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干中学速率：搜寻匹配模型的估计AI 解读

Abstract We construct and estimate by maximum likelihood a job search model where wages are set by Nash bargaining and idiosyncratic productivity follows a geometric Brownian motion. The proposed framework enables us to endogenize job destruction and to estimate the rate of learning‐by‐doing. Although the range of the observations is not independent of the parameters, we establish that the estimators satisfy asymptotic normality. The structural model is estimated using…

非平稳时间序列分位数曲线的非参数推断AI 解读

The paper considers nonparametric specification tests of quantile curves for a general class of nonstationary processes. Using Bahadur representation and Gaussian approximation results for nonstationary time series, simultaneous confidence bands and integrated squared difference tests are proposed to test various parametric forms of the quantile curves with asymptotically correct type I error rates. A wild bootstrap procedure is implemented to alleviate the problem of slow…

非可分模型中的结构性测量误差AI 解读

This paper considers measurement error from a new perspective. In surveys, response errors are often caused by the fact that respondents recall past events and quantities imperfectly. We explore the consequences of limited recall for the identification of marginal effects. Our identification approach is entirely nonparametric, using Matzkin-type nonseparable models that nest a large class of potential structural models. We show that measurement error due to limited recall…

不完全信息同时行动博弈的半参数估计AI 解读

This paper studies the problem of estimating the normal-form payoff parameters of a simultaneous, discrete game where the realization of such payoffs is not common knowledge. The paper contributes to the existing literature in two ways. First, by making a comparison with the complete information case it formally describes a set of conditions under which allowing for private information in payoffs facilitates the identification of various features of the game. Second,…

结合函数及导数数据的非参数最小二乘估计AI 解读

Cost function estimation often involves data on a function and a family of its derivatives. Such data can substantially improve convergence rates of nonparametric estimators. We propose series-type estimators which incorporate the various derivative data into a single nonparametric least-squares procedure. Convergence rates are obtained and it is shown that for low-dimensional cases, much of the beneficial impact is realized even if only data on ordinary first-order partials…

面板数据的稳健惩罚分位数回归估计AI 解读

This paper investigates a class of penalized quantile regression estimators for panel data. The penalty serves to shrink a vector of individual specific effects toward a common value. The degree of this shrinkage is controlled by a tuning parameter [lambda]. It is shown that the class of estimators is asymptotically unbiased and Gaussian, when the individual effects are drawn from a class of zero-median distribution functions. The tuning parameter, [lambda], can thus be…

存在内生性与样本选择时的面板模型估计AI 解读

We consider estimation of panel data models with sample selection when the equation of interest contains endogenous explanatory variables as well as unobserved heterogeneity. Assuming that appropriate instruments are available, we propose several tests for selection bias and two estimation procedures that correct for selection in the presence of endogenous regressors. The tests are based on the fixed effects two-stage least squares estimator, thereby permitting arbitrary…

潜在竞标人数未知时的一价拍卖估计：误分类方法AI 解读

In this paper, we consider nonparametric identification and estimation of first-price auction models when N*, the number of potential bidders, is unknown to the researcher, but observed by bidders. Exploiting results from the recent econometric literature on models with misclassification error, we develop a nonparametric procedure for recovering the distribution of bids conditional on the unknown N*. Monte Carlo results illustrate that the procedure works well in practice.…

参数比值的识别稳健置信集及其离散选择应用AI 解读

We study the problem of building confidence sets for ratios of parameters, from an identification robust perspective. In particular, we address the simultaneous confidence set estimation of a finite number of ratios. Results apply to a wide class of models suitable for estimation by consistent asymptotically normal procedures. Conventional methods (e.g. the delta method) derived by excluding the parameter discontinuity regions entailed by the ratio functions and which…

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通过模型变换估计固定效应面板随机前沿模型AI 解读

Traditional panel stochastic frontier models do not distinguish between unobserved individual heterogeneity and inefficiency. They thus force all time-invariant individual heterogeneity into the estimated inefficiency. Greene (2005) proposes a true fixed-effect stochastic frontier model which, in theory, may be biased by the incidental parameters problem. The problem usually cannot be dealt with by model transformations owing to the nonlinearity of the stochastic frontier…

