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广义矩估计中的一致矩条件选择程序AI 解读

This paper considers a generalized method of moments (GMM) estimation problem in which one has a vector of moment conditions, some of which are correct and some incorrect. The paper introduces several procedures for consistently selecting the correct moment conditions. Application of the results of the paper to instrumental variables estimation problems yields consistent procedures for selecting instrumental variables. The paper specifies moment selection criteria that are…

近不稳定 AR(p) 过程的渐近推断AI 解读

In this paper nearly unstable AR( p ) processes (in other words, models with characteristic roots near the unit circle) are studied. Our main aim is to describe the asymptotic behavior of the least-squares estimators of the coefficients. A convergence result is presented for the general complex-valued case. The limit distribution is given by the help of some continuous time AR processes. We apply the results for real-valued nearly unstable AR( p ) models. In this case the…

半参数选择偏差模型的最大似然估计及其艾滋病疫苗试验应用AI 解读

The following problem is treated: given s possibly selection biased samples from an unknown distribution function, and assuming that the sampling rule weight functions for each of the samples are mathematically specified up to a common unknown finite-dimensional parameter, how can we use the data to estimate the unknown parameters? We propose a simple maximum partial likelihood method for deriving the semiparametric maximum likelihood estimator. A discussion of assumptions…

评估状态空间过程中的观测预测影响AI 解读

An important inferential objective in state space modelling is to recover unobserved states using fixed-interval smoothing. Thus, the identification of cases which have a substantial influence on the smoothers is a relevant practical problem. To facilitate this identification, we propose a case-deletion diagnostic which can be easily computed using the outputs of the standard filtering and smoothing algorithms. Our diagnostic is defined as the Kullback-Leibler directed…

广义线性模型的可识别性、非正常先验与 Gibbs 抽样AI 解读

Markov chain Monte Carlo algorithms are widely used in the fitting of generalized linear models (GLMs). Such model fitting is somewhat of an art form, requiring suitable trickery and tuning to obtain results in which one can have confidence. A wide range of practical issues arise. The focus here is on parameter identifiability and posterior propriety. In particular, we clarify that nonidentifiability arises for usual GLMs and discuss its implications for simulation-based…

来源国社会项目对不同类别赴美移民的影响AI 解读

"This article uses a unique set of pooled cross-sectional and time series data to examine the annual rate of U.S. immigration during 1972-1991 from 60 source countries. One distinguishing feature of the article is that it breaks out and cross-classifies various classes of immigrants--numerically limited versus numerically exempt and new immigrant versus adjustment of status. A second distinguishing feature is that it utilizes a unique vector of variables relating to the…

来源国社会保障项目与不同类别美国移民AI 解读

This article uses a unique set of pooled cross-sectional and time series data to examine the annual rate of U.S. immigration during 1972–1991 from 60 source countries. One distinguishing feature of the article is that it breaks out and cross-classifies various classes of immigrants—numerically limited versus numerically exempt and new immigrant versus adjustment of status. A second distinguishing feature is that it utilizes a unique vector of variables relating to the…

This paper examines the roles played by innovations identified from a simple four-variable VAR characterized by cointegration. Using knowledge of cointegration rank and “textbook” relations that link macroeconomic aggregates, we identify distinct “real” and “nominal” innovations that dictate the long-run behavior of the model. We also examine the explanatory power of transitory innovations that are orthogonal to these permanent shocks. One of the permanent shocks displays…

面板数据的对称归一化工具变量估计AI 解读

The authors discuss the estimation of linear panel-data models with sequential moment restrictions using symmetrically normalized generalized method of moments (SNM) estimators and limited information maximum likelihood (LIML) analogues. These estimators are asymptotically equivalent to standard generalized method of moments (GMM) estimators but are invariant to normalization and tend to have a smaller finite-sample bias, especially when the instruments are poor. The authors…

Jackknife 工具变量估计AI 解读

Two-stage-least-squares (2SLS) estimates are biased towards the probability limit of OLS estimates. This bias grows with the degree of over-identification and can generate highly misleading results. In this paper we propose two simple alternatives to 2SLS and limited-information-maximum-likelihood (LIML) estimators for models with more instruments than endogenous regressors. These estimators can be interpreted as instrumental variables procedures using an instrument that is…

弱工具变量下LIML与刀切工具变量估计量的小样本性质AI 解读

Using Monte Carlo simulations we study the small sample performance of the traditional TSLS, the LIML and four new jackknife IV estimators when the instruments are weak. We find that the new estimators and LIML have a smaller bias but a larger variance than the TSLS. In terms of root mean square error, neither LIML nor the new estimators perform uniformly better than the TSLS. The main conclusion from the simulations and an empirical application on labour supply functions is…

