论文检索

受污染多期教育实验的处理效应估计：缩小班级规模的动态影响AI 解读

This paper introduces an empirical strategy to estimate dynamic treatment effects in randomized trials that provide treatment in multiple stages and in which various noncompliance problems arise, such as attrition and selective transitions between treatment and control groups. Our approach is applied to the highly influential four-year randomized class size study, Project STAR. We find benefits from attending small classes in all cognitive subject areas in kindergarten and…

加性系数模型的样条回拟核平滑AI 解读

Additive coefficient model (Xue and Yang, 2006a, 2006b) is a flexible regression and autoregression tool that circumvents the “curse of dimensionality.” We propose spline-backfitted kernel (SBK) and spline-backfitted local linear (SBLL) estimators for the component functions in the additive coefficient model that are both (i) computationally expedient so they are usable for analyzing high dimensional data, and (ii) theoretically reliable so inference can be made on the…

IPS 面板单位根检验的局部渐近功效与初始观测影响AI 解读

In this note we derive the local asymptotic power function of the standardized averaged Dickey–Fuller panel unit root statistic of Im, Pesaran, and Shin (2003, Journal of Econometrics , 115, 53–74), allowing for heterogeneous deterministic intercept terms. We consider the situation where the deviation of the initial observation from the underlying intercept term in each individual time series may not be asymptotically negligible. We find that power decreases monotonically as…

门槛模型中的外生性检验AI 解读

Most work in the area of nonlinear econometric modeling is based on a single equation and assumes exogeneity of the explanatory variables. Recently, work by Caner and Hansen (2004) and Psaradakis, Sola, and Spagnolo (2005) has considered the possibility of estimating nonlinear models by methods that take into account endogeneity but provide no tests for exogeneity. This paper examines the problem of testing for exogeneity in nonlinear threshold models. We suggest new…

线性回归中空间自相关不变检验的功效性质AI 解读

This paper derives some exact power properties of tests for spatial autocorrelation in the context of a linear regression model. In particular, we characterize the circumstances in which the power vanishes as the autocorrelation increases, thus extending the work of Krämer (2005). More generally, the analysis in the paper sheds new light on how the power of tests for spatial autocorrelation is affected by the matrix of regressors and by the spatial structure. We mainly focus…

不同收敛速度估计量的合并：侧重社会互动模型的最小 χ² 方法AI 解读

This paper considers the extension of the classical minimum distance approach for the pooling of estimates with various rates of convergence. Under a setting where relatively high rates of convergence can be attained, the minimum distance estimators are shown to be consistent and asymptotically normally distributed. The constrained estimates can be efficient relative to the unconstrained ones. The minimized distance function is shown to be asymptotically χ 2 -distributed,…

重尾误差下非平稳半强 GARCH(1,1) 模型的估计AI 解读

This paper studies the estimation of a semi-strong GARCH(1,1) model when it does not have a stationary solution, where semi-strong means that we do not require the errors to be independent over time. We establish necessary and sufficient conditions for a semi-strong GARCH(1,1) process to have a unique stationary solution. For the nonstationary semi-strong GARCH(1,1) model, we prove that a local minimizer of the least absolute deviations (LAD) criterion converges at the rate $\root \of n $…

固定效应动态面板中在单位根处保持强工具的 GMM 估计AI 解读

This paper develops new estimation and inference procedures for dynamic panel data models with fixed effects and incidental trends. A simple consistent GMM estimation method is proposed that avoids the weak moment condition problem that is known to affect conventional GMM estimation when the autoregressive coefficient ( ρ ) is near unity. In both panel and time series cases, the estimator has standard Gaussian asymptotics for all values of ρ ∈ (−1, 1] irrespective of how the…

已实现瞬时波动率的非参数滤波：核方法AI 解读

A kernel weighted version of the standard realized integrated volatility estimator is proposed. By different choices of the kernel and bandwidth, the measure allows us to focus on specific characteristics of the volatility process. In particular, as the bandwidth vanishes, an estimator of the realized spot volatility is obtained. We denote this the filtered spot volatility. We show consistency and asymptotic normality of the kernel smoothed realized volatility and the…

用于因果推断的带符号有向无环图AI 解读

Summary Formal rules governing signed edges on causal directed acyclic graphs are described and it is shown how these rules can be useful in reasoning about causality. Specifically, the notions of a monotonic effect, a weak monotonic effect and a signed edge are introduced. Results are developed relating these monotonic effects and signed edges to the sign of the causal effect of an intervention in the presence of intermediate variables. The incorporation of signed edges in…

多元或函数型数据线性预测中的成分排序与选择AI 解读

Summary The problem of component choice in regression-based prediction has a long history. The main cases where important choices must be made are functional data analysis, and problems in which the explanatory variables are relatively high dimensional vectors. Indeed, principal component analysis has become the basis for methods for functional linear regression. In this context the number of components can also be interpreted as a smoothing parameter, and so the viewpoint…

