Self-weighted and local quasi-maximum likelihood estimators for ARMA-GARCH/IGARCH models

The limit distribution of the quasi-maximum likelihood estimator (QMLE) for parameters in the ARMA-GARCH model remains an open problem when the process has infinite 4th moment. We propose a self-weighted QMLE and show that it is consistent and asymptotically normal under only a fractional moment condition. Based on this estimator, the asymptotic normality of the local QMLE is established for the ARMA model with GARCH (finite variance) and IGARCH errors. Using the self-weighted and the local QMLEs, we construct Wald statistics for testing linear restrictions on the parameters, and their limiting distributions are given. In addition, we show that the tail index of the IGARCH process is always 2, which is independently of interest.     

One-step R-estimation in linear models with stable errors

Classical estimation techniques for linear models either are inconsistent, or perform rather poorly, under α$\alpha$-stable error densities; most of them are not even rate-optimal. In this paper, we propose an original one-step R-estimation method and investigate its asymptotic performances under stable densities. Contrary to traditional least squares, the proposed R-estimators remain root-n$n$ consistent (the optimal rate) under the whole family of stable distributions, irrespective of their asymmetry and tail index. While parametric stable-likelihood estimation, due to the absence of a closed form for stable densities, is quite cumbersome, our method allows us to construct estimators reaching the parametric efficiency bounds associated with any prescribed values (_α0,_b0)$({\alpha }_0,b_0)$ of the tail index α$\alpha$ and skewness parameter b$b$, while preserving root-n$n$ consistency under any (α,b)$(\alpha ,b)$ as well as under usual light-tailed densities. The method furthermore avoids all forms of multidimensional argmin computation. Simulations confirm its excellent finite-sample performances.     

Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension

Pseudo maximum likelihood estimates are developed for higher-order spatial autoregressive models with increasingly many parameters, including models with spatial lags in the dependent variables both with and without a linear or nonlinear regression component, and regression models with spatial autoregressive disturbances. Consistency and asymptotic normality of the estimates are established. Monte Carlo experiments examine finite-sample behaviour.     

Spatial wage disparities: Sorting matters!

Spatial wage disparities can result from spatial differences in the skill composition of the workforce, in non-human endowments, and in local interactions. To distinguish between these explanations, we estimate a model of wage determination across local labour markets using a very large panel of French workers. We control for worker characteristics, worker fixed effects, industry fixed effects, and the characteristics of the local labour market. Our findings suggest that individual skills account for a large fraction of existing spatial wage disparities with strong evidence of spatial sorting by skills. Interaction effects are mostly driven by the local density of employment. Not controlling for worker heterogeneity leads to very biased estimates of interaction effects. Endowments only appear to play a small role.     

Identifying strategic interactions in Swedish local income tax policies

This paper uses data on Swedish local governments to test for strategic interaction in local tax setting. We make use of a number of indirect predictions from the theories of tax competition and yardstick competition in order to test for the presence of strategic interaction in these forms. Using such additional predictions of the theories serves a twofold purpose—first it helps us establish if the spatial coefficient is due to strategic interactions or merely reflecting spatial error correlation, and second, it helps identify the source of interaction. The analysis provides strong evidence for spatial correlation in tax rates among Swedish local governments. Moreover, we find weak evidence of tax competition effects in the setting of tax rates.     

Looking across borders: A test of spatial policy interdependence using local government efficiency ratings

Spatial patterns in (local) government taxation and spending decisions have received a lot of attention. Still, the focus on taxation or expenditure levels in previous studies may be incomplete. Indeed, (rational) individuals are likely to consider the level of spending on (or taxation for) public goods provision simultaneously with how much public goods they actually receive—thus assessing the ‘price/quantity’ of government policies. Therefore, the present paper argues that incumbents may want their ‘price/quantity’ ratio to be close to that in neighbouring regions. Analysing Flemish local governments' efficiency ratings for the year 2000 (which relate total spending to the quantity of locally provided public goods), we confirm the existence of neighbourhood effects in local government policies.     

新形势下城乡规划应对空间发展问题的策略探析

时代进步社会变迁,中国城乡面貌日新月异的同时逐渐面临严峻的空间发展问题.首先探讨了城乡规划面临的空间发展问题及产生原因,并归纳总结了国家应对战略所体现的四个基本思路:区域城乡空间协调发展、节约集约利用资源、保护耕地和保障生态安全格局;并对实现国家战略意图的三大主要空间规划进行对比分析,明确了城乡规划的功能定位;最后以此...     

Spatial stochastic frontier models: accounting for unobserved local determinants of inefficiency

This paper analyzes the productivity of farms across 370 municipalities in the Center-West region of Brazil. A stochastic frontier model with a latent spatial structure is proposed to account for possible unknown geographical variation of the outputs. The paper compares versions of the model that include the latent spatial effect in the mean of output or as a variable that conditions the distribution of inefficiency, include or not observed municipal variables, and specify independent normal or conditional autoregressive priors for the spatial effects. The Bayesian paradigm is used to estimate the proposed models. As the resultant posterior distributions do not have a closed form, stochastic simulation techniques are used to obtain samples from them. Two model comparison criteria provide support for including the latent spatial effects, even after considering covariates at the municipal level. Models that ignore the latent spatial effects produce significantly different rankings of inefficiencies across agents.

