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线性混合模型中 EBLUP 分布的参数自助近似与预测区间AI 解读

Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly useful in small area problems. The variability of an EBLUP is traditionally measured by the mean squared prediction error (MSPE), and interval estimates are generally constructed using estimates of the MSPE. Such methods have shortcomings like under-coverage or over-coverage, excessive length and lack…

近似高斯随机场最大值的分布AI 解读

Motivated by the problem of testing for the existence of a signal of known parametric structure and unknown “location” (as explained below) against a noisy background, we obtain for the maximum of a centered, smooth random field an approximation for the tail of the distribution. For the motivating class of problems this gives approximately the significance level of the maximum score test. The method is based on an application of a likelihood-ratio-identity followed by…

散布矩阵同质性的最优秩检验AI 解读

We propose a class of locally and asymptotically optimal tests, based on multivariate ranks and signs for the homogeneity of scatter matrices in m elliptical populations. Contrary to the existing parametric procedures, these tests remain valid without any moment assumptions, and thus are perfectly robust against heavy-tailed distributions (validity robustness). Nevertheless, they reach semiparametric efficiency bounds at correctly specified elliptical densities and maintain…

基于符号秩的自适应拟合优度检验AI 解读

Within the nonparametric regression model with unknown regression function l and independent, symmetric errors, a new multiscale signed rank statistic is introduced and a conditional multiple test of the simple hypothesis l=0 against a nonparametric alternative is proposed. This test is distribution-free and exact for finite samples even in the heteroscedastic case. It adapts in a certain sense to the unknown smoothness of the regression function under the alternative, and…

连续时间因果效应的统计建模AI 解读

This article studies the estimation of the causal effect of a time-varying treatment on time-to-an-event or on some other continuously distributed outcome. The paper applies to the situation where treatment is repeatedly adapted to time-dependent patient characteristics. The treatment effect cannot be estimated by simply conditioning on these time-dependent patient characteristics, as they may themselves be indications of the treatment effect. This time-dependent confounding…

利用样本分位数估计局部自相似高斯过程的 Hurst 指数AI 解读

This paper is devoted to the introduction of a new class of consistent estimators of the fractal dimension of locally self-similar Gaussian processes. These estimators are based on convex combinations of sample quantiles of discrete variations of a sample path over a discrete grid of the interval [0, 1]. We derive the almost sure convergence and the asymptotic normality for these estimators. The key-ingredient is a Bahadur representation for sample quantiles of nonlinear…

紧黎曼流形上均匀性的数据驱动 Sobolev 检验AI 解读

Data-driven versions of Sobolev tests of uniformity on compact Riemannian manifolds are proposed. These tests are invariant under isometries and are consistent against all alternatives. The large-sample asymptotic null distributions are given.

GARCH(1,1) 参数准极大似然估计的自助法高阶改进AI 解读

This paper reconsiders a block bootstrap procedure for Quasi Maximum Likelihood estimation of GARCH models, based on the resampling of the likelihood function, as proposed by Gonçalves and White [2004. Maximum likelihood and the bootstrap for nonlinear dynamic models. Journal of Econometrics 119, 199-219]. First, we provide necessary conditions and sufficient conditions, in terms of moments of the innovation process, for the existence of the Edgeworth expansion of the…

资格随机设计中的处理反应数据分析AI 解读

In this paper, we develop and compare two alternative approaches for calculating the effect of the actual intake when treatments are randomized, but compliance with the assignment in the treatment arm is less than perfect for reasons that are correlated with the outcome. The approaches are based on different identification assumptions about these unobserved confounders. In the first approach, which stems from [Sommer, A., Zeger, S., 1991. On estimating efficacy in clinical…

协变量门限引致变点的二元响应模型半参数估计AI 解读

This paper is concerned with semiparametric estimation of a threshold binary response model. The estimation method considered in the paper is semiparametric since the parameters for a regression function are finite-dimensional, while allowing for heteroskedasticity of unknown form. In particular, the paper considers Manski's [Manski, Charles F., 1975. Maximum score estimation of the stochastic utility model of choice. Journal of Econometrics 3 (3), 205-228; Manski, Charles…

含噪声资产价格的跳跃检验：互换方差方法AI 解读

This paper proposes a new test for jumps in asset prices that is motivated by the literature on variance swaps. Formally, the test follows by a direct application of Itô's lemma to the semi-martingale process of asset prices and derives its power from the impact of jumps on the third and higher order return moments. Intuitively, the test statistic reflects the cumulative gain of a variance swap replication strategy which is known to be minimal in the absence of jumps but…

不可观测因素引致样本流失时的面板模型推断AI 解读

This paper concerns identification and estimation of a finite-dimensional parameter in a panel data-model under nonignorable sample attrition. Attrition can depend on second period variables which are unobserved for the attritors but an independent refreshment sample from the marginal distribution of the second period values is available. This paper shows that under a quasi-separability assumption, the model implies a set of conditional moment restrictions where the moments…

