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因果推断:处理效应、政策评估与因果设计。
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Semiparametric inference in observational duration-response studies, with duration possibly right-censored

治疗时长可能右删失的观察性时长—响应研究中的半参数推断

Once treatment is found to be effective in clinical studies, attention often focuses on optimum or efficacious treatment delivery. In treatment duration-response studies, the optimum treatment delivery refers to the treatment length that optimises the mean response. In many studies, the treatment length is often left to the discretion of an attending investigator or physician but may be abruptly terminated because of treatment-terminating events. Thus, a recommended…

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Local Model Uncertainty and Incomplete-Data Bias (With Discussion)

局部模型不确定性与不完整数据偏差(含讨论)

Summary Problems of the analysis of data with incomplete observations are all too familiar in statistics. They are doubly difficult if we are also uncertain about the choice of model. We propose a general formulation for the discussion of such problems and develop approximations to the resulting bias of maximum likelihood estimates on the assumption that model departures are small. Loss of efficiency in parameter estimation due to incompleteness in the data has a dual…

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Nonparametric Identification under Discrete Variation

离散变化下的非参数识别

This paper provides weak conditions under which there is nonparametric interval identification of local features of a structural function that depends on a discrete endogenous variable and is nonseparable in latent variates. The function delivers values of a discrete or continuous outcome and instruments may be discrete valued. Application of the analog principle leads to quantile regression based interval estimators of values and partial differences of structural functions.…

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Nonparametric bounds on the returns to language skills

语言技能回报的非参数界限

Abstract This paper applies the theoretical literature on nonparametric bounds on treatment effects to the estimation of how limited English proficiency (LEP) affects wages and employment opportunities for Hispanic workers in the United States. I analyse the identifying power of several weak assumptions on treatment response and selection, and stress the interactions between LEP and education, occupation and immigration status. I show that the combination of two weak but…

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Listening to What the World Says: Bilingualism and Earnings in the United States

倾听世界的语言:美国的双语能力与收入

Is there a shortage of critical foreign language skills in the United States? Recent concerns about national security and economic globalization suggest increased demand and wage premia for foreign language speakers. The use of English as the international language, however, suggests a decrease in demand for foreign language skills in the United States. To address this question, we study a representative sample of U.S. college graduates. Ordinary least squares regressions…

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Markov switching causality and the money–output relationship

马尔可夫切换因果性与货币—产出关系

Abstract The causal link between monetary variables and output is one of the most studied issues in macroeconomics. One puzzle from this literature is that the results of causality tests appear to be sensitive with respect to the sample period that one considers. As a way of overcoming this difficulty, we propose a method for analysing Granger causality which is based on a vector autoregressive model with time‐varying parameters. We model parameter time‐variation so as to…

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Aggregate vs. disaggregate data analysis—a paradox in the estimation of a money demand function of Japan under the low interest rate policy

日本低利率政策下货币需求估计的总量与分项分析悖论

Abstract We use Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of a liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and…

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Empirical-likelihood-based semiparametric inference for the treatment effect in the two-sample problem with censoring

删失两样本问题中治疗效应的经验似然半参数推断

To compare two samples of censored data, we propose a unified semiparametric inference for the parameter of interest when the model for one sample is parametric and that for the other is nonparametric. The parameter of interest may represent, for example, a comparison of means, or survival probabilities. The confidence interval derived from the semiparametric inference, which is based on the empirical likelihood principle, improves its counterpart constructed from the common…

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Exact, Nonparametric Inference When Doses Are Measured With Random Errors

剂量含随机测量误差时的精确非参数推断

Studies that estimate the effects of exposure to a possibly harmful agent often compare exposed subjects who received varied doses with matched controls who received zero dose. If the doses are measured with error, then one may wish to use the fallible doses to estimate a linear relationship between the unobserved true dose and the observed response. If one is willing to assume that the dose errors for exposed subjects are symmetrically distributed about 0—that is, the dose…

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Are Firms Underleveraged? An Examination of the Effect of Leverage on Default Probabilities

企业杠杆是否不足?杠杆对违约概率的影响

ABSTRACT A commonly held view in corporate finance is that firms are less leveraged than they should be, given the potentially large tax benefits of debt. In this paper, I study the effect of firms' leverage on default probabilities as represented by the firms' ratings. Using an instrumental variable approach, I find that the leverage's effect on ratings is three times stronger than it is if the endogeneity of leverage is ignored. This stronger effect results in a higher…

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Fixed-Effects and Related Estimators for Correlated Random-Coefficient and Treatment-Effect Panel Data Models

相关随机系数与处理效应面板模型的固定效应及相关估计量

I derive conditions under which a class of fixed-effects estimators consistently estimates the population-averaged slope coefficients in panel data models with individual-specific slopes, where the slopes are allowed to be correlated with the covariates. In addition to including the usual fixed-effects estimator, the results apply to estimators that eliminate individual-specific trends. I apply the results, and propose alternative estimators, to estimation of average…

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Structural Equations, Treatment Effects, and Econometric Policy Evaluation1

