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结构变化断点日期的连续记录Laplace推断AI 解读

Building upon the continuous record asymptotic framework recently introduced by Casini and Perron (2020a) for inference in structural change models, we propose a Laplace-based (Quasi-Bayes) procedure for the construction of the estimate and confidence set for the date of a structural change. It is defined by an integration rather than an optimization-based method. A transformation of the least-squares criterion function is evaluated in order to derive a proper distribution,…

估计断点之后的推断AI 解读

In an important class of econometric problems, researchers select a target parameter by maximizing the Euclidean norm of a data-dependent vector. Examples that can be cast into this frame include threshold regression models with estimated thresholds and structural break models with estimated break dates. Estimation and inference procedures that ignore the randomness of the target parameter can be severely biased and misleading when this randomness is non-negligible. This…

混合首次穿越时间模型的似然计算AI 解读

We present a method for computing the likelihood of a mixed hitting-time model that specifies durations as the first time a latent Lévy process crosses a heterogeneous threshold. This likelihood is not generally known in closed form, but its Laplace transform is. Our approach to its computation relies on numerical methods for inverting Laplace transforms that exploit special properties of the first passage times of Lévy processes. We use our method to implement a maximum…

利用平滑与筛方法求解动态离散选择模型AI 解读

We propose to combine smoothing, simulations and sieve approximations to solve for either the integrated or expected value function in a general class of dynamic discrete choice (DDC) models. We use importance sampling to approximate the Bellman operators defining the two functions. The random Bellman operators, and therefore also the corresponding solutions, are generally non-smooth which is undesirable. To circumvent this issue, we introduce smoothed versions of the random…

递增拍卖中的竞价摩擦AI 解读

This paper develops an approach for identifying and estimating the distribution of valuations in ascending auctions where an indeterminate number of bidders have an unknown number of bidding opportunities. To finesse the complications for identification and estimation due to multiple equilibria, our empirical analysis is based on the fact that bidders play undominated strategies in every equilibrium. We apply the model to a monthly financial market in which local banks…

非法药物使用、教育与劳动力市场结果AI 解读

In this paper we investigate the causal effects of consuming illegal drugs on educational attainment, employment, and wages. To identify these effects we develop and estimate a dynamic structural model to jointly consider decisions of whether to consume drugs, attend school, participate in the labor force, and save.

动态离散选择模型的半参数估计AI 解读

We consider the estimation of dynamic binary choice models in a semiparametric setting, in which the per-period utility functions are known up to a finite number of parameters, but the distribution of utility shocks is left unspecified. This semiparametric setup differs from most of the existing identification and estimation literature for dynamic discrete choice models. To show identification we derive and exploit a new recursive representation for the unknown quantile…

结构动态 Logit 模型中未观测异质性的充分统计量AI 解读

We study the identification and estimation of structural parameters in dynamic panel data logit models where decisions are forward-looking and the joint distribution of unobserved heterogeneity and observable state variables is nonparametric, i.e., fixed-effects model. We consider models with two endogenous state variables: the lagged decision variable, and the time duration in the last choice. This class of models includes as particular cases important economic applications…

多元跳跃扩散模型的有效估计与滤波AI 解读

This paper develops estimators of the transition density, filters, and parameters of multivariate jump–diffusion models. The drift, volatility, jump intensity, and jump magnitude are allowed to be state-dependent and non-affine. It is not necessary to diagonalize the volatility matrix. Our density and filter estimators converge at the canonical rate typically associated with exact Monte Carlo estimation. Our parameter estimators have the same asymptotic distribution as…

参数维数增长的时间序列模型平均预测AI 解读

An important problem with the model averaging approach is the choice of weights. In this paper, a generalized Mallows model averaging (GMMA) criterion for choosing weights is developed in the context of an infinite order autoregressive (AR(∞)) process. The GMMA method adapts to the circumstances in which the dimensions of candidate models can be large and increase with the sample size. The GMMA method is shown to be asymptotically optimal in the sense of achieving the lowest…

类别解释变量的收缩估计AI 解读

This paper introduces a flexible regularization approach that reduces point estimation risk of group means stemming from e.g. categorical regressors, (quasi-)experimental data or panel data models. The loss function is penalized by adding weighted squared ℓ2-norm differences between group location parameters and informative first stage estimates. Under quadratic loss, the penalized estimation problem has a simple interpretable closed-form solution that nests methods…

横截面与时间依赖下大面板的无平滑参数推断AI 解读

This paper addresses inference in large panel data models in the presence of both cross-sectional and temporal dependence of unknown form. We are interested in making inferences that do not rely on the choice of any smoothing parameter as is the case with the often employed “HAC” estimator for the covariance matrix. To that end, we propose a cluster estimator for the asymptotic covariance of the estimators and valid bootstrap schemes that do not require the selection of a…