局部复合分位数回归平滑：局部多项式回归的替代方法AI 解读

Summary Local polynomial regression is a useful non-parametric regression tool to explore fine data structures and has been widely used in practice. We propose a new non-parametric regression technique called local composite quantile regression smoothing to improve local polynomial regression further. Sampling properties of the estimation procedure proposed are studied. We derive the asymptotic bias, variance and normality of the estimate proposed. The asymptotic relative…

同时降维与变量选择的稀疏偏最小二乘回归AI 解读

Summary Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate…

短期与长期因果度量：理论与推断AI 解读

The concept of causality introduced by Wiener [Wiener, N., 1956. The theory of prediction, In: E.F. Beckenback, ed., The Theory of Prediction, McGraw-Hill, New York (Chapter 8)] and Granger [Granger, C. W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods, Econometrica 37, 424-459] is defined in terms of predictability one period ahead. This concept can be generalized by considering causality at any given horizon h as well as tests for…

利用条件鞅变换检验半参数条件矩限制AI 解读

This paper studies conditional moment restrictions that contain unknown nonparametric functions, and proposes a general method of obtaining asymptotically distribution-free tests via martingale transforms. Examples of such conditional moment restrictions are single index restrictions, partially parametric regressions, and partially parametric quantile regressions. This paper introduces a conditional martingale transform that is conditioned on the variable in the…

均值—方差有效性检验的比较AI 解读

We analyse the asymptotic properties of mean-variance efficiency tests based on generalised methods of moments, and parametric and semiparametric likelihood procedures that assume elliptical innovations. We study the trade-off between efficiency and robustness, and prove that the parametric estimators provide asymptotically valid inferences when the conditional distribution of the innovations is elliptical but possibly misspecified and heteroskedastic. We compare the small…

工具变量回归的野生自助法检验AI 解读

We propose a wild bootstrap procedure for linear regression models estimated by instrumental variables. Like other bootstrap procedures that we proposed elsewhere, it uses efficient estimates of the reduced-form equation(s). Unlike the earlier procedures, it takes account of possible heteroscedasticity of unknown form. We apply this procedure to t tests, including heteroscedasticity-robust t tests, and to the Anderson–Rubin test. We provide simulation evidence that it works…

协整关系的线性检验：购买力平价应用AI 解读

This article shows that when applied to nonstationary time series, the conventional Regression Error Specification Test (RESET) leads to severe size distortion and its asymptotic distribution involves a mixture of noncentral χ2 distributions. Nonstationarity introduces bias terms in the limit distribution, and appropriate corrections for the bias are presented leading to a modified RESET test that has a central χ2 limit distribution. In simulations, this modified test is…

疯狂三月、分位数回归对阵预测与哈耶克假说AI 解读

A quantile regression variant of the classical paired comparison model of mean ratings is proposed. The model is estimated using data for the regular 2004–2005 U.S. college basketball season, and evaluated based on predictive performance for the 2005 National Collegiate Athletic Association (NCAA) basketball tournament. Rather than basing predictions entirely on conditional mean estimates produced by classical least-squares paired comparison methods, the proposed methods…

具有非正态残差的结构向量自回归AI 解读

In structural vector autoregressive (SVAR) modeling, sometimes the identifying restrictions are insufficient for a unique specification of all shocks. In this paper it is pointed out that specific distributional assumptions can help in identifying the structural shocks. In particular, a mixture of normal distributions is considered as a possible model that can be used in this context. Our model setup enables us to test restrictions which are just-identifying in a standard…

基于偏斜t分布的非高斯面板数据模型聚类AI 解读

We propose a model-based method to cluster units within a panel. The underlying model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units are clustered according to their dynamic behavior, equilibrium level, and the effect of covariates. Inference is addressed from a Bayesian perspective, and model comparison is conducted using Bayes factors. Particular attention is paid to prior elicitation and posterior propriety. We suggest priors…

考虑估计风险的参数化风险价值回测AI 解读

One of the implications of the creation of the Basel Committee on Banking Supervision was the implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk. Since then, the capital requirements of commercial banks with trading activities are based on VaR estimates. Therefore, appropriately constructed tests for assessing the out-of-sample forecast accuracy of the VaR model (backtesting procedures) have become of crucial practical importance. In this…

带长期识别约束的近非平稳 SVAR 模型推断AI 解读

This paper considers inference for impulse responses in models with highly persistent variables. We show that the impulse responses of interest are not consistently estimable under the long-run identification scheme when the strongly dependent process is parameterized as local to unity. We employ the instrumental variable framework to argue that the inconsistency and the large sampling uncertainty associated with the impulse responses arise from a weak instrument problem.…

随机占优有效性的检验AI 解读

We consider consistent tests for stochastic dominance efficiency at any order of a given portfolio with respect to all possible portfolios constructed from a set of assets. We justify block bootstrap approaches to achieve valid inference in a time series setting. The test statistics are computed using linear and mixed integer programming formulations. Monte Carlo results show that the bootstrap procedure performs well in finite samples. The empirical application reveals that…