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Quantile Difference in Differences with Time-Varying Qualification in Panel Data 面板数据中具有时变定性的差异中的分位数差异
Journal of Econometric Methods Pub Date : 2022-08-19 DOI: 10.1515/jem-2021-0032
Karim Nchare, Ryo Makioka
{"title":"Quantile Difference in Differences with Time-Varying Qualification in Panel Data","authors":"Karim Nchare, Ryo Makioka","doi":"10.1515/jem-2021-0032","DOIUrl":"https://doi.org/10.1515/jem-2021-0032","url":null,"abstract":"Abstract This paper investigates the identification and estimation of the quantile treatment effect in a difference in differences (DID) setting when treatment is provided only to qualified individuals at a certain point in time and the qualification is time-varying. The time-varying qualification may affect an outcome variable even when the actual effect of treatment is zero. We show how to account for this “movers effect” bias and propose the quantile treatment effect on “in-stayers” that are qualified both before and after the treatment. The estimate is identified under three main assumptions: (i) panel data availability, (ii) a distributional common trend assumption conditional on covariates, and (iii) a copula stability assumption. We then apply our method to estimate the effects of an increase in the benefits of the Supplemental Nutrition Assistance Program (SNAP) on recipients’ food expenditure shares. The results show significant heterogeneity and highlight the importance of accounting for time-varying qualification.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"12 1","pages":"105 - 116"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45105469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Random Forest-based Approach to Combining and Ranking Seasonality Tests 基于随机森林的季节性检验组合和排序方法
Journal of Econometric Methods Pub Date : 2022-06-23 DOI: 10.1515/jem-2020-0020
Daniel Ollech, Karsten Webel
{"title":"A Random Forest-based Approach to Combining and Ranking Seasonality Tests","authors":"Daniel Ollech, Karsten Webel","doi":"10.1515/jem-2020-0020","DOIUrl":"https://doi.org/10.1515/jem-2020-0020","url":null,"abstract":"Abstract Virtually every seasonal adjustment software includes an ensemble of tests for assessing whether a given time series is in fact seasonal and hence a candidate for seasonal adjustment. However, such tests are certain to produce either agreeing or conflicting results, raising the questions how to identify the most accurate tests and how to aggregate the results in the latter case. We suggest a novel random forest-based approach to answer these questions. We simulate seasonal and non-seasonal ARIMA processes that are representative of the macroeconomic time series analysed regularly by the Bundesbank. Treating the time series’ seasonal status as a classification problem, we use the p-values of the seasonality tests implemented in the seasonal adjustment software JDemetra+ as predictors to train conditional random forests on the simulated data. We show that this aggregation approach avoids the size distortions of the JDemetra+ tests without sacrificing too much power compared to the most powerful test. We also find that the modified QS and Friedman tests are the most accurate ones in the considered ensemble.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"12 1","pages":"117 - 130"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47376494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Frontmatter Frontmatter
Journal of Econometric Methods Pub Date : 2022-01-01 DOI: 10.1515/jem-2022-frontmatter1
{"title":"Frontmatter","authors":"","doi":"10.1515/jem-2022-frontmatter1","DOIUrl":"https://doi.org/10.1515/jem-2022-frontmatter1","url":null,"abstract":"","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46845559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Generalized Non-Parametric Instrumental Variable-Control Function Approach to Estimation in Nonlinear Settings 非线性环境下的广义非参数仪表变量控制函数估计方法
Journal of Econometric Methods Pub Date : 2022-01-01 DOI: 10.1515/jem-2021-0038
K. Kim, Amil Petrin
{"title":"A Generalized Non-Parametric Instrumental Variable-Control Function Approach to Estimation in Nonlinear Settings","authors":"K. Kim, Amil Petrin","doi":"10.1515/jem-2021-0038","DOIUrl":"https://doi.org/10.1515/jem-2021-0038","url":null,"abstract":"Abstract When the endogenous variables enter non-parametrically into the regression equation standard linear instrumental variables approaches fail. Two existing solutions are the non-parametric instrumental variables (NPIVs) estimators, which are based on a set of conditional moment restrictions (CMRs), and the control function (CF) estimators, which use conditional mean independence (CMI) restrictions. Our first contribution is to show that – similar to CMI – the CMR place shape restrictions on the conditional expectation of the error given the instruments and endogenous variables that are sufficient for identification, and we call our new estimator based on these restrictions the CMR-CF estimator. Our second contribution is to develop an estimator for non-linear and non-parametric settings that can combine both CMR and CMI restrictions, which cannot be done in either the NPIV nor the non-parametric CF setting. This new “Generalized CMR-CF” uses both CMR and CMI restrictions together by allowing the conditional expectation of the structural error to depend on both instruments and control variables. When sieves are used to approximate both the structural function and the CF our estimator reduces to a series of least squares regressions. Our Monte Carlos illustrate that our new estimator performs well across several economic settings.