Least squares, preliminary test and Stein-type estimation in general vector AR(p) models

In the general vector autoregressive process AR ( p ), multivariate least square estimation (LSE)/maximum likelihood estimation (MLE) of a subset of the parameters is considered when the complementary subset is suspected to be redundant. This may be viewed as a special case of linear constraints of autoregressive parameters. We incorporate this nonsample information in the estimation process and propose preliminary test and Stein-type estimators for the target subset of parameters. Under local alternatives their asymptotic properties are investigated and compared with those of unrestricted and restricted LSE. The dominance picture of the estimators is presented.     

基于空间非均衡视角的大城市病成因研究

本文通过多次实验,选取空间自相关程度最为显著的距离阈值,设定空间权重矩阵,使得空间权重矩阵更加符合我国城市分布特征,充分体现人口在城市集聚过程中的空间自回归机制,并结合广义空间模型和广义空间两阶段最小二乘法,实证探查“大城市病”成因,研究发现:就业机会、流通经济布局和公共服务的空间非均衡是导致人口过度集聚,从而引致“大城市病”的显著相关因素,并针对以上因素给出了对策建议。     

Shrinkage estimation of the proportion in randomized response

This article considers the asymptotic estimation theory for the proportion in randomized response survey usinguncertain prior information (UPI) about the true proportion parameter which is assumed to be available on the basis of some sort of realistic conjecture. Three estimators, namely, the unrestricted estimator, the shrinkage restricted estimator and an estimator based on a preliminary test, are proposed. Their asymptotic mean squared errors are derived and compared. The relative dominance picture of the estimators is presented.     

Geometrical Interpretation of the Mean and the Constant in a Box-Jenkins Time Series Model

Certain manuals and computer programs mistakenly identify the mean with the constant in Box-Jenkins time series models. In this paper, it will be shown that (a) the mean and the constant have different values in autoregressive models, and (b) they have an algebraic and graphical relationship.     

Forecasting levels of log variables in vector autoregressions

Sometimes forecasts of the original variable are of interest, even though a variable appears in logarithms (logs) in a system of time series. In that case, converting the forecast for the log of the variable to a naïve forecast of the original variable by simply applying the exponential transformation is not theoretically optimal. A simple expression for the optimal forecast under normality assumptions is derived. However, despite its theoretical advantages, the optimal forecast is shown to be inferior to the naïve forecast if specification and estimation uncertainty are taken into account. Hence, in practice, using the exponential of the log forecast is preferable to using the optimal forecast.     

The Spatial Durbin Model and the Common Factor Tests

Abstract

The spatial Durbin model occupies an interesting position in the field of spatial econometrics. It is the reduced form of a model with cross-sectional dependence in the errors and it may be used as the nesting equation in a more general approach of model selection. Specifically, in this equation we obtain the common factor tests (of which the likelihood ratio is the best known) whose objective is to discriminate between substantive and residual dependence in an apparently misspecified equation. Our paper tries to delve deeper into the role of the spatial Durbin model in the problem of specifying a spatial econometric model. We include a Monte Carlo study related to the performance of the common factor tests presented in the paper in small sample sizes.     

Editorial

Abstract

In this editorial we summarize and comment on papers published in issue 7.1. This is a themed issue, with four of the papers being originally presented at the 9th International Workshop in Spatial Statistics and Econometrics held at the University of Orléans, France. This was organized by Cem Ertur, who was chair of the Scientific Committee, and who has co-edited the current issue and taken the lead in writing about the papers from the Orléans workshop. The first paper, which was not an Orléans paper, is ‘Business Cycles Association in a Small Monetary Union: The Case of Switzerland’ by Alexandra Ferreira-Lopes &; Tiago Sequeira. From Orléans we have ‘QML Estimation of Spatial Dynamic Panel Data Models with Time Varying Spatial Weights Matrices’ by Lung-Fei Lee &; Jihai Yu; ‘Improving the J Test in the SARAR Model by Likelihood-Based Estimation’ by Peter Burridge; ‘The Mundlak Approach in the Spatial Durbin Panel Data Model’ by Nicolas Debarsy; and ‘Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal’ by Arnab Bhattacharjee, Eduardo Castro &; João Marques.

