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等权重下的随机效应与空间自相关AI 解读

This note considers a panel data regression model with spatial autoregressive disturbances and random effects where the weight matrix is normalized and has equal elements. This is motivated by Kelejian, Prucha, and Yuzefovich (2005, Journal of Regional Science, forthcoming), who argue that such a weighting matrix, having blocks of equal elements, might be considered when units are equally distant within certain neighborhoods but unrelated between neighborhoods. We derive a…

固定效应模型误差序列相关的 Portmanteau 检验AI 解读

We propose a portmanteau test for serial correlation of the error term in a fixed effects model. The test is derived as a Lagrange multiplier test, but it also has a straightforward Wald test interpretation. In Monte Carlo experiments, the test displays good size and power properties.The authors thank the co-editor, the referee, David Drukker, Christian Hansen, and Jeffrey Wooldridge for their helpful comments.

平滑系数模型的半参数贝叶斯推断AI 解读

We describe procedures for Bayesian estimation and testing in cross sectional, panel data and nonlinear smooth coefficient models. The smooth coefficient model is a generalization of the partially linear or additive model wherein coefficients on linear explanatory variables are treated as unknown functions of an observable covariate. In the approach we describe, points on the regression lines are regarded as unknown parameters and priors are placed on differences between…

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预测者多快吸收新信息？跨国调查证据AI 解读

Abstract Using forecasts from Consensus Economics Inc., we provide evidence on the efficiency of real GDP growth forecasts by testing whether forecast revisions are uncorrelated. As the forecast data used are multi‐dimensional—18 countries, 24 monthly forecasts for the current and the following year and 16 target years—the panel estimation takes into account the complex structure of the variance–covariance matrix due to propagation of shocks across countries and economic…

测量误差对生产函数估计的影响评估AI 解读

Abstract This article explores to what extent the poor results that are often found when estimating parameters of production functions can be attributed to measurement errors, due to the use of common price deflators across firms. Because of the lack of detailed micro‐economic data, econometricians have to rely on industry‐wide deflators when computing outputs and intermediate inputs. A unique feature of the longitudinal data used in this paper is that it reports firm‐level…

财富动态：减少面板数据中的噪声AI 解读

Abstract Although the asset data from the Health and Retirement Study (HRS) is of very high quality, there is sufficient noise to frustrate attempts to study saving behaviour by examining wave‐to‐wave change in wealth. In this research, we attempt to reduce noise by means of reactive‐dependent interviewing in which respondents with large inexplicable changes in assets between 1998 and 2000 are called back by HRS interviewers, presented with their prior reports and asked to…

具有异质线性趋势的二元选择面板模型的一致估计AI 解读

This paper presents an extension of fixed effects binary choice models for panel data, to the case of heterogeneous linear trends. Two estimators are proposed: a Logit estimator based on double conditioning and a semiparametric, smoothed maximum score estimator based on double differences. We investigate small-sample properties of these estimators with a Monte Carlo simulation experiment, and compare their statistical properties with standard fixed effects procedures. An…

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扩散指数预测的置信区间与因子增强回归推断AI 解读

We consider the situation when there is a large number of series, N, each with T observations, and each series has some predictive ability for some variable of interest. A methodology of growing interest is first to estimate common factors from the panel of data by the method of principal components and then to augment an otherwise standard regression with the estimated factors. In this paper, we show that the least squares estimates obtained from these factor-augmented…

具有多因子误差结构的大规模异质面板：估计与推断AI 解读

This paper presents a new approach to estimation and inference in panel data models with a general multifactor error structure. The unobserved factors and the individual-specific errors are allowed to follow arbitrary stationary processes, and the number of unobserved factors need not be estimated. The basic idea is to filter the individual-specific regressors by means of cross-section averages such that asymptotically as the cross-section dimension (N) tends to infinity,…

动态随机前沿模型中的推断AI 解读

Abstract An important issue in models of technical efficiency measurement concerns the temporal behaviour of inefficiency. Consideration of dynamic models is necessary but inference in such models is complicated. In this paper we propose a stochastic frontier model that allows for technical inefficiency effects and dynamic technical inefficiency, and use Bayesian inference procedures organized around data augmentation techniques to provide inferences. Also provided are…

