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一般隐马尔可夫模型最大似然估计量的一致性AI 解读

Consider a parametrized family of general hidden Markov models, where both the observed and unobserved components take values in a complete separable metric space. We prove that the maximum likelihood estimator (MLE) of the parameter is strongly consistent under a rather minimal set of assumptions. As special cases of our main result, we obtain consistency in a large class of nonlinear state space models, as well as general results on linear Gaussian state space models and…

Cliff–Ord 面板模型中的 Hausman 检验AI 解读

This paper studies the random effects model and the fixed effects model for spatial panel data. The model includes a Cliff and Ord type spatial lag of the dependent variable as well as a spatially lagged one-way error component structure, accounting for both heterogeneity and spatial correlation across units. We discuss instrumental variable estimation under both the fixed and the random effects specifications and propose a spatial Hausman test which compares these two…

Hausman 检验与弱工具变量AI 解读

We consider the following problem. There is a structural equation of interest that contains an explanatory variable that theory predicts is endogenous. There are one or more instrumental variables that credibly are exogenous with regard to this structural equation, but which have limited explanatory power for the endogenous variable. Further, there is one or more potentially 'strong' instruments, which has much more explanatory power but which may not be exogenous. Hausman…

少量政策变化下的双重差分推断AI 解读

In difference-in-differences applications, identification of the key parameter often arises from changes in policy by a small number of groups. In contrast, typical inference assumes that the number of groups changing policy is large. We present an alternative inference approach for a small (finite) number of policy changers, using information from a large sample of nonchanging groups. Treatment effect point estimators are not consistent, but we can consistently estimate…

风险厌恶竞标者第一价格拍卖的半参数估计AI 解读

In view of the non-identification of the first-price auction model with risk-averse bidders, this paper proposes some parametric identifying restrictions and a semiparametric estimator for the risk aversion parameter(s) and the latent distribution of private values. Specifically, we exploit heterogeneity across auctioned objects to establish semiparametric identification under a conditional quantile restriction of the bidders' private value distribution and a…

方差动态：期权与高频收益的联合证据AI 解读

This paper analyzes the S&P 500 index return variance dynamics and the variance risk premium by combining information in variance swap rates constructed from options and quadratic variation estimators constructed from tick data on S&P 500 index futures. Estimation shows that the index return variance jumps. The jump arrival rate is not constant over time, but is proportional to the variance rate level. The variance jumps are not rare events but arrive frequently. Estimation…

随机时间下收益波动率度量中的因果效应AI 解读

We provide a structural approach to identify instantaneous causality effects between durations and stock price volatility. So far, in the literature, instantaneous causality effects have either been excluded or cannot be identified separately from Granger type causality effects. By giving explicit moment conditions for observed returns over (random) duration intervals, we are able to identify an instantaneous causality effect. The documented causality effect has significant…

基于已实现变差度量的投机性价格波动率简约式建模框架AI 解读

Building on realized variance and bipower variation measures constructed from high-frequency financial prices, we propose a simple reduced form framework for effectively incorporating intraday data into the modeling of daily return volatility. We decompose the total daily return variability into the continuous sample path variance, the variation arising from discontinuous jumps that occur during the trading day, as well as the overnight return variance. Our empirical…

含不可观测异质性的拍卖模型识别与估计AI 解读

In many procurement auctions, the bidders' unobserved costs depend both on a common shock and on idiosyncratic private information. Assuming a multiplicative structure, I derive sufficient conditions under which the model is identified and propose a non-parametric estimation procedure that results in uniformly consistent estimators of the cost components' distributions. The estimation procedure is applied to data from Michigan highway procurement auctions. Private…

缺失数据与测量误差下的收入贫困估计AI 解读

Reliable measures of poverty are an essential statistical tool for public policies aimed at reducing poverty. In this article we consider the reliability of income poverty measures based on survey data which are typically plagued by missing data and measurement error. Neglecting these problems can bias the estimated poverty rates. We show how to derive upper and lower bounds for the population poverty rate using the sample evidence, an upper bound on the probability of…

协变量依赖删失下的异方差变换模型AI 解读

In this article we propose an inferential procedure for transformation models with conditional heteroscedasticity in the error terms. The proposed method is robust to covariate dependent censoring of arbitrary form. We provide sufficient conditions for point identification. We then propose an estimator and show that it is √n-consistent and asymptotically normal. We conduct a simulation study that reveals adequate finite sample performance. We also use the estimator in an…

乘法均值独立性下数据误差混合模型中期望结果的识别AI 解读

We consider the problem of identifying a mean outcome in corrupt sampling where the observed outcome is drawn from a mixture of the distribution of interest and another distribution. Relaxing the contaminated sampling assumption that the outcome is statistically independent of the mixing process, we assess the identifying power of an assumption that the conditional means of the distributions differ by a factor of proportionality. For binary outcomes, we consider the special…