结构方程、处理效应与计量经济政策评估

This paper uses the marginal treatment effect (MTE) to unify the nonparametric literature on treatment effects with the econometric literature on structural estimation using a nonparametric analog of a policy invariant parameter; to generate a variety of treatment effects from a common semiparametric functional form; to organize the literature on alternative estimators; and to explore what policy questions commonly used estimators in the treatment effect literature answer. A…

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Likelihood approach for marginal proportional hazards regression in the presence of dependent censoring

相依删失下边际比例风险回归的似然方法

In many public health problems, an important goal is to identify the effect of some treatment/intervention on the risk of failure for the whole population. A marginal proportional hazards regression model is often used to analyze such an effect. When dependent censoring is explained by many auxiliary covariates, we utilize two working models to condense high-dimensional covariates to achieve dimension reduction. Then the estimator of the treatment effect is obtained by…

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Testing Agency Theory with Entrepreneur Effort and Wealth

利用企业家努力与财富检验代理理论

ABSTRACT We develop a principal‐agent model in an entrepreneurial setting and test the model's predictions using unique data on entrepreneurial effort and wealth in privately held firms. Accounting for unobserved firm heterogeneity using instrumental‐variables techniques, we find that entrepreneurial ownership shares increase with outside wealth and decrease with firm risk; effort increases with ownership; and effort increases firm performance. The magnitude of the effects…

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Counts with an endogenous binary regressor: A series expansion approach

We propose an estimator for count data regression models where a binary regressor is endogenously determined. This estimator departs from previous approaches by using a flexible form for the conditional probability function of the counts. Using a Monte Carlo experiment we show that our estimator improves the fit and provides a more reliable estimate of the impact of regressors on the count when compared to alternatives which do restrict the mean to be linear‐exponential. In…

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Causal Inference Using Potential Outcomes

使用潜在结果进行因果推断

Causal effects are defined as comparisons of potential outcomes under different treatments on a common set of units. Observed values of the potential outcomes are revealed by the assignment mechanism—a probabilistic model for the treatment each unit receives as a function of covariates and potential outcomes. Fisher made tremendous contributions to causal inference through his work on the design of randomized experiments, but the potential outcomes perspective applies to…

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Job Creation or Destruction? Labor Market Effects of Wal-Mart Expansion

创造还是摧毁就业?沃尔玛扩张的劳动力市场效应

This paper estimates the effect of Wal-Mart expansion on retail employment at the county level. Using an instrumental variables approach to correct for both measurement error in entry dates and endogeneity of the timing of entry, I find that Wal-Mart entry increases retail employment by 100 jobs in the year of entry. Half of this gain disappears over the next five years as other retail establishments exit and contract, leaving a long-run statistically significant net gain of…

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Is Trade Good or Bad for the Environment? Sorting Out the Causality

贸易有益还是有害于环境?厘清因果关系

We seek to contribute to the debate over globalization and the environment by asking: What is the effect of trade on a country's environment, for a given level of GDP? We take specific account of the endogeneity of trade, using exogenous geographic determinants of trade as instrumental variables. We find that trade tends to reduce three measures of air pollution. Statistical significance is high for concentrations of SO2, moderate for NO2, and lacking for particulate matter.…

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AUTOMATIC INFERENCE OF THE CONTEMPORANEOUS CAUSAL ORDER OF A SYSTEM OF EQUATIONS

方程系统同期因果次序的自动推断

When Stephen Perez and I first began our Monte Carlo studies of the efficacy of general-to-specific search methodologies in 1995, we were keenly aware of our limited ability to capture the tacit knowledge of the skilled time-series econometrician operating in the London School of Economics (LSE) tradition (Hoover and Perez, 1999a, 1999b). Econometrics, we believed, was an art, and our algorithm was not intended to replace the artist. David Hendry and Hans-Martin Krolzig's…

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An IV Model of Quantile Treatment Effects

分位数处理效应的工具变量模型

The ability of quantile regression models to characterize the heterogeneous impact of variables on different points of an outcome distribution makes them appealing in many economic applications. However, in observational studies, the variables of interest (e.g., education, prices) are often endogenous, making conventional quantile regression inconsistent and hence inappropriate for recovering the causal effects of these variables on the quantiles of economic outcomes. In…

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Wages, Experience and Seniority

工资、工作经验与任职年限

In this paper we study the sources of wage growth. We identify the contribution to such growth of general, sector specific and firm specific human capital. Our results are interpretable within the context of a model where the returns to human capital may be heterogeneous and where firms may offer different combinations of entry level wages and firm specific human capital development. We allow for the possibility that wages are match specific and that workers move jobs as a…

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Semiparametric Difference-in-Differences Estimators

The difference-in-differences (DID) estimator is one of the most popular tools for applied research in economics to evaluate the effects of public interventions and other treatments of interest on some relevant outcome variables. However, it is well known that the DID estimator is based on strong identifying assumptions. In particular, the conventional DID estimator requires that, in the absence of the treatment, the average outcomes for the treated and control groups would…

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