含未观测效应的非线性面板模型的积分似然推断AI 解读

We propose a new integrated likelihood based approach for estimating panel data models when the unobserved individual effects enter the model nonlinearly. Unlike existing integrated likelihoods in the literature, the one we propose is closer to a genuine likelihood. Although the statistical theory for the proposed estimator is developed in an asymptotic setting where the number of individuals and the number of time periods both approach infinity, results from a simulation…

效用最大化二元预测中的模型选择AI 解读

The maximum utility estimation proposed by Elliott and Lieli (2013) can be viewed as cost-sensitive binary classification; thus, its in-sample overfitting issue is similar to that of perceptron learning. A utility-maximizing prediction rule (UMPR) is constructed to alleviate the in-sample overfitting of the maximum utility estimation. We establish non-asymptotic upper bounds on the difference between the maximal expected utility and the generalized expected utility of the…

具有条件稀疏结构的大型协方差矩阵的非参数估计AI 解读

This paper studies estimation of covariance matrices with conditional sparse structure. We overcome the challenge of estimating dense matrices using a factor structure, the challenge of estimating large-dimensional matrices by postulating sparsity on covariance of random noises, and the challenge of estimating varying matrices by allowing factor loadings to smoothly change. A kernel-weighted estimation approach combined with generalised shrinkage is proposed. Under some…

协变量选择性缺失下的非参数回归AI 解读

We consider the problem of regression with selectively observed covariates in a nonparametric framework. Our approach relies on instrumental variables that explain variation in the latent covariates but have no direct effect on selection. The regression function of interest is shown to be a weighted version of observed conditional expectation where the weighting function is a fraction of selection probabilities. Nonparametric identification of the fractional probability…

局部平稳模型的间接推断AI 解读

We propose the use of indirect inference estimation to conduct inference in complex locally stationary models. We develop a local indirect inference algorithm and establish the asymptotic properties of the proposed estimator. Due to the nonparametric nature of locally stationary models, the resulting indirect inference estimator exhibits nonparametric rates of convergence. We validate our methodology with simulation studies in the confines of a locally stationary moving…

不规则识别与大量极端倾向得分下处理效应的有效推断AI 解读

This paper provides a framework for conducting valid inference for causal parameters without imposing strong variance or support restrictions on the propensity score. In particular, it covers the case of irregularly identified treatment effect parameters. We provide limit theorems for inverse probability weighting and doubly robust estimation of causal or counterfactual parameters that do not rely on trimming approaches. By construction the limiting distributions of these…

工具变量模型检验中真实与名义拒绝概率差的界AI 解读

This paper presents a simple method for carrying out inference in a wide variety of possibly nonlinear IV models under weak assumptions. The method provides a finite-sample bound on the difference between the true and nominal probabilities of rejecting a correct null hypothesis. The method is a non-Studentized version of the S test of Stock and Wright (2000) but is implemented and analyzed differently. It does not require restrictive distributional assumptions, linearity of…

通过两阶段非参数惩罚样条求解欧拉方程AI 解读

This study proposes a novel estimation-based approach to solving asset pricing models for both stationary and time-varying observations. Our method is robust to misspecification errors while inheriting a closed-form solution. By representing the Euler equation into a well-posed integral equation of the second kind, we propose a penalized two-stage nonparametric estimation method and establish its optimal convergence under mild conditions. With the merit of penalized splines,…

简便且可靠的聚类稳健 GMM 推断AI 解读

This paper develops a new asymptotic theory for GMM estimation and inference in the presence of clustered dependence. The key feature of our alternative asymptotics is that the number of clusters G is regarded as fixed as the sample size increases. Under the fixed-G asymptotics, we show that the Wald and t tests in two-step GMM are asymptotically pivotal only if we recenter the estimated moment process in the clustered covariance estimator (CCE). Also, the J statistic, the…

时变模型平均AI 解读

Structural changes often occur in economics and finance due to changes in preferences, technologies, institutional arrangements, policies, crises, etc. Improving forecast accuracy of economic time series with structural changes is a long-standing problem. Model averaging aims at providing an insurance against selecting a poor forecast model. All existing model averaging approaches in the literature are designed with constant (non-time-varying) combination weights. Little…

识别二元选择中的异质性社会效应AI 解读

We identify and estimate heterogeneous social effects within groups of individuals that make binary choices. These heterogeneous social effects, which include peer and contextual effects, are modeled through unobserved influence matrices that summarize how the members within each group affect each other’s outcomes. We recover parameters in social effects as well as the unknown influence matrices by exploiting how these matrices are linked to the reduced-form effects of…

用于简单假设检验的贝叶斯稳健卡方检验AI 解读

In this paper, we introduce a new Bayesian chi-squared test based on an adjusted quadratic loss function for testing a simple null hypothesis. We show that the asymptotic null distribution of our suggested test is a central chi-squared distribution under some assumptions required for the Bayesian large sample theory. We refer to our test as the Bayesian robust chi-squared test, since it is robust to parametric misspecification in the alternative model. That is, the limiting…