏一下，29.倾向得分匹配PSM, 你真的用对了吗? 对主流期刊86篇文章分析与总结，30.内生转换模型vs内生处理模型vs样本选择模型vs工具变量2SLS，31.ESP内生转化概率模型是什么, 如何做, 如何解释, 为什么需要它? 32.Heckman模型out了,内生转换模型掌控大局，33.因果效应中的双重稳健估计值, 让你的估计精准少误，34.加权DID, IPW-DID实证程序百科全书式的宝典
关于Stata，1.Stata16新增功能有哪些? 满满干货拿走不谢，2.Stata资料全分享，快点收藏学习，3.Stata统计功能、数据作图、学习资源，4.Stata学习的书籍和材料大放送, 以火力全开的势头，5.史上最全Stata绘图技巧, 女生的最爱，6.把Stata结果输出到word, excel的干货方案，7.编程语言中的函数什么鬼？Stata所有函数在此集结，8.世界范围内使用最多的500个Stata程序，9.6张图掌握Stata软件的方方面面, 还有谁, 还有谁? 10.LR检验、Wald检验、LM检验什么鬼？怎么在Stata实现，11.Stata15版新功能，你竟然没有想到，一睹为快，12."高级计量经济学及Stata应用"和"Stata十八讲"配套数据，13.数据管理的Stata程序功夫秘籍，14.非线性面板模型中内生性解决方案以及Stata命令，15.把动态面板命令讲清楚了，对Stata的ado详尽解，16.半参数估计思想和Stata操作示例，17.Stata最有用的points都在这里,无可替代的材料，18.PSM倾向匹配Stata操作详细步骤和代码，干货十足，19.随机前沿分析和包络数据分析 SFA,DEA 及Stata操作，20.福利大放送, Stata编程技巧和使用Tips大集成，21.使用Stata进行随机前沿分析的经典操作指南，22.Stata, 不可能后悔的10篇文章, 编程code和注解，23.用Stata学习Econometrics的小tips, 第二发礼炮，24.用Stata学习Econometrics的小tips, 第一发礼炮，25.广义合成控制法gsynth, Stata运行程序release，26.多重中介效应的估计与检验, Stata MP15可下载，27.输出变量的描述性统计的方案，28.2SLS第一阶段输出, 截面或面板数据及统计值都行，29.盈余管理指标的构建及其Stata实现程序, 对应解读和经典文献，30.Python, Stata, R软件史上最全快捷键合辑！，31.用Stata做面板数据分析, 操作代码应有尽有，32.用Stata做面板数据分析, 操作代码应有尽有，33.没有这5个Stata命令, 我真的会活不下去！，34.第一(二)卷.Stata最新且有趣的程序系列汇编，35.第三卷.Stata最新且急需的程序系列汇编，36.第四卷.Stata最新且急需的程序系列汇编，37.干货: UN和WTO推荐的最全且权威的实证研究方法及在Stata实现！必收藏！，38.再中心化影响函数RIF回归和分解的Stata操作程序，39.R和Stata软件meta分析操作详细攻略, 对研究再开展研究的利器！，40.不能安装Stata命令咋弄？这个方法一直都比较靠谱！，41.使用Stata做结构方程模型GSEM的操作指南，42.疫情期计量课程免费开放！面板数据, 因果推断, 时间序列分析与Stata应用，43.一些Stata常见操作代码和注释, 能够让年轻学人更快掌握相关命令！44.Stata语言中的常用函数及其用法解释, 在附上42篇Stata相关学习资料，45.Stata经典操作笔记和学习资源合辑! 都是些博士生导师比较推荐的!
断点回归设计RDD的文章1.断点回归设计RDD分类与操作案例，2.RDD断点回归, Stata程序百科全书式的宝典，3.断点回归设计的前沿研究现状, RDD，4.断点回归设计什么鬼？且听哈佛客解析，5.断点回归和读者的提问解答，6.断点回归设计RDD全面讲解, 教育领域用者众多，7.没有工具变量、断点和随机冲击，也可以推断归因，8.找不到IV, RD和DID该怎么办? 这有一种备选方法，9.2卷RDD断点回归使用手册, 含Stata和R软件操作流程，10.DID, 合成控制, 匹配, RDD四种方法比较, 适用范围和特征，11.安神+克拉克奖得主的RDD论文, 断点回归设计，12.伊斯兰政府到底对妇女友不友好？RDD经典文献，13.PSM,RDD,Heckman,Panel模型的操作程序，14.RDD经典文献, RDD模型有效性稳健性检验，15.2019年发表在JDE上的有趣文章, 计量方法最新趋势，16.关于(模糊)断点回归设计的100篇精选Articles专辑！17.断点回归设计RDD精辟解释, 保证你一辈子都忘不了，18.“RDD女王”获2020年小诺奖！她的RD数据, 程序, GIS和博士论文可下载！关于她学术研究过程的最全采访！19.中国博导要求掌握的RDD方法实证运用范文(配程序code), 不然就不要用RDD做实证研究！20.最近70篇关于中国环境生态的经济学papers合辑！21.事件研究法用于DID的经典文献"环境规制"论文数据和程序，22.环境, 能源和资源经济学手册推荐, 经典著作需要反复咀嚼，23.中文刊上用断点回归RDD和合成控制法SCM的实证文章有哪些？不看至少需要收藏一下！24.上双一流大学能多赚多少钱? 学习断点回归RDD, 机制分析的经典文章！25.JPE上利用地理断点RDD和IV研究中国环境议题的do文件release！
