A new alternative quantile regression model for the bounded response with educational measurements applications of OECD countries

This article introduces a new distribution with two tuning parameters specified on the unit interval. It follows from a ‘hyperbolic secant transformation’ of a random variable following the Weibull distribution. The lack of research on the prospect of hyperbolic transformations providing flexible distributions over the unit interval is a motivation for the study. The main distributional structural properties of the new distribution are established. The different estimation methods and two simulation works have been derived for model parameters. Subsequently, we develop a related quantile regression model for further statistical perspectives. We consider two real data applications based on the educational measurements of both OECD and some non-members of OECD countries. Our regression model aims to relate the desire to get top grades on certain young students in the OECD countries with some of their Education and School Life Index such as reading performance, work environment at home, and paid work experience. It is shown that the elaborated quantile regression model has a better fitting power than famous regression models when the unit response variable possesses skewed distribution as well as two independent variables are significant in the statistical sense at any standard significance level for the median response.     

Graphical Network Models for International Financial Flows

The late-2000s financial crisis stressed the need to understand the world financial system as a network of countries, where cross-border financial linkages play a fundamental role in the spread of systemic risks. Financial network models, which take into account the complex interrelationships between countries, seem to be an appropriate tool in this context. To improve the statistical performance of financial network models, we propose to generate them by means of multivariate graphical models. We then introduce Bayesian graphical models, which can take model uncertainty into account, and dynamic Bayesian graphical models, which provide a convenient framework to model temporal cross-border data, decomposing the model into autoregressive and contemporaneous networks. The article shows how the application of the proposed models to the Bank of International Settlements locational banking statistics allows the identification of four distinct groups of countries, that can be considered central in systemic risk contagion.     

OECD数字经济核算研究最新动态及其启示

本文主要概览了OECD数字经济核算研究的最新动态,探讨了数字经济内涵界定、核算内容及方法和数据来源三方面核算问题。首先重点梳理了OECD基于包容性视角、核算视角和初步测算视角的内涵界定及特征分析,然后综述了OECD数字经济核算的主要研究议题及方法,包括增加值及总产出核算、价格和物量核算、消费品资本化核算、“免费”经济核算、数字平台中介服务的流量核算等内容,接着以最具典型性的分享经济为例探讨了目前OECD国家官方统计机构公布的关于数据来源的统计实务,并针对最为广泛使用的税务机关数据和抽样调查数据,分别对美国、加拿大和英国公布的官方统计现状进行了更为深入的阐述。最后,结合数字经济的内涵界定、特征分析、核算方法和数据来源,本文为我国数字经济的核算工作提供了全面统计、动态监测、优化方法和挖掘来源等重要启示。     

要素价格与中国产业国际竞争力：基于ISIC的跨国比较

 基于ISIC两位数分类的分析表明,中国制造业对于OECD国家相对实际竞争力水平具有明显的行业差异。纺织服装、造纸等低技术产业竞争力较强,中低技术、中高技术行业的相对实际竞争力较低。生产要素的价格变动对中国产业国际竞争力具有较大的影响,2000-2008年中国与27个OECD国家面板数据的实证分析表明,原材料购进价格的相对提高对中国产业国际竞争力的影响明显带有行业差异性,低技术产业以及高技术产业这两大类产业竞争力受原材料价格提高影响更为明显,而中低和中高技术产业则几乎不受影响;能源相对价格的提高对除了炼焦和精炼石油制品以外的其他几乎所有行业竞争力带来负面影响;劳动力相对价格的提高也大大降低了中国产业的竞争力,低技术产业受其影响最为明显。从长期来看,技术能力的提高才是中国产业国际竞争力提高的根本途径。     

高铁开通促进了农业全要素生产率增长吗?——来自长三角地区准自然实验的经验证据

本研究将高铁开通作为一项"准自然实验",基于2005-2017年长三角地区25个地级及以上城市的面板数据,采用倍差法并借助匹配倍差法和安慰剂测试等稳健性检验工具,对高铁开通的农业全要素生产率增长效应进行实证检验。研究发现:高铁开通在短期和长期都对沿线城市农业全要素生产率增长具有显著的促进作用;将农业全要素生产率分解为技术进步和技术效率后发现,高铁开通对农业全要素生产率的增长效应主要表征为对农业技术进步的推动而非对农业技术效率的改善;高铁开通有助于强化城镇化对农业全要素生产率增长的促进作用以及产业结构对农业全要素生产率增长的反哺作用。在我国高铁建设纵深推进和"乡村振兴战略"全面实施的背景下,应将高铁建设纳入推进乡村振兴战略和促进农业高质量发展的有效手段和政策工具,借助高铁开通的杠杆效应进一步发挥好城镇化、产业结构升级对农业全要素生产率的带动作用。     

Technology spillover and regional convergence process: a statistical analysis of the Italian case

