Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals

This paper considers semiparametric efficient estimation of conditional moment models with possibly nonsmooth residuals in unknown parametric components (θ) and unknown functions (h) of endogenous variables. We show that: (1) the penalized sieve minimum distance (PSMD) estimator can simultaneously achieve root-n asymptotic normality of and nonparametric optimal convergence rate of , allowing for noncompact function parameter spaces; (2) a simple weighted bootstrap procedure consistently estimates the limiting distribution of the PSMD ; (3) the semiparametric efficiency bound formula of [Ai, C., Chen, X., 2003. Efficient estimation of models with conditional moment restrictions containing unknown functions. Econometrica, 71, 1795–1843] remains valid for conditional models with nonsmooth residuals, and the optimally weighted PSMD estimator achieves the bound; (4) the centered, profiled optimally weighted PSMD criterion is asymptotically chi-square distributed. We illustrate our theories using a partially linear quantile instrumental variables (IV) regression, a Monte Carlo study, and an empirical estimation of the shape-invariant quantile IV Engel curves.     

Predicting bankruptcy in the Texas nursing facility industry

Approximately 50% of nursing facilities in Texas petitioned for bankruptcy during the 1998–2004 period. Using a logit regression model tested for robustness, we find nursing facilities that are profit-seekers, chain members, pay higher than average wage rates, accept more intensive-care residents and obtain a larger than average portion of their funding from public sources are highly vulnerable to negative changes in regulatory policy decisions on Medicare and Medicaid reimbursement. Larger facilities having higher than average occupancy rates and quality of care are less susceptible to adverse decisions. The model correctly classifies a facility as either bankrupt or solvent in about 75% of cases. We also examine the duration of bankruptcy using accelerated failure-time models. It appears that the duration of bankruptcy depends on location, out-of-state ownership, length of ownership, volume of resident days supplied, total cost and proportion of revenues from Medicaid.     

Measuring a boom and bust: The Sydney housing market 2001–2006

The Sydney housing market peaked in 2003. The period 2001–2006 is, therefore, of particular interest since it captures a boom and bust in the housing market. We compute hedonic, repeat-sales and median price indexes for five regions in Sydney over this period. While the three approaches are in broad agreement regarding the timing of the turning point in the housing market, some important differences also emerge. In particular, we find evidence of sample selection bias in our hedonic and repeat-sales data sets (with the former focusing more on better quality dwellings and the latter more on lower quality dwellings). These sample selection biases could in turn cause bias (in opposite directions) in our hedonic and repeat-sales indexes. Median indexes may likewise be biased as a result of an apparent decline in the average quality of dwellings sold in the latter part of the sample. We also find evidence of convergence in prices across regions during the boom and divergence in the subsequent bust.     

物流系统的数学模型

作为企业“第三利润源泉”的物流,对其成本的控制目前已经成为物流合理化进程中的热点问题。文章把物流系统看成是一个特殊的生产系统,用柯布一道格拉斯生产函数方程对其进行建模。把物流系统的产出——满足服务需求看成是此方程的产出,把运输费用、储存费用和管理费用看戍是投入．对物流系统的数学建模提供了一个新的方法。最后用这个物流系统模型对社会物流系统进行了实例验证。     

收益最大化中心选址问题的数学模型

物流中心的选址问题在物流中心的规划设计中占有重要的战略地位,合理的物流中心选址方案可以有效地节约费用,提高企业的竞争力。文章针对第三方物流企业选址的要求,以物流中心收益最大化为目标,并同时考虑物流中心固定运营成本和可变运营成本以及退货所产生的损失费用等因素,建立了一个新的物流中心选址模型。并针对该模型,提出了一种改进的微粒群算法。     

Quantile cointegrating regression

Quantile regression has important applications in risk management, portfolio optimization, and asset pricing. The current paper studies estimation, inference and financial applications of quantile regression with cointegrated time series. In addition, a new cointegration model with quantile-varying coefficients is proposed. In the proposed model, the value of cointegrating coefficients may be affected by the shocks and thus may vary over the innovation quantile. The proposed model may be viewed as a stochastic cointegration model which includes the conventional cointegration model as a special case. It also provides a useful complement to cointegration models with (G)ARCH effects. Asymptotic properties of the proposed model and limiting distribution of the cointegrating regression quantiles are derived. In the presence of endogenous regressors, fully-modified quantile regression estimators and augmented quantile cointegrating regression are proposed to remove the second order bias and nuisance parameters. Regression Wald tests are constructed based on the fully modified quantile regression estimators. An empirical application to stock index data highlights the potential of the proposed method.     

Self-employment of immigrants and natives in Sweden – a multilevel analysis

Recent research suggests that self-employment among immigrants is due to a combination of multiple situational, cultural and institutional factors, all acting together. Using multilevel regression and unique data on the entire population of Sweden for the year 2007, this study attempts to quantify the relative importance for the self-employed of embeddedness in ethnic contexts (country of birth) and regional business and public regulatory frameworks (labour market areas). This information indicates whether the layers under consideration are valid constructs of the surroundings that influence individual self-employment. The results show that 10% (women) and 8% (men) of the total variation in individual differences in self-employment can be attributed to the country of birth. When labour market areas are included in the analyses, the share of the total variation increases to 14% for women and 12% for men. The results show that the ethnic context and the economic environment play a minor role in understanding individual differences in self-employment levels. The results can have important implications when planning interventions or other actions focusing on self-employment as public measures to promote self-employment often are based on geographic areas and ethnic contexts.     