近奇异设计下的 GMM 与广义经验似然估计AI 解读

Nearly-Singular design relaxes the nonsingularity assumption of the limit weight matrix in GMM, and the nonsingularity of the limit variance matrix for the first order conditions in GEL. The sample versions of these matrices are nonsingular, but in large samples we assume these sample matrices converge to a singular matrix. This can result in size distortions for the overidentifying restrictions test and large bias for the estimators. This nearly-singular design may occur…

检验技术无效率是否依赖企业特征的有效方法AI 解读

We wish to test whether technical inefficiency depends on observable characteristics of the firm. We consider a two-step procedure in which the second step is a regression of estimated inefficiency on firm characteristics. A valid test of the hypothesis of no effect requires an adjustment to the variance matrix of the estimates. Unfortunately the adjustment is not distribution-free. We show that this test is the LM test in the exponential case. We also consider tests based…

偏微分方程估计及其金融应用AI 解读

Linear parabolic partial differential equations (PDE's) and diffusion models are closely linked through the celebrated Feynman-Kac representation of solutions to PDE's. In asset pricing theory, this leads to the representation of derivative prices as solutions to PDE's. Very often implied derivative prices are calculated given preliminary estimates of the diffusion model for the underlying variable. We demonstrate that the implied derivative prices are consistent and derive…

双重差分与广义最小二乘：假设检验的高阶性质AI 解读

We investigate the estimation and inference in difference in difference econometric models used in the analysis of treatment effects. When the innovations in such models display serial correlation, commonly used ordinary least squares (OLS) procedures are inefficient and may lead to tests with incorrect size. Implementation of feasible generalized least squares (FGLS) procedures is often hindered by too few observations in the cross-section to allow for unrestricted…

协方差平稳面板 AR(1)／单位根模型的最大似然估计与推断AI 解读

This paper considers Maximum Likelihood (ML) based estimation and inference procedures for linear dynamic panel data models with fixed effects. The paper first studies the asymptotic properties of MaCurdy's [MaCurdy, T., 1982. The use of time series processes to model the time structure of earnings in a longitudinal data analysis. Journal of Econometrics 18, 83-114] First Difference Maximum Likelihood (FDML) estimator for the covariance stationary panel AR(1)/unit rootÂ…

贝叶斯学习模型中预测分歧的演化AI 解读

We estimate a Bayesian learning model with heterogeneity aimed at explaining expert forecast disagreement and its evolution over horizons. Disagreement is postulated to have three components due to differences in: (i) the initial prior beliefs, (ii) the weights attached on priors, and (iii) interpreting public information. The fixed-target, multi-horizon, cross-country feature of the panel data allows us to estimate the relative importance of each component precisely. The…

一般混合条件下平方 CUSUM 检验的极限分布AI 解读

We consider the cumulative sum (CUSUM) of squares test in a linear regression model with general mixing assumptions on the regressors and the errors. We derive its limit distribution and show how it depends on the nature of the error process. We suggest a corrected version that has a limit distribution free of nuisance parameters. We also discuss how it provides an improvement over the standard approach to testing for a change in the variance in a univariate times series.…

密度可能不存在时的核估计AI 解读

Nonparametric kernel estimation of density and conditional mean is widely used, but many of the pointwise and global asymptotic results for the estimators are not available unless the density is continuous and appropriately smooth; in kernel estimation for discrete-continuous cases smoothness is required for the continuous variables. Nonsmooth density and mass points in distributions arise in various situations that are examined in empirical studies; some examples and…

一致区分 I(0) 与 I(1) 过程的不可能性AI 解读

An I(0) process is commonly defined as a process that satisfies a functional central limit theorem, i.e., whose scaled partial sums converge weakly to a Wiener process, and an I(1) process as a process whose first differences are I(0). This paper establishes that with this definition, it is impossible to consistently discriminate between I(0) and I(1) processes. At the same time, on a more constructive note, there exist consistent unit root tests and also nontrivial…

有无单位根的 AR(1) 时间序列中的高斯推断AI 解读

This paper introduces a simple first-difference-based approach to estimation and inference for the AR(1) model. The estimates have virtually no finite-sample bias and are not sensitive to initial conditions, and the approach has the unusual advantage that a Gaussian central limit theory applies and is continuous as the autoregressive coefficient passes through unity with a uniform $\sqrt{n}$ rate of convergence. En route, a useful central limit theorem (CLT) for sample…

含低频随机水平跳变与异常值的协整过程渐近理论AI 解读

This is an analytical study of the effect of level-shift and temporary-change components, when present but neglected, on the trace test for cointegration. The contribution is threefold. First, we discuss in a multivariate framework, and jointly, effects that in the previous literature have been discussed in a univariate setting and in isolation. Second, we consider a rather general specification of shifts and outliers with random size, number, and timing and with flexible…

美国失业率的平滑转换与马尔可夫转换自回归模型比较AI 解读

Abstract Logistic smooth transition and Markov switching autoregressive models of a logistic transform of the monthly US unemployment rate are estimated by Markov chain Monte Carlo methods. The Markov switching model is identified by constraining the first autoregression coefficient to differ across regimes. The transition variable in the LSTAR model is the lagged seasonal difference of the unemployment rate. Out‐of‐sample forecasts are obtained from Bayesian predictive…