Spatial heteroskedasticity and autocorrelation consistent estimation of covariance matrix

This paper considers spatial heteroskedasticity and autocorrelation consistent (spatial HAC) estimation of covariance matrices of parameter estimators. We generalize the spatial HAC estimator introduced by Kelejian and Prucha (2007) to apply to linear and nonlinear spatial models with moment conditions. We establish its consistency, rate of convergence and asymptotic truncated mean squared error (MSE). Based on the asymptotic truncated MSE criterion, we derive the optimal bandwidth parameter and suggest its data dependent estimation procedure using a parametric plug-in method. The finite sample performances of the spatial HAC estimator are evaluated via Monte Carlo simulation.     

The role of spatial interdependence in local government cost efficiency: An application to waste Italian sector

The paper aims to study the effect of spatial interdependence, among nearby municipalities, on public services efficiency. An empirical analysis on the waste disposal service in 4250 Italian municipalities was carried out to evaluate the efficiency of waste management expenditure, once the impact of positive/negative externalities, of neighbouring local governments, on efficiency levels is isolated. From a methodological point of view, our study extends the spatial stochastic frontier methodology proposed in Fusco & Vidoli (2013) usable only for production analysis, allowing to admit into a cost frontier the spatial autocorrelation among residuals. Ignoring spatial autocorrelation leads to inferential problems violating one of the most important assumption of classical regression models: the non-correlation of the residuals. We found a significant spatial interdependence among neighbouring municipalities in term of cost efficiency, that, thanks to the methodology proposed, has been isolated allowing a discussion on the specific efficiency of municipalities. These results may suggest the need to consider proximity effects in future investigations about the efficiency of waste management and, more generally, of public services.     

Spatial interdependence in cost efficiency and local government optimal size: The case of Italian municipalities

This study provides an original approach to empirically determine the optimal size of municipalities for efficient social service delivery in Italy, accounting for spatial spillover effects across municipalities as well as endogeneity between the output provided and its cost. A highly disaggregated and up-to-date database of Italian municipalities is used to estimate, through endogenous spatial frontier of Kutlu et al. (2020), the minimum costs. In municipalities with population ranging from 2000 to 3500 economies of scale in social service provision emerge, exhibiting cost-optimality. This outcome is completely ignored in some Regional government where financing criteria are based on historical, non-public, and ex-post repayment criteria.     

Intra-metropolitan shifts in labor demand and the adjustment of local markets

I offer new evidence on the adjustment of local labor markets to geographic shifts in labor demand within US metropolitan areas using a unique data set in which metropolitan subregions are geographically matched across the 1970–1980 and 1980–1990 decades. The evidence uncovered paints the following picture. Workers, especially those with less education, make incomplete adjustments within metropolitan areas in response to intra-metropolitan demand shifts. Although blacks may not make especially limited adjustments, they have disproportionately suffered deleterious effects from job movements because the demand shifts have tended to be away from their places of residence.     

Liquidity,bank runs,and fire sales under local thinking

We examine the implications on banking crises when markets are populated by agents that neglect tail risks and form expectations conditioned on a favorable subset of possible states of the economy. We find that optimal bank liquidity is lower than would be the case under rational expectations, and, consequently, the banking system is more vulnerable to adverse shocks, which lead to bank runs. Asset pledgeability of surviving banks is also affected so that their capacity to raise external funds for purchasing assets of distressed banks is weakened. Further, we examine the case when asset returns are correlated through securitization. In this case adverse shocks are felt uniformly across the banking sector and banks that survive with the help of a public liquidity backstop will become risk-averse and reluctant to purchase distressed assets. Finally, we explore a government funded asset purchase program, that is implemented with an asset price target.     

LM tests of spatial dependence based on bootstrap critical values

To test the existence of spatial dependence in an econometric model, a convenient test is the Lagrange Multiplier (LM) test. However, evidence shows that, in finite samples, the LM test referring to asymptotic critical values may suffer from the problems of size distortion and low power, which become worse with a denser spatial weight matrix. In this paper, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values of the LM statistics. Conditions for their validity are clearly laid out and formal justifications are given in general, and in detail under several popular spatial LM tests using Edgeworth expansions. Monte Carlo results show that when the conditions are not fully met, bootstrap may lead to unstable critical values that change significantly with the alternative, whereas when all conditions are met, bootstrap critical values are very stable, approximate much better the finite sample critical values than those based on asymptotics, and lead to significantly improved size and power. The methods are further demonstrated using more general spatial LM tests, in connection with local misspecification and unknown heteroskedasticity.     

低碳目标下城市产业结构调整与空间结构优化的协调——以杭州为例

以低能耗、低污染、低排放为特征的低碳经济发展模式成为城市发展转型的方向.低碳目标下城市发展方式转型的关键在于产业结构调整和空间结构优化的协调.文章以杭州为例分析了低碳城市建设中二者协调的经验和问题,并得出启示和政策建议.