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"11 1","pages":"91 - 125"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41736709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Biases in Maximum Simulated Likelihood Estimation of Bivariate Models 二元模型最大模拟似然估计中的偏差
Journal of Econometric Methods Pub Date : 2021-12-06 DOI: 10.1515/jem-2021-0003
Maksat Jumamyradov, Murat K. Munkin
{"title":"Biases in Maximum Simulated Likelihood Estimation of Bivariate Models","authors":"Maksat Jumamyradov, Murat K. Munkin","doi":"10.1515/jem-2021-0003","DOIUrl":"https://doi.org/10.1515/jem-2021-0003","url":null,"abstract":"Abstract This paper finds that the maximum simulated likelihood (MSL) estimator produces substantial biases when applied to the bivariate normal distribution. A specification of the random parameter bivariate normal model is considered, in which a direct comparison between the MSL and maximum likelihood (ML) estimators is feasible. The analysis shows that MSL produces biased results for the correlation parameter. This paper also finds that the MSL estimator is biased for the bivariate Poisson-lognormal model, developed by Munkin and Trivedi (1999. “Simulated Maximum Likelihood Estimation of Multivariate Mixed-Poisson Regression Models, with Application.” The Econometrics Journal 2: 29–48). A simulation study is conducted, which shows that MSL leads to serious inferential biases, especially large when variance parameters in the true data generating process are small. The MSL estimator produces biases in the estimated marginal effects, conditional means and probabilities of count outcomes.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"11 1","pages":"55 - 70"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42650727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Density Forecast of Financial Returns Using Decomposition and Maximum Entropy 基于分解和最大熵的财务收益密度预测
Journal of Econometric Methods Pub Date : 2021-12-03 DOI: 10.1515/jem-2020-0014
Tae-Hwy Lee, He Wang, Zhou Xi, Ru Zhang
{"title":"Density Forecast of Financial Returns Using Decomposition and Maximum Entropy","authors":"Tae-Hwy Lee, He Wang, Zhou Xi, Ru Zhang","doi":"10.1515/jem-2020-0014","DOIUrl":"https://doi.org/10.1515/jem-2020-0014","url":null,"abstract":"Abstract We consider a multiplicative decomposition of the financial returns to improve the density forecasts of financial returns. The multiplicative decomposition is based on the identity that financial return is the product of its absolute value and its sign. Advantages of modeling the two components are discussed. To reduce the effect of the estimation error due to the multiplicative decomposition in estimation of the density forecast model, we impose a moment constraint that the conditional mean forecast is set to match with the sample mean. Imposing such a moment constraint operates a shrinkage and tilts the density forecast of the decomposition model to produce the improved maximum entropy density forecast. An empirical application to forecasting density of the daily stock returns demonstrates the benefits of using the decomposition and imposing the moment constraint to obtain the improved density forecast. We evaluate the density forecast by comparing the logarithmic score (LS), the quantile score (QS), and the continuous ranked probability score (CRPS). We contribute to the literature on the density forecast and the decomposition models by showing that the density forecast of the decomposition model can be improved by imposing a sensible constraint in the maximum entropy framework.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"12 1","pages":"57 - 83"},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47740910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A New and More Versatile Approach 通过用一般人口数据补充参与者数据来建模定性结果:一种新的更通用的方法
Journal of Econometric Methods Pub Date : 2021-12-02 DOI: 10.1515/jem-2021-0004
B. Erard
