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A Unified Approach to Identifying Multivariate Time Series Models

识别多元时间序列模型的统一方法

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.…

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Some Linear Models for Estimating the Motion of Rigid Bodies with Applications to Geometric Quality Assurance

用于刚体运动估计与几何质量检验的线性模型

A geometrical method to assess the quality of a part is to sample points on the surface of the part and to bring an ideal computer representation of the part as close as possible to the sampled locations. The points are sampled with a coordinate measuring machine (CMM) that gives the coordinates, with respect to the machine's own three-dimensional system of axis, of the sampled locations. Computer-assisted design (CAD) gives the ideal representation of the part. The matching…

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A Unified Approach to Identifying Multivariate Time Series Models

多元时间序列模型识别的统一方法

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.…

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Some Linear Models for Estimating the Motion of Rigid Bodies with Applications to Geometric Quality Assurance

刚体运动估计的线性模型及其几何质量检验应用

A geometrical method to assess the quality of a part is to sample points on the surface of the part and to bring an ideal computer representation of the part as close as possible to the sampled locations. The points are sampled with a coordinate measuring machine (CMM) that gives the coordinates, with respect to the machine's own three-dimensional system of axis, of the sampled locations. Computer-assisted design (CAD) gives the ideal representation of the part. The matching…

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Structural Models of the Liquidity Effect

流动性效应的结构模型

In this paper we examine a number of recent studies that claim to have obtained a well-defined liquidity effect using structural VAR models based on broad measures of money. These studies can be distinguished in terms of the identifying restrictions, sample periods, and frequency of data used. We show that estimation of the structural coefficients of all these models can be achieved by instrumental-variable methods, where the instruments are predetermined variables and the…

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Business Cycle Turning Points, A New Coincident Index, and Tests of Duration Dependence Based on a Dynamic Factor Model With Regime Switching

商业周期转折点、新同步指数与持续期依赖:状态转换动态因子模型

The synthesis of the dynamic factor model of Stock and Watson (1989) and the regime-switching model of Hamilton (1989) proposed by Diebold and Rudebusch (1996) potentially encompasses both features of the business cycle identified by Burns and Mitchell (1946): (1) comovement among economic variables through the cycle and (2) nonlinearity in its evolution. However, maximum-likelihood estimation has required approximation. Recent advances in multimove Gibbs sampling…

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A Stochastic Volatility Model With Markov Switching

具有马尔可夫状态切换的随机波动模型

This article presents a new way of modeling time-varying volatility. We generalize the usual stochastic volatility models to encompass regime-switching properties. The unobserved state variables are governed by a first-order Markov process. Bayesian estimators are constructed by Gibbs sampling. High-, medium- and low-volatility states are identified for the Standard and Poor's 500 weekly return data. Persistence in volatility is explained by the persistence in the low- and…

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Modelling Longitudinal and Spatially Correlated Data: Methods, Applications, and Future Directions

Generalized Linear Models.- Linear Models, Vector Spaces, and Residual Likelihood.- An Assessment of Approximate Maximum Likelihood Estimators in Generalized Linear Models.- Scaled Link Functions for Heterogeneous Ordinal Response Data.- Longitudinal Data Analysis.- Software Design for Longitudinal Data Analysis.- Asymptotic Properties of Nonlinear Mized-Effects Models.- Structured Antedependence Models for Longitudinal Data.- Effect of Confounding and Other Misspecification…

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Identification and Kullback information in the GLSEM

一般线性结构计量模型中的识别与 Kullback 信息

In this paper we employ the Kullback information apparatus in (a) obtaining the strong consistency of the maximum likelihood (ML) estimator in the standard version of the general linear structural econometric model (GLSEM); (b) deriving very siccinctly the necessary and sufficient (nas) conditions for identification by the use of exclision restrictions. The arguments given in (a), however, are equally applicable to a wide class of nonlinear models and the arguments in (b)…

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Business cycle analysis without much theory A look at structural VARs

少量理论约束下的商业周期分析:审视结构 VAR

This paper examines the usefulness of applying structural vector autoregressions (SVARs) to the study of business cycles. The SVAR approach aims to provide robust inferences, by imposing only weak theoretical restrictions. We illustrate that the robustness of conclusions drawn from SVAR exercises are questionable. We also examine the problem of identification failure in structural VAR models.