贝叶斯模型平均中的默认先验与预测表现：增长决定因素应用AI 解读

Bayesian model averaging (BMA) has become widely accepted as a way of accounting for model uncertainty, notably in regression models for identifying the determinants of economic growth. To implement BMA the user must specify a prior distribution in two parts: a prior for the regression parameters and a prior over the model space. Here we address the issue of which default prior to use for BMA in linear regression. We compare 12 candidate parameter priors: the unit…

动态随机一般均衡模型的动态识别AI 解读

This paper studies dynamic identification of parameters of a dynamic stochastic general equilibrium model from the first and second moments of the data. Classical results for dynamic simultaneous equations do not apply because the state space solution of the model does not constitute a standard reduced form. Full rank of the Jacobian matrix of derivatives of the solution parameters with respect to the parameters of interest is necessary but not sufficient for identification.…

凸矩预测模型中的尖锐识别域AI 解读

We provide a tractable characterization of the sharp identi…cation region of the parameters in a broad class of incomplete econometric models.Models in this class have set valued predictions that yield a convex set of conditional or unconditional moments for the observable model variables.In short, we call these models with convex moment predictions.Examples include static, simultaneous move …nite games of complete and incomplete information in the presence of multiple…

含逆向选择与道德风险的合同模型：非参数识别AI 解读

This paper studies the nonparametric identification of a contract model with adverse selection and moral hazard. Specifically, we consider the false moral hazard model developed by Laffont and Tirole (1986). We first extend this model to allow for general random demand and cost functions. We establish the nonparametric identification of the demand, cost, deterministic transfer, and effort disutility functions as well as the joint distribution of the random elements of the…

非参数工具变量回归AI 解读

Nous nous intéressons à l'estimation non paramétrique d'une fonction de régression instrumentale ϕ .Cette fonction est définie à l'aide de conditions de moment provenant d'un modèle économétrique structurel de la forme ( )des variables endogènes et les W des instruments.La fonction ϕ est alors la solution d'un problème inverse mal posé, et nous proposons une procédure d'estimation utilisant la régularisation de Tikhonov.Le papier analyse l'identification et la…

二元因变量三角方程系统中的部分识别AI 解读

This paper studies the special case of the triangular system of equations in Vytlacil and Yildiz (2007), where both dependent variables are binary but without imposing the restrictive support condition required by Vytlacil and Yildiz (2007) for identification of the average structural function (ASF) and the average treatment effect (ATE). Under weak regularity conditions, we derive upper and lower bounds on the ASF and the ATE. We show further that the bounds on the ASF and…

应用非参数工具变量估计AI 解读

Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up to finitely many parameters (e.g., a linear model), and instrumental variables are used identify and estimate these parameters. However, linear and other finite-dimensional…

弱收敛假设下的检验效率AI 解读

The asymptotic validity of tests is usually established by making appropriate primitive assumptions, which imply the weak convergence of a specific function of the data, and an appeal to the continuous mapping theorem. This paper, instead, takes the weak convergence of some function of the data to a limiting random element as the starting point and studies efficiency in the class of tests that remain asymptotically valid for all models that induce the same weak limit. It is…

具有半参数限制的缺失数据模型的效率界AI 解读

This paper shows that the semiparametric efficiency bound for a parameter identified by an unconditional moment restriction with data missing at random (MAR) coincides with that of a particular augmented moment condition problem. The augmented system consists of the inverse probability weighted (IPW) original moment restriction and an additional conditional moment restriction which exhausts all other implications of the MAR assumption. The paper also investigates the value…

在早产儿围产期护理观察研究中构建更强工具变量AI 解读

An instrument is a random nudge toward acceptance of a treatment that affects outcomes only to the extent that it affects acceptance of the treatment. Nonetheless, in settings in which treatment assignment is mostly deliberate and not random, there may exist some essentially random nudges to accept treatment, so that use of an instrument might extract bits of random treatment assignment from a setting that is otherwise quite biased in its treatment assignments. An instrument…

不完整纵向二元数据的贝叶斯收缩模型AI 解读

We consider inference in randomized longitudinal studies with missing data that is generated by skipped clinic visits and loss to follow-up. In this setting, it is well known that full data estimands are not identified unless unverified assumptions are imposed. We assume a non-future dependence model for the drop-out mechanism and partial ignorability for the intermittent missingness. We posit an exponential tilt model that links non-identifiable distributions and…

利用逻辑斯蒂结构直接效应模型估计二元结局的受控直接效应AI 解读

We consider the problem of assessing whether an exposure affects a dichotomous outcome other than by modifying a given mediator. The standard approach, logistic regression adjusting for both exposure and the mediator, is known to be biased in the presence of confounders for the mediator-outcome relationship. Because additional regression adjustment for such confounders is only justified when they are not affected by the exposure, inverse probability weighting has been…