RÉSUMÉ Dans la présente communication, nous résumons les communications publiées dans l’édition 7.1, et nous présentons des commentaires sur ces dernières. Il s'agit d'une édition à thème, quatre des communications ayant été présentées initialement au 9^ème atelier international de statistiques et d’économétrie spatiales, à l'université d'Orléans, en France. Cette édition a été organisée par Cem Ertur, qui était président du Comité scientifique, a coédité l’édition actuelle, et a pris le pas dans les communications sur les communications émanant de l'atelier d'Orléans. La première communication, qui n’était pas une communication d'Orléans, est « Association de Cycles commerciaux dans une Union monétaire restreinte: le cas de la Suisse », par Alexandra Ferreira-Lopes &; Tiago Sequeira. D'Orléans, nous avons reçu « Estimation QML de modèles de données de groupe dynamique spatial, avec matrices de poids spatiaux temporalisées », par Lung-Fei Lee &; Jihai Yu; « Optimisation du test « J » dans le modèle SARAR par estimation basée sur les probabilité », par Peter Burridge; « L'approche de Mundlak dans le modèle spatial de données de panel de Durbin », par Nicolas Debarsy; et « Interactions spatiales dans les modèles hédoniques des prix: le marché de l'immobilier urbain d'Aveiro, au Portugal », par Arnab Bhattacharjee, Eduardo Castro &; João Marques.

EXTRACTO En este trabajo resumimos y hacemos comentarios sobre trabajos publicados en la edición 7.1. Esta edición tiene un tema, y cuatro de sus estudios se presentaron originalmente en el Noveno Taller Internacional de Estadísticas Espaciales y Econometría celebrado en la Universidad de Orleans, Francia. Éste fue organizado por Cem Ertur, que presidió el Comité Científico, coeditó la edición actual y adoptó la posición líder en escribir sobre los estudios derivados del taller de trabajo de Orleans. El primer trabajo, que no fue uno de los estudios de Orleans, es la ‘Asociación de Ciclos de Negocios en una Unión Monetaria Pequeña: el Caso de Suiza’ de Alexandra Ferreira-Lopes &; Tiago Sequeira. Los estudios procedentes de Orleans son: ‘Estimación QML de modelos de datos de panel dinámicos espaciales con matrices de pesos espaciales que varían con el tiempo’ de Lung-Fei Lee &; Jihai Yu; ‘Mejora de la prueba J en el modelo SARAR por estimación basada en probabilidad’ de Peter Burridge; ‘El planteamiento Mundlak en el modelo espacial de datos de panel Durbin’ de Nicolas Debarsy; e, ‘Interacciones espaciales en modelos hedónicos de fijación de precios: el mercado de la vivienda urbana de Aveiro, Portugal’ de Arnab Bhattacharjee, Eduardo Castro &; João Marques.

     

Spanish Internal Migration: Is there Anything New to Say?

Abstract

Spanish internal migration has long been resistant to traditional economic explanations. However, this paper examines the data for the period 1999–2006 after considerable changes in the Spanish economy. Moreover, it examines migration at the disaggregated level of Spanish provinces rather than regions, the usual unit of measurement. Using a spatial error model as well as a spatial autoregression model it finds the differentials in wages and unemployment between provinces to be significant explanatory variables. House prices are also important in accounting for the dynamics of internal migration.

Les migrations internes en Espagne: qu'y a-t-il de nouveau?

Les migrations internes en Espagne résistent, depuis toujours, à des explications économiques traditionnelles. Cependant, la présente communication examine les données relatives à la période 1999–2006, dans le sillage des changements considérables qui sont survenus dans l’économie espagnole. Elle se penche également sur la migration au niveau désagrégé de provinces espagnoles plutôt que de régions, unité de mesure traditionnelle. En utilisant un modèle d'erreur spatiale ainsi qu'un modèle à autorégression spatiale, elle en conclut que les différences sur le plan des salaires et du chômage entre les différentes provinces constituent des variables explicatives significatives. En outre, le prix de l'immobilier résidentiel joue également un rôle important dans l'examen de la dynamique des migrations internes.

Migracin interior espaola: Queda algo nuevo por decir?

La migración interior española lleva mucho tiempo resistiéndose a las explicaciones económicas tradicionales. No obstante, este artículo examina los datos de 1999–2006, después de considerables cambios en la economía española. Asimismo, examina la migración al nivel desagregado de provincias españolas, en lugar de regiones, que representa la unidad típica de medida. Utilizando un modelo de error espacial, así como un modelo de autorregresión espacial, se descubre que los diferenciales en salarios y empleo entre las provincias son importantes variables explicativas. Los precios de la vivienda también son importantes a la hora de comprender la dinámica de la migración interior.