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Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants

利用参军申请者的社会保障数据估计志愿兵役的劳动力市场影响

Effects of military service on veterans' earnings are estimated using Social Security administrative records to fit models with heterogeneous potential outcomes. The first estimation strategy uses matching and regression to compare applicants who enlisted with applicants who did not enlist. The second uses instrumental variables generated by an error in military entrance exams. The empirical results suggest that military service led to higher employment rates for veterans.…

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Accounting for Dropouts in Evaluations of Social Programs

社会项目评估中如何处理项目退出者

This paper explores issues that arise in the evaluation of social programs using experimental data in the frequently encountered case where some of the experimental treatment group members drop out of the program prior to receiving treatment. We begin with the standard estimator for this case and the identifying assumption upon which it rests. We then examine the behavior of the estimator when the dropouts receive a partial "dose" of the program treatment prior to dropping…

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Robust Models in Probability Sampling

概率抽样中的稳健模型

Summary In the estimation of a population mean or total from a random sample, certain methods based on linear models are known to be automatically design consistent, regardless of how well the underlying model describes the population. A sufficient condition is identified for this type of robustness to model failure; the condition, which we call ‘internal bias calibration’, relates to the combination of a model and the method used to fit it. Included among the internally…

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Semiparametric Latent Variable Model Estimation with Endogenous or Mismeasured Regressors

含内生或误测解释变量的半参数潜变量模型估计

A simple root n consistent, asymptotically normal semiparametric estimator of the coefficient vector beta in the latent variable specification y = L (beta'x + e) is constructed. The distribution of e is unknown and may be correlated with x or be conditionally heteroscedastic, e.g., x can contain measurement error. The function L can also be unknown. The identification assumption is that e is uncorrelated with instruments u and that the conditional distribution of e given x…

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Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models

一类动态非线性统计模型的估计与预测

A class of nonlinear state-space models, characterized by a single source of randomness, is introduced. A special case, the model underpinning the multiplicative Holt-Winters method of forecasting, is identified. Maximum likelihood estimation based on exponential smoothing instead of a Kalman filter, and with the potential to be applied in contexts involving non-Gaussian disturbances, is considered. A method for computing prediction intervals is proposed and evaluated on…

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The Standardized Influence Matrix and its Applications

标准化影响矩阵及其应用

In this article we introduce the standardized influence matrix (SIM) of parameter estimators as a conjugate of the sample covariance matrix of standardized influence functions (SIFs) evaluated at the data points. We propose principal component analysis for the SIM and its complement (SIM analysis) as a diagnostic tool for regression or other statistical inference, and provide theoretical insight to the local influence defined by Cook. We show that SIM analysis reveals the…

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An Analysis of Transformations for Additive Nonparametric Regression

加性非参数回归中的因变量变换分析

We consider a nonparametric regression model with a parametric family of dependent variable transformations, one of which induces additive covariate effects. We estimate the additive regression effects using the integration method and estimate the transformation parameter from a profiled instrumental variable and pseudolikelihood criterion. The asymptotic distributions of the parameter and regression estimates are given. The practical performance is investigated via an…

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Instrumental-Variable Estimation of Count Data Models: Applications to Models of Cigarette Smoking Behavior

计数数据模型的工具变量估计:吸烟行为模型的应用

As with most analyses involving microdata, applications of count data models must somehow account for unobserved heterogeneity. The count model literature has generally assumed that unobservables and observed covariates are statistically independent. Yet for many applications this independence assumption is clearly tenuous. When the unobservables are omitted variables correlated with included regressors, standard estimation methods will generally be inconsistent. Though…

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Some Impossibility Theorems in Econometrics With Applications to Structural and Dynamic Models

计量经济学中的若干不可能性定理及其在结构与动态模型中的应用

General characterizations of valid confidence sets and tests in problems involving (locally almost) unidentified (LAU) parameters are presented. In particular, any valid confidence set for an unbounded LAU parameter must be unbounded with positive probability. Consequently, almost surely bounded confidence sets, like Wald-type confidence sets, have zero coverage probability and Wald-type test statistics cannot be pivotal functions. The results are applied to simultaneous…

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Interdependent preferences: an econometric analysis

相互依赖偏好的计量分析

The theoretical model of Gaertner (1974) and Pollak (1976) for the interdependence of preferences in the Linear Expenditure System is estimated for a cross-section of households. The interdependence of consumption of different households has implications for the stochastic structure of the model and for the identifiability of its parameters. Both aspects are dealt with. The empirical results indicate a significant role played by the interdependence of preferences. One of its…

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Bounding Causal Effects Using Data from a Contaminated Natural Experiment: Analysing the Effects of Teenage Childbearing

利用受污染自然实验数据界定因果效应:青少年生育的影响

In this paper, we consider what can be learned about causal effects when one uses a contaminated instrumental variable. In particular, we consider what inferences can be made about the causal effect of teenage childbearing on a teen mother's subsequent outcomes when we use the natural experiment of miscarriages to form an instrumental variable for teen births. Miscarriages might not meet all of the conditions required for an instrumental variable to identify such causal…

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Estimating Outcome Distributions for Compliers in Instrumental Variables Models

工具变量模型中服从者结果分布的估计

In Imbens and Ingrist (1994), Angrist, Imbens and Rubin (1996) and Imbens and Rubin (1997), assumptions have been outlined under which instrumental variables estimands can be given a causal interpretation as a local average treatment effect without requiring functional form or constant treatment effect assumptions. We extend these results by showing that under these assumptions one can estimate more from the data than the average causal effect for the subpopulation of…

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