{"title":"Modeling Qualitative Outcomes by Supplementing Participant Data with General Population Data: A New and More Versatile Approach","authors":"B. Erard","doi":"10.1515/jem-2021-0004","DOIUrl":"https://doi.org/10.1515/jem-2021-0004","url":null,"abstract":"Abstract Although one often has detailed information about participants in a program, the lack of comparable information on non-participants precludes standard qualitative choice estimation. This challenge can be overcome by incorporating a supplementary sample of covariate values from the general population. This paper presents new estimators based on this sampling strategy, which perform comparably to the best existing supplementary sampling estimators. The key advantage of the new estimators is that they readily incorporate sample weights, so that they can be applied to Census surveys and other supplementary data sources that have been generated using complex sample designs. This substantially widens the range of problems that can be addressed under a supplementary sampling estimation framework. The potential for improving precision by incorporating imperfect knowledge of the population prevalence rate is also explored.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"11 1","pages":"35 - 53"},"PeriodicalIF":0.0,"publicationDate":"2021-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48233372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation 具有序列相关性的二元响应面板数据模型的条件Logit的鲁棒性
Journal of Econometric Methods Pub Date : 2021-11-17 DOI: 10.1515/jem-2021-0005
D. Kwak, Robert S. Martin, J. Wooldridge
{"title":"The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation","authors":"D. Kwak, Robert S. Martin, J. Wooldridge","doi":"10.1515/jem-2021-0005","DOIUrl":"https://doi.org/10.1515/jem-2021-0005","url":null,"abstract":"Abstract We examine the conditional logit estimator for binary panel data models with unobserved heterogeneity. A key assumption used to derive the conditional logit estimator is conditional serial independence (CI), which is problematic when the underlying innovations are serially correlated. A Monte Carlo experiment suggests that the conditional logit estimator is not robust to violation of the CI assumption. We find that higher persistence and smaller time dimension both increase the magnitude of the bias in slope parameter estimates. We also compare conditional logit to unconditional logit, bias corrected unconditional logit, and pooled correlated random effects logit.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"12 1","pages":"33 - 56"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44756123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Subgraph Network Random Effects Error Components Models: Specification and Testing 子图网络随机效应误差分量模型的规范与测试
Journal of Econometric Methods Pub Date : 2021-08-18 DOI: 10.1515/jem-2021-0001
Gabriel Montes-Rojas
{"title":"Subgraph Network Random Effects Error Components Models: Specification and Testing","authors":"Gabriel Montes-Rojas","doi":"10.1515/jem-2021-0001","DOIUrl":"https://doi.org/10.1515/jem-2021-0001","url":null,"abstract":"Abstract This paper develops a subgraph random effects error components model for network data linear regression where the unit of observation is the node. In particular, it allows for link and triangle specific components, which serve as a basal model for modeling network effects. It then evaluates the potential effects of ignoring network effects in the estimation of the coefficients’ variance-covariance matrix. It also proposes consistent estimators of the variance components using quadratic forms and Lagrange Multiplier tests for evaluating the appropriate model of random components in networks. Monte Carlo simulations show that the tests have good performance in finite samples. It applies the proposed tests to the Call interbank market in Argentina.","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"11 1","pages":"17 - 34"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44074441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Linear Rescaling to Accurately Interpret Logarithms 线性缩放以准确地解释对数
Journal of Econometric Methods Pub Date : 2021-06-06 DOI: 10.1515/jem-2021-0029
Nick Huntington-Klein
{"title":"Linear Rescaling to Accurately Interpret Logarithms","authors":"Nick Huntington-Klein","doi":"10.1515/jem-2021-0029","DOIUrl":"https://doi.org/10.1515/jem-2021-0029","url":null,"abstract":"Abstract The standard approximation of a natural logarithm in statistical analysis interprets a linear change of p in ln(X) as a (1 + p) proportional change in X, which is only accurate for small values of p. I suggest base-(1 + p) logarithms, where p is chosen ahead of time. A one-unit change in log1 + p(X) is exactly equivalent to a (1 + p) proportional change in X. This avoids an approximation applied too broadly, makes exact interpretation easier and less error-prone, improves approximation quality when approximations are used, makes the change of interest a one-log-unit change like other regression variables, and reduces error from the use of log(1 + X).","PeriodicalId":36727,"journal":{"name":"Journal of Econometric Methods","volume":"12 1","pages":"139 - 147"},"PeriodicalIF":0.0,"publicationDate":"2021-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43772250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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