Recent literature broadly highlight the importance of modelling technological innovation effects on economic growth. This paper develops a methodology that allows to measure technology contribution to economic convergence; the choice of a regional framework also allows to underline interregional knowledge transmission as a the major channel of technological progress. Moreover, the specification of a dynamic growth model enables to evaluate both the regional convergence and the effect of innovation on long-run labour productivity without resorting to any technology index measurement. We contribute to the methodological literature also by comparing different dynamic panel data estimation procedures and by detecting both the presence of small sample bias and the existence of a nearly unit root autoregressive process in labour productivity series. The results of an empirical analysis on Italian regions show how most of innovation resources derives from relevant spillover mechanisms. Furthermore, technology spillover intensity seems to be strongly affected by geography and productive structure of regions.Financial support by Fondazione Cassa di Risparmio di Rimini and Uniturim S.p.A. is gratefully acknowledged.     

OECD国家能源消费、经济增长与碳排放关系研究

基于面板数据模型,分析1971--2006年间28个OECD国家能源消费、经济增长和碳排放之间的关系,再根据面板数据的单位根检验和协整性检验,确定三者之间存在长期的均衡关系。通过固定影响变系数模型的估计发现：碳排放与能源消费呈显著的正相关关系,各国碳排放的能源消费弹性系数存在明显的差异;从长期看,部分国家碳排放与GDP的关系呈现倒“U”形,部分国家呈“U”形。     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.     

Bayesian Dynamic Factor Models and Portfolio Allocation

We discuss the development of dynamic factor models for multivariate financial time series, and the incorporation of stochastic volatility components for latent factor processes. Bayesian inference and computation is developed and explored in a study of the dynamic factor structure of daily spot exchange rates for a selection of international currencies. The models are direct generalizations of univariate stochastic volatility models and represent specific varieties of models recently discussed in the growing multivariate stochastic volatility literature. We discuss model fitting based on retrospective data and sequential analysis for forward filtering and short-term forecasting. Analyses are compared with results from the much simpler method of dynamic variance-matrix discounting that, for over a decade, has been a standard approach in applied financial econometrics. We study these models in analysis, forecasting, and sequential portfolio allocation for a selected set of international exchange-rate-return time series. Our goals are to understand a range of modeling questions arising in using these factor models and to explore empirical performance in portfolio construction relative to discount approaches. We report on our experiences and conclude with comments about the practical utility of structured factor models and on future potential model extensions.     

部分国家季度支出法国内生产总值核算方法与数据发布情况及启示

本文研究分析并归纳了经济合作与发展组织（OECD）34个成员国及巴西、俄罗斯、印度和南非等4个金砖国家季度支出法GDP核算的主要方法、资料来源及数据发布情况,提出要进一步提高基础数据质量,加快正式建立我国季度支出法GDP核算制度。     

Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility

In this article, we develop a mixed frequency dynamic factor model in which the disturbances of both the latent common factor and of the idiosyncratic components have time-varying stochastic volatilities. We use the model to investigate business cycle dynamics in the euro area and present three sets of empirical results. First, we evaluate the impact of macroeconomic releases on point and density forecast accuracy and on the width of forecast intervals. Second, we show how our setup allows to make a probabilistic assessment of the contribution of releases to forecast revisions. Third, we examine point and density out of sample forecast accuracy. We find that introducing stochastic volatility in the model contributes to an improvement in both point and density forecast accuracy. Supplementary materials for this article are available online.     

Forecasting the industrial production using alternative factor models and business survey data

This paper compares the forecasting performance of three alternative factor models based on business survey data for the industrial production in Italy. The first model uses static principal component analysis, while the other two apply dynamic principal component analysis in frequency domain and subspace algorithms for state-space representation, respectively. Once the factors are extracted from the business survey data, then they are included into a single equation to predict the industrial production index. The forecast results show that the three factor models have a better performance than that of a simple autoregressive benchmark model regardless of the specification and estimation methods. Furthermore, the state-space model yields superior forecasts amongst the factor models.     

Conditional time-dependent nonparametric estimators with an application to healthcare production function

ABSTRACT

By using the probabilistic framework of production efficiency, the paper develops time-dependent conditional efficiency estimators performing a non-parametric frontier analysis. Specifically, by applying both full and quantile (robust) time-dependent conditional estimators, it models the dynamic effect of health expenditure on countries’ technological change and technological catch-up levels. The results from the application reveal that the effect of per capita health expenditure on countries’ technological change and technological catch-up is nonlinear and is subject to countries’ specific income levels.     

A Bayesian panel data framework for examining the economic growth convergence hypothesis: do the G7 countries converge?