A Spatial Modelling Framework for Income Estimation

Abstract

The main aim of this paper is to provide a spatial modelling framework for income estimation through the application of a contemporary spatial analysis technique. The application refers to the modelling of mean recorded household income in the area covered by the postcodes of the municipality of Athens in 2001. The main findings suggest that there is a very strong relationship between the proportion of people with a postgraduate qualification (namely, a Master's degree or PhD) and mean household income. Furthermore, there is evidence that this relationship is not stationary across space. This finding allows a better understanding as well as modelling of the main determinants of income in Athens.     

Spatial Nonstationarity and Spurious Regression: the Case with a Row-normalized Spatial Weights Matrix

Abstract

This paper investigates the spurious regression in the spatial setting where the regressant and regressors may be generated from possible nonstationary spatial autoregressive processes. Under the near unit root specification with a row-normalized spatial weights matrix, it is shown that the possible spurious regression phenomena in the spatial setting are relatively weaker than those in the nonstationary time series scenario. The regression estimates might or might not converge to 0. The divergence might occur only when the regressant has a near unit root much closer to unity than that of the regressor. For the t and F statistics, there could be over-rejection of the null of uncorrelatedness under certain situations, but they do not diverge. However, the coefficient of determination R ² converges to 0, which provides strong evidence of the spurious regression even when t and F statistics are large. Simulation results about different statistics are in line with the theoretical results we derive in this paper.

Non-stationnarité spatiale et fausse régression: l'argument pour la matrice de pondération spatiale à normalisation ‘row-normalized’

RÉSUMÉ?La présente communication se penche sur la fausse régression dans les cadres spatiaux, o[ugrave] des variables dépendantes et des variables explicatives peuvent être produites par d’éventuels procédés autorégressifs spatiaux non stationnaires. Dans le cadre de la spécification de la racine quasi-unitaire, avec une matrice de pondération spatiale normalisée ‘row-normalized’, il est démontré que les phénomènes de fausse régression dans les cadres spatiaux sont relativement plus faibles que ceux du scénario à série chronologique non stationnaire. Pour les statistiques t et F, on pourra assister à une sur-réjection du néant de la non corrélation dans certaines circonstances, mais aucune divergence. Toutefois, le coefficient de détermination R² converge vers 0, en apportant ainsi une preuve substantielle de la fausse, même en présence de statistiques t et F élevées. Les résultats des simulations sur différentes statistiques sont en accord avec les résultats théoriques que nous dérivons dans la présente communication.

No estacionariedad espacial y regresión falsa: el caso con la matriz de pesos espaciales standardizada por filas

RÉSUMÉ?Este trabajo investiga la regresión falsa en el ámbito espacial donde la variable dependiente y las variables independientes pueden generarse a partir de posibles procesos autorregresivos espaciales no estacionarios. Bajo la especificación de raíz unitaria con una matriz de pesos espaciales estandarizada por filas, se muestra que los posibles fenómenos de regresión falsa son relativamente más débiles que los del caso de la serie de tiempo no estacionario. En las estadísticas t y F, podría producirse un sobrerrechazo de la hipótesis nula de incorrelación bajo ciertas situaciones, pero no son divergentes. No obstante, el coeficiente de determinación R² converge a 0, lo que ofrece una evidencia fuerte de la regresión falsa incluso cuando las estadísticas t y F son amplias. Los resultados de simulación sobre diferentes estadísticas se mantienen en línea con los resultados teóricos que obtenemos en este trabajo.

     

Spatial Allocation of Capital: The Role of Risk Preferences

Abstract

This paper considers a model of spatial allocation of investment capital under uncertainty. We demonstrate that the spatial concentration of economic activity depends upon properties of risk preferences deeper than risk aversion. The degree of so-called relative prudence unambiguously decides whether or not the diversification of income risk favours the geographic dispersion of economic activity. In our framework we relate risk diversification with economic integration. Then there exists risk preferences so that spatial concentration of industry and capital is not affected by the degree of economic integration or segmentation of the regions. We also study the impact of net return regressibility upon spatial allocation.

Affectation spatiale du capital: le rôle des préférences de risques

RÉSUMÉ La présente communication se penche sur un modèle d'application spatiale du capital-investissement en présence d'incertitudes. Nous démontrons que la concentration spatiale de l'activité économique est tributaire de propriétés de préférences de risque plus profondes que l'aversion au risque. Le degré de la soi-disant prudence relative décide sans ambiguïté si la diversification des risques de revenus favorise la dispersion géographique de l'activité économique. Dans le cadre de notre étude, nous examinons la diversification du risque en fonction de l'intégration économique. Il existe également des préférences de risque, qui font en sorte que la concentration spatiale de l'industrie et du capital n'est pas affectée par le degré d'intégration économique de la segmentation des régions.

Asignación espacial de capital: la función de las preferencias de riesgo

EXTRACTO Este trabajo considera un modelo de asignación espacial de capital de inversión en situación incierta. Demostramos que la concentración espacial de actividad económica depende más profundamente de las propiedades de las preferencias de riesgo que de la aversión al riesgo. El grado de la llamada prudencia relativa decide sin ambigüedad si la diversificación del riesgo de ingresos favorece, o no, la dispersión geográfica de la actividad económica. En nuestro marco, relacionamos la diversificación del riesgo con la integración económica. Asimismo, existen preferencias de riesgos para que la concentración espacial de la industria y el capital no sean afectados por el grado de integración o segmentación económica de las regiones.