动态条件分位数模型的有效估计AI 解读

In this paper we consider the problem of semiparametric efficient estimation in conditional quantile models with time series data. We construct an M-estimator which achieves the semiparametric efficiency bound recently derived by Komunjer and Vuong (forthcoming). Our efficient M-estimator is obtained by minimizing an objective function which depends on a nonparametric estimator of the conditional distribution of the variable of interest rather than its density. The estimator…

技术变化的计量建模AI 解读

The purpose of this paper is to present a new approach to econometric modeling of substitution and technical change. Substitution is determined by observable variables, such as prices of output and inputs and shares of inputs in the value of output. Our principal innovation is to represent the rate and biases of technical change by unobservable or latent variables. This representation is considerably more flexible than the constant time trends employed in the previous…

工具变量可能众多时 LIML 估计量的渐近最优性AI 解读

We consider the estimation of the coefficients of a linear structural equation in a simultaneous equation system when there are many instrumental variables. We derive some asymptotic properties of the limited information maximum likelihood (LIML) estimator when the number of instruments is large; some of these results are new as well as old, and we relate them to results in some recent studies. We have found that the variance of the limiting distribution of the LIML…

更便宜意味着更好吗？兼职教师对学生结果的影响AI 解读

Higher education has increasingly relied on part-time, adjunct instructors. Critics argue that adjuncts reduce educational quality because they often have less education than full-time professors. On the other hand, by specializing in teaching or being concurrently employed, adjuncts could enhance learning experiences. This paper quantifies how adjuncts affect subsequent student interest and course performance relative to full-time faculty using an instrumental variable…

两样本工具变量估计量AI 解读

Following an influential article by Angrist and Krueger (1992) on two-sample instrumental variables (TSIV) estimation, numerous empirical researchers have applied a computationally convenient two-sample two-stage least squares (TS2SLS) variant of Angrist and Krueger's estimator. In the two-sample context, unlike the single-sample situation, the IV and 2SLS estimators are numerically distinct. We derive and compare the asymptotic distributions of the two estimators and find…

分位数回归的置信带AI 解读

Let ( X 1 , Y 1 ), …, ( X n , Y n ) be independent and identically distributed random variables and let l ( x ) be the unknown p -quantile regression curve of Y conditional on X . A quantile smoother l n ( x ) is a localized, nonlinear estimator of l ( x ). The strong uniform consistency rate is established under general conditions. In many applications it is necessary to know the stochastic fluctuation of the process { l n ( x ) – l ( x )}. Using strong approximations of…

部分线性空间自回归模型的剖面准最大似然估计AI 解读

We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are partially linear. The rate of convergence of the spatial parameter estimator depends on some general features of the spatial weight matrix of the model. The estimators of other finite-dimensional parameters in the model have the regular -rate of convergence and the estimator of the nonparametric component is consistent but with different restrictions on the choice of bandwidth…

半参数空间自回归模型的有效估计AI 解读

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing non-stochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series nonparametric estimates of the score function are employed in adaptive estimates of parameters of interest. These estimates are as efficient as the ones based on a correct form, in particular…

未知异方差下空间自回归模型的 GMM 估计AI 解读

In the presence of heteroskedastic disturbances, the MLE for the SAR models without taking into account the heteroskedasticity is generally inconsistent. The 2SLS estimates can have large variances and biases for cases where regressors do not have strong effects. In contrast, GMM estimators obtained from certain moment conditions can be robust. Asymptotically valid inferences can be drawn with consistently estimated covariance matrices. Efficiency can be improved by…

允许截面依赖的面板数据广义非线性工具变量单位根检验AI 解读

This paper proposes a unit root test for panel data with cross-sectional dependence. The test generalizes the nonlinear IV unit root test of Chang (2002) to the case where there exist some common factors in panels. The main idea is to eliminate the cross-sectional dependence through the method of principal components as in Bai and Ng (2004) and then apply Chang's test to the treated data. Under certain conditions, the proposed test is consistent and has a standard normal…

动态面板模型的间接推断AI 解读

Maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size and large cross section sample size asymptotics. This paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference, shows unbiasedness and analyzes efficiency. Monte Carlo studies show that our procedure achieves substantial bias reductions with only mild…

面板数据均值与方差的共同断点AI 解读

This paper establishes the consistency of the estimated common break point in panel data. Consistency is obtainable even when a regime contains a single observation, making it possible to quickly identify the onset of a new regime. We also propose a new framework for developing the limiting distribution for the estimated break point, and show how to construct confidence intervals. The least squares method is used for estimating breaks in means and the quasi-maximum…

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