In this paper, we suggest a Bayesian panel (longitudinal) data approach to test for the economic growth convergence hypothesis. This approach can control for possible effects of initial income conditions, observed covariates and cross-sectional correlation of unobserved common error terms on inference procedures about the unit root hypothesis based on panel data dynamic models. Ignoring these effects can lead to spurious evidence supporting economic growth divergence. The application of our suggested approach to real gross domestic product panel data of the G7 countries indicates that the economic growth convergence hypothesis is supported by the data. Our empirical analysis shows that evidence of economic growth divergence for the G7 countries can be attributed to not accounting for the presence of exogenous covariates in the model.     

Regression models for time-varying extremes

A common approach to modelling extreme data are to consider the distribution of the exceedance value over a high threshold. This approach is based on the distribution of excess, which follows the generalized Pareto distribution (GPD) and has shown to be adequate for this type of situation. As with all data involving analysis in time, excesses above a threshold may also vary and suffer from the influence of covariates. Thus, the GPD distribution can be modelled by entering the presence of these factors. This paper presents a new model for extreme values, where GPD parameters are written on the basis of a dynamic regression model. The estimation of the model parameters is made under the Bayesian paradigm, with sampling points via MCMC. As with environmental data, behaviour data are related to other factors such as time and covariates such as latitude and distance from the sea. Simulation studies have shown the efficiency and identifiability of the model, and applying real rain data from the state of Piaui, Brazil, shows the advantage in predicting and interpreting the model against other similar models proposed in the literature.     

Testing the Significance of Categorical Predictor Variables in Nonparametric Regression Models

In this paper we propose a test for the significance of categorical predictors in nonparametric regression models. The test is fully data-driven and employs cross-validated smoothing parameter selection while the null distribution of the test is obtained via bootstrapping. The proposed approach allows applied researchers to test hypotheses concerning categorical variables in a fully nonparametric and robust framework, thereby deflecting potential criticism that a particular finding is driven by an arbitrary parametric specification. Simulations reveal that the test performs well, having significantly better power than a conventional frequency-based nonparametric test. The test is applied to determine whether OECD and non-OECD countries follow the same growth rate model or not. Our test suggests that OECD and non-OECD countries follow different growth rate models, while the tests based on a popular parametric specification and the conventional frequency-based nonparametric estimation method fail to detect any significant difference.     

Bayesian updating of atmospheric dispersion after a nuclear accident

Summary.  We consider a Bayesian forecasting system to predict the dispersal of contamination on a large scale grid in the event of an accidental release of radioactivity. The statistical model is built on a physical model for atmospheric dispersion and transport called MATCH. Our spatiotemporal model is a dynamic linear model where the state parameters are the (essentially, deterministic) predictions of MATCH; the distributions of these are updated sequentially in the light of monitoring data. One of the distinguishing features of the model is that the number of these parameters is very large (typically several hundreds of thousands) and we discuss practical issues arising in its implementation as a realtime model. Our procedures have been checked against a variational approach which is used widely in the atmospheric sciences. The results of the model are applied to test data from a tracer experiment.     

Relative risk analysis of dengue cases using convolution extended into spatio-temporal model

Dengue Hemmorage Fever (DHF) cases have become a serious problem every year in tropical countries such as Indonesia. Understanding the dynamic spread of the disease is essential in order to find an effective strategy in controlling its spread. In this study, a convolution (Poisson-lognormal) model that integrates both uncorrelated and correlated random effects was developed. A spatial–temporal convolution model to accomodate both spatial and temporal variations of the disease spread dynamics was considered. The model was applied to the DHF cases in the city of Kendari, Indonesia. DHF data for 10 districts during the period 2007–2010 were collected from the health services. The data of rainfall and population density were obtained from the local offices in Kendari. The numerical experiments indicated that both the rainfall and the population density played an important role in the increasing DHF cases in the city of Kendari. The result suggested that DHF cases mostly occured in January, the wet session with high rainfall, and in Kadia, the densest district in the city. As people in the city have high mobility while dengue mosquitoes tend to stay localized in their area, the best intervention is in January and in the district of Kadia.     

The independent factor analysis approach to latent variable modelling

Independent factor analysis (IFA) has recently been proposed in the signal processing literature as a way to model a set of observed variables through linear combinations of latent independent variables and a noise term. A peculiarity of the method is that it defines a probability density function for the latent variables by mixtures of Gaussians. The aim of this paper is to cast the method into a more rigorous statistical framework and to propose some developments. In the first part, we present the IFA model in its population version, address identifiability issues and draw some parallels between the IFA model and the ordinary factor analysis (FA) one. Then we show that the IFA model may be reinterpreted as an independent component analysis-based rotation of an ordinary FA solution. We also give evidence that the IFA model represents a special case of mixture of factor analysers. In the second part, we address inferential issues, also deriving the standard errors for the model parameter estimates and providing model selection criteria. Finally, we present some empirical results on real data sets.     

Bayesian Analysis of DSGE Models

This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the non-linear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models and a DSGE model that was solved with a second-order perturbation method.