利用两个随机过程预测死因—年龄特定死亡人数AI 解读

Mortality forecasts are critical information for assessing the health of a population and are necessary for making informed decisions about how best to direct health-related resources and activities. Timeliness in making health statistics available is crucial to identify and address current health problems. Being motivated to meet these needs, we propose a method to forecast the number of cause-age specific deaths through a two random processes model. Unlike the previous…

含内生时变协变量的不完整泊松数据的条件推断方法AI 解读

We investigate the effect of protease inhibitors (PIs) on the rate of emergency room (ER) visits among HIV-infected women from a longitudinal cohort study. One strategy for accounting for serial correlation in longitudinal studies is to assume that observations are independent, conditional on unit-specific nuisance parameters. It is possible to estimate these models using unconditional maximum likelihood, where the nuisance parameters are assigned a parametric distribution…

二元纵向数据半参数动态模型的推断AI 解读

This article deals with the analysis of a hierarchical semiparametric model for dynamic binary longitudinal responses. The main complicating components of the model are an unknown covariate function and serial correlation in the errors. Existing estimation methods for models with these features are of O(N3), where N is the total number of observations in the sample. Therefore, nonparametric estimation is largely infeasible when the sample size is large, as in typical in the…

重复测量分析中的秩回归AI 解读

We consider rank-based regression models for repeated measures. To account for possible withinsubject correlations, we decompose the total ranks into between- and within-subject ranks and obtain two different estimators based on between- and within-subject ranks. A simple perturbation method is then introduced to generate bootstrap replicates of the estimating functions and the parameter estimates. This provides a convenient way for combining the corresponding two types of…

纵向数据中次序约束假设的检验AI 解读

Summary In many biomedical studies, we are interested in comparing treatment effects with an inherent ordering. We propose a quadratic score test (QST) based on a quadratic inference function for detecting an order in treatment effects for correlated data. The quadratic inference function is similar to the negative of a log-likelihood, and it provides test statistics in the spirit of a χ2-test for testing nested hypotheses as well as for assessing the goodness of fit of…

函数型与纵向数据分析中主成分方法的性质AI 解读

The use of principal component methods to analyze functional data is appropriate in a wide range of different settings. In studies of “functional data analysis,” it has often been assumed that a sample of random functions is observed precisely, in the continuum and without noise. While this has been the traditional setting for functional data analysis, in the context of longitudinal data analysis a random function typically represents a patient, or subject, who is observed…

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动态反馈对面板模型 LS 与矩估计精度的影响AI 解读

The finite sample behaviour is analysed of particular least squares (LS) and a range of (generalized) method of moments (MM) estimators in panel data models with individual effects and both a lagged dependent variable regressor and another explanatory variable. The latter may be affected by lagged feedbacks from the de- pendent variable too. Asymptotic expansions indicate how the order of magnitude of bias of MM estimators tends to increase with the number of moment condi-…

动态面板模型中极大似然估计的偏差削减AI 解读

This paper investigates a simple dynamic linear panel regression model with both fixed effects and time effects. Using “large n and large T” asymptotics, we approximate the distribution of the fixed effect estimator of the autoregressive parameter in the dynamic linear panel model and derive its asymptotic bias. We find that the same higher order bias correction approach proposed by Hahn and Kuersteiner (2002, Econometrica 70, 1639–1659) can be applied to the dynamic linear…

因子分析中更多数据总是更好吗？AI 解读

Factors estimated from large macroeconomic panels are being used in an increasing number of applications. However, little is known about how the size and the composition of the data affect the factor estimates. In this paper, we question whether it is possible to use more series to extract the factors, and yet the resulting factors are less useful for forecasting, and the answer is yes. Such a problem tends to arise when the idiosyncratic errors are cross-correlated. It can…

动态面板离散选择模型的参数界AI 解读

Identification of dynamic nonlinear panel data models is an important and delicate problem in econometrics. In this paper we provide insights that shed light on the identification of parameters of some commonly used models. Using these insights, we are able to show through simple calculations that point identification often fails in these models. On the other hand, these calculations also suggest that the model restricts the parameter to lie in a region that is very small in…