关于工具变量，参看1.内生性问题操作指南, 广为流传的22篇文章，2.看完顶级期刊文章后, 整理了内生性处理小册子，3.如何寻找工具变量？得工具者得实证计量，4.内生性处理的秘密武器－工具变量估，5.工具变量在社会科学因果推断中的应用，6.为你的"工具变量"合理性进行辩护, 此文献可以作为范例，7.没有工具变量、断点和随机冲击，也可以推断归因，8.工具变量与因果推断, 明尼苏达Bellemare关于IV的分析，9.工具变量IV与内生性处理的精细解读，10.我的"工具变量"走丢了，寻找工具变量思路手册，11.面板数据里处理多重高维固定效应的神器, 还可用工具变量处理内生性，12.豪斯曼, 拉姆齐检验，过度拟合，弱工具和过度识别，模型选择和重抽样问题，13.工具变量先锋 Sargan，供参考，14.AEA期刊的IV靠不靠谱？15.计量大焖锅: iv, clorenz, rank, scalar, bys, xtile, newey, nlcom，16.GMM是IV、2SLS、GLS、ML的统领，待我慢慢道来，17.IV和GMM相关估计步骤，内生性、异方差性等检验方法，18.因果推断IV方法经典文献，究竟是制度还是人力资本促进了经济的发展？19.内生变量的交互项如何寻工具变量, 交互项共线咋办，20.面板数据、工具变量选择和HAUSMAN检验的若干问题，21.IV和Matching老矣, “弹性联合似然法”成新趋势，22.IV回归系数比OLS大很多咋回事, 怎么办呢? ，23.不用IV, 基于异方差识别方法解决内生性, 赐一篇文献，24.找不到IV, RD和DID该怎么办? 这有一种备选方法，25.内生转换模型vs内生处理模型vs样本选择模型vs工具变量2SLS，26.内生性, 工具变量与 GMM估计, 程序code附，27.GMM和工具变量在面板数据中的运用，28.关于工具变量的材料包, 标题,模型,内生变量,工具变量，29.必须使用所有外生变量作为工具变量吗？30.工具变量精辟解释, 保证你一辈子都忘不了，31.毛咕噜论文中一些有趣的工具变量！33.前沿: 删失数据分位数工具变量(CQIV)估计, 做删失数据异质性效应分析，34.不需要找工具变量, 新方式构建工具变量, 导师再也不用担心内生性问题了！35.关于顶级外刊工具变量的使用最全策略, 不收藏反复读就不要谈IV估计！36.如何通过因果图选择合适的工具变量？一份关于IV的简短百科全书，37.前沿: nature刊掀起DAG热, 不掌握就遭淘汰无疑！因果关系研究的图形工具! 38.最清晰的内生性问题详解及软件操作方案！实证研究必备工具！39.中国女学者与其日本同行在JPE上发文了！利用独特数据, 地理断点RDD和IV研究中国环境议题！40.双胞胎样本解决遗漏变量和测量误差, LIV解决选择偏差，41.内生性处理的秘密武器－工具变量估计，42.工具变量IV必读文章20篇, 因果识别就靠他了，43.看完顶级期刊文章后, 整理了内生性处理小册子，44.“内生性” 到底是什么鬼? New Yorker告诉你，45.Heckman两步法的内生性问题(IV-Heckman)，46.最全估计方法，解决遗漏变量偏差，内生性，混淆变量和相关问题，47.非线性面板模型中内生性解决方案，48.内生性处理方法与进展，49.内生性问题和倾向得分匹配，50.你的内生性解决方式out, ERM独领风骚，51.面板数据是怎样处理内生性的，52.计量分析中的内生性问题综述，53.一份改变实证研究的内生性处理思维导图，54.Top期刊里不同来源内生性处理方法，55.面板数据中heckman方法和程序(xtheckman)，56.控制函数法CF, 处理内生性的广义方法，57.二值选择模型内生性检验方法，58.2SRI还是2SPS, 内生性问题的二阶段CF法实现，59.非线性模型及离散内生变量处理利器, 应用计量经济学中的控制函数法！60.最全利用工具变量控制内生性的步骤和代码—在经管研究中的应用，61.如何选择合适的工具变量, 基于既有文献的总结和解释！62.中介效应最新进展: 中介效应中的工具变量法使用方法及其代码！63.弱工具变量的稳健性检验, 附上code和相关说明！64.工具变量对因果效应的识别和外推, 大牛的顶级评述！
关于一些计量方法的合辑，参看①“实证研究中用到的200篇文章, 社科学者常备toolkit”、②实证文章写作常用到的50篇名家经验帖, 学者必读系列、③过去10年AER上关于中国主题的Articles专辑、④AEA公布2017-19年度最受关注的十大研究话题, 给你的选题方向，⑤2020年中文Top期刊重点选题方向, 写论文就写这些。后面，咱们又引荐了①使用CFPS, CHFS, CHNS数据实证研究的精选文章专辑！，②这40个微观数据库够你博士毕业了, 反正凭着这些库成了教授，③Python, Stata, R软件史上最全快捷键合辑！，④关于(模糊)断点回归设计的100篇精选Articles专辑！，⑤关于双重差分法DID的32篇精选Articles专辑！，⑥关于合成控制法SCM的33篇精选Articles专辑！⑦最近80篇关于中国国际贸易领域papers合辑！，⑧最近70篇关于中国环境生态的经济学papers合辑！⑨使用CEPS, CHARLS, CGSS, CLHLS数据库实证研究的精选文章专辑！⑩最近50篇使用系统GMM开展实证研究的papers合辑！
下面这些短链接文章属于合集，可以收藏起来阅读，不然以后都找不到了。