     

飞机试验模型型面加工精度的提高方法

本文通过对飞机试验模型加工案例的分析,结合目前现有的数控加工能力,探讨了在飞机试验模型加工中如何提高加工精度,并对其产生误差的原因进行深入分析,为加工出高质量的飞机试验模型奠定了基础。     

Does local government competition aggravate haze pollution? A new perspective of factor market distortion

Haze pollution has become a new threat to China's sustainable development, but it may be that local government behaviour can play an important role in the prevention and control of pollutants. A dynamic spatial autoregressive (SAR) model is used to study the relationship between local government competition and haze pollution. To further explore the indirect impact of factor market distortion on haze pollution and control potential endogeneity problems, a newly developed intermediary effect model that incorporates the characteristics of the generalized method of moments (GMM) is utilized to explore how factor market distortion indirectly affects haze pollution. The research results show that regional haze pollution in China is characterized by significant spatial correlation, and local government competition has a positive impact on haze pollution; that is, local government competition exacerbates haze pollution. In general, local government competition not only directly leads to an increase in haze pollution but also further intensifies it by distorting the local factor market, and the intermediary role of factor market distortion is approximately 7.04%. The results of the regional inspection found that competition among local governments in the eastern region did not lead to haze pollution, and distortion of the factor market did not exist as an intermediary effect. However, both direct and intermediary effects are significant in the central and western regions. Therefore, an official performance appraisal system that includes ecological constraints should be established to guide the benign transformation of local government competition, and an environmental management mechanism must be developed for joint prevention and control to reduce haze pollution. In addition, the free flow of factors and marketization are equally important.     

A Dynamic Origin-constrained Spatial Interaction Model Applied to Poland's Inter-provincial Migration

Abstract

In contemporary Europe migratory processes are becoming inceasingly important, given the diminishing impact of natural changes on population dynamics. In this paper, we focus on some determinants of internal migration in Poland. The choice of variables is guided by recent considerations about interregional migration in other transitional economies. The quantitative analysis of Polish migration flows has been performed by considering migration interactions over time. A dynamic version of an origin-constrained spatial interaction model is proposed. This approach relies on using the method of offsets, generalizing the Poisson regression framework, and applied to the calibration of spatial interaction models.

Modèle dynamique d'interactions spatiales limitées par l'origine, appliqué aux migrations interprovinciales en Pologne

RÉSUMÉ Dans l'Europe contemporaine, les processus migratoires acquièrent une importance toujours majeure, compte tenu de la réduction de l'impact des changements naturels sur la dynamique de la population. Dans la présente communication, nous nous concentrons sur certains facteurs déterminants de la migration interne en Pologne. Le choix des variables est déterminé par de récentes considérations concernant les migrations interrégionales dans d'autres économies transitionnelles. On procède à l'analyse quantitative des flux migratoires polonais, en examinant des interactions migratoires progressives. On propose une version dynamique d'un modèle d'interaction spatiale limité par l'origine: ce principe est basé sur l'utilisation de la méthode d'offsets, généralisant le cadre de régression de Poisson, et appliqué au calibrage de modèles d'interaction spatiale.

Modelo dinámico de interacción espacial restringido por origen aplicado a la emigración interprovincial de Polonia

RÉSUMÉ En la Europa contemporánea, los procesos migratorios son cada vez más importantes debido al decreciente impacto de los cambios naturales sobre la dinámica de la población. En este trabajo, nos centramos en algunos de los determinantes de la emigración interna en Polonia. La elección de variables se guía por consideraciones recientes sobre la emigración interregional en otras economías transicionales. El análisis cuantitativo de los flujos polacos de emigración se ha realizado considerando las interacciones de emigración a través del tiempo. Se propone una versión dinámica de un modelo de interacción espacial restringido por origen. Este planteamiento se basa en el empleo del método de compensaciones (method of offsets), generalizando el marco de regresión Poisson, y aplicándolo a la calibración de modelos de interacción espacial.

     

Testing for random effects in panel models with spatially correlated disturbances

In the empirical analysis of panel data the Breusch–Pagan (BP) statistic has become a standard tool to infer on unobserved heterogeneity over the cross-section. Put differently, the test statistic is central to discriminate between the pooled regression and the random effects model. Conditional versions of the test statistic have been provided to immunize inference on unobserved heterogeneity against random time effects or patterns of spatial error correlation. Panel data models with spatially correlated error terms are typically set out under the presumption of some known adjacency matrix parameterizing the correlation structure up to a scaling factor. This paper delivers a bootstrap scheme to generate critical values for the BP statistic allowing robust inference under misspecification of the adjacency matrix. Moreover, asymptotic results are derived for the case of a finite cross-section and infinite time dimension. Finite sample simulations show that misspecification of spatial covariance features could lead to large size distortions, while the robust bootstrap procedure retains asymptotic validity.