证据不完美时的法医学身份识别AI 解读

We study the problem of forensic identification when the trace evidence from the scene of the crime is imperfect: for example, it might be measured with error, or be partially missing. A general framework for imperfect data is developed, and applied in particular to the following cases, singly and in combination: ‘measurement error’ in the recorded information at the scene of the crime; ‘binning’, i.e. discretisation of an originally continuous crime measurement; ‘paternity…

随机曲线分布众数的非参数估计AI 解读

Summary Motivated by the need to develop meaningful empirical approximations to a ‘typical’ data value, we introduce methods for density and mode estimation when data are in the form of random curves. Our approach is based on finite dimensional approximations via generalized Fourier expansions on an empirically chosen basis. The mode estimation problem is reduced to a problem of kernel-type multivariate estimation from vector data and is solved using a new recursive…

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Jeffreys 先验下联立方程模型有限信息分析的后验分布AI 解读

This paper studies the use of the Jeffreys prior in Bayesian analysis of the simultaneous equations model (SEM). Exact representations are obtained for the posterior density of the structural coefficient β in canonical SEMs with two endogenous variables. For the general case with m endogenous variables and an unknown covariance matrix, the Laplace approximation is used to derive an analytic formula for the same posterior density. Both the exact and the approximate formulas…

弱工具变量回归中结构参数的推断AI 解读

The authors consider the problem of making asymptotically valid inference on structural parameters in instrumental variables regression with weak instruments. Using local-to-zero asymptotics, they derive the asymptotic distributions of likelihood ratio (LR) and Lagrange multiplier (LM) type statistics for testing simple hypotheses on structural parameters based on maximum likelihood and generalized methods of moments estimation methods. In contrast to the nonstandard…

计算机化自适应测试中异常作答者的贝叶斯识别AI 解读

We consider the problem of identifying examinees with aberrant response patterns in a computerized adaptive test (CAT). The vector Y of responses of an examinee from a CAT is a multivariate response vector. Multivariate observations may be outlying in many different directions, and we characterize specific directions as corresponding to outliers with different interpretations. We develop a class of outlier statistics to identify different types of outliers based on a control…

计算机自适应测试中异常作答者的贝叶斯识别AI 解读

We consider the problem of identifying examinees with aberrant response patterns in a computerized adaptive test (CAT). The vector Y of responses of an examinee from a CAT is a multivariate response vector. Multivariate observations may be outlying in many different directions, and we characterize specific directions as corresponding to outliers with different interpretations. We develop a class of outlier statistics to identify different types of outliers based on a control…

傅里叶回归阶数识别的最优设计AI 解读

For the Fourier regression model, we determine optimal designs for identifying the order of periodicity. It is shown that the optimal design problem for trigonometric regression models is equivalent to the problem of optimal design for discriminating between certain homo-and heteroscedastic polynomial regression models. These optimization problems are then solved using the theory of canonical moments, and the optimal discriminating designs for the Fourier regression model…

双因素方差分析中的交互作用与异常值AI 解读

The two-way analysis of variance with interactions is a well established and integral part of statistics. In spite of its long standing, it is shown that the standard definition of interactions is counterintuitive and obfuscates rather than clarifies. A different definition of interaction is given which among other advantages allows the detection of interactions even in the case of one observation per cell. A characterization of unconditionally identifiable interaction…

工业时间序列中可能不连续出现的异常值检测AI 解读

Summary A method for robust estimation and multiple outlier detection in time series generated by autoregressive integrated moving average processes in industrial environments is developed. The procedure is based on reweighted maximum likelihood estimation using Huber or redescending weights and, therefore, generalizes the well-established robust M-estimation procedures used in the regression framework. When the scalar process is non-stationary, the computations required can…

利用税制改革估计劳动供给反应AI 解读

The 1980s tax reforms and the changing dispersion of wages offer one of the best opportunities yet to estimate labor supply effects. Nevertheless, changing sample composition, aggregate shocks, the changing composition of the tax paying population, and discontinuities in the tax system create serious identification and estimation problems. The authors develop grouping estimators that address these issues. Their results reveal positive and moderately sized wage elasticities.…

随机截断下的强大数定律AI 解读

The random truncation model is defined by the conditional probability distribution $H (x, y) =P[X\leq x,Y\leq y |X \geq Y] where $X$ and $Y$ are independent random variables. A problem of interest is the estimation of the distribution function $F$ of $X$ with data from the distribution $H$. Under random truncation, $F$ need not be fully identifiable from $H$ and only a part of it, say $F_0$ , is. We show that the nonparametric MLE $F_n$ of $F_0$ obeys the strong law of large numbers in the sense that for any nonnegative, measurable function $…

识别多元时间序列模型的统一方法AI 解读

This article proposes a Bayesian procedure for simultaneous identification of the Kronecker indices and model parameters of a multivariate linear system. The model parameters include the starting values and innovations of the system so that the series considered may be co-integrated or non-invertible. The procedure uses some recent developments in stochastic search variable selection in linear regression analysis and Markov chain Monte Carlo methods in statistical computing.…