公共充电基础设施对电动汽车购买的异质性影响研究

交通部门是中国最重要的碳排放源之一,交通电动化对实现双碳目标至关重要。本文以中国纯电动汽车消费为例,采集2017年1月至2020年11月中国31个省份（因数据缺失,不包含港澳台地区）充电桩数量及电动汽车销量数据构建面板固定效应模型和门槛效益模型,实证分析了不同类型公共充电基础设施对不同用途纯电动汽车购买的影响,以及不同地区温度对销量影响的门槛效应。研究发现公共充电桩数量增加1%,纯电动汽车购买量显著增加0.85%,其中出租租赁用途纯电动汽车销量显著增加1.57%。直流公共充电桩对纯电动汽车购买的影响远大于交流公共充电桩,并对不同用途的电动汽车存在异质性影响。此外,在气温更低的地方公共充电桩对纯电动汽车购买的影响更大,其显著门槛值为15摄氏度,且气温对出租租赁汽车的影响比非营业用途更大。政策制定者应合理考虑公共充电桩等基础设施的增设布局以保障电动汽车的续航能力,从而促进交通运输部门的脱碳进程。     

应用期权方法构建商业银行贷款利率模型

以分析公司普通股所具有的看涨期权特性为出发点，以Black—Scholes看涨期权定价模型为基础，根据不同企业资本结构和资产回报风险的差别，构造了一个商业银行基本贷款利率模型。从期权角度来设计贷款利率模型，除考虑了企业的经营风险因责外，还同时考虑了企业融资结构所带来的财务风险，能更加全面地反映出商业银行贷款的保障程度。     

Empirical Bayes analysis of log-linear models for a generalized finite stationary Markov chain

This article presents the empirical Bayes method for estimation of the transition probabilities of a generalized finite stationary Markov chain whose ith state is a multi-way contingency table. We use a log-linear model to describe the relationship between factors in each state. The prior knowledge about the main effects and interactions will be described by a conjugate prior. Following the Bayesian paradigm, the Bayes and empirical Bayes estimators relative to various loss functions are obtained. These procedures are illustrated by a real example. Finally, asymptotic normality of the empirical Bayes estimators are established.     

The effect of inflation uncertainty on inflation: Stochastic volatility in mean model within a dynamic framework

This paper investigates the effect of inflation uncertainty innovations on inflation over time by considering the monthly United States data for the time period 1976–2006. In order to investigate the effect of inflation uncertainty innovation on inflation, a Stochastic Volatility in Mean model (SVM) has been employed. SVM models are generally used to capture the innovation to inflation uncertainty, which cannot be achieved in the framework of popular deterministic ARCH type of models. Empirical evidence provided here suggests that innovations in inflation volatility increases inflation persistently. This evidence is robust across various definitions of inflation and different sub-periods.     

Extending the BG/NBD: A simple model of purchases and complaints

Extant customer-base models like the beta geometric/negative binomial distribution (BG/NBD) predict future purchasing based on customers' observed purchase history. We extend the BG/NBD by adding an important non-transactional element that also drives future purchases: complaint history. Our model retains several desirable properties of the BG/NBD: it can be implemented in readily available software, and estimation requires only customer-specific statistics, rather than detailed transaction-sequence data. The likelihood function is closed-form, and managerially relevant metrics are obtained by drawing from beta and gamma densities and transforming these draws to a sample average. Based on more than two years of individual-level data from a major U.S. internet and catalog retailer, our model with complaints outperforms both the original BG/NBD and a modified version. Even though complaints are rare and non-transactional events, they lead to different substantive insights about customer purchasing and drop-out: customers purchase faster but also drop out much faster. Furthermore, there is more heterogeneity in drop-out rates following a purchase than a complaint.     

Polytomous logistic regression

In this paper a review will be given of some methods available for modelling relationships between categorical response variables and explanatory variables. These methods are all classed under the name polytomous logistic regression (PLR). Models for PLR will be presented and compared; model parameters will be tested and estimated by weighted least squares and by likelihood. Usually, software is needed for computation, and available statistical software is reported.
An industrial problem is solved to some extent as an example to illustrate the use of PLR. The paper is concluded by a discussion on the various PLR-methods and some topics that need a further study are mentioned.     

Assessing the economic impact of entrepreneurship on a regional economy using social accounting matrices: the case of Andalusia

This article analyses the impact that entrepreneurial activity has, from the economic point of view, on a regional economy (Andalusia), based on a Social Accounting Matrix linear model. Moreover, to measure entrepreneurship at regional level, it is defined what can be considered an entrepreneurial initiative company from a quantitative point of view. The results obtained, in terms of Production, GDP and job creation, show how entrepreneurship, in the case of Andalusia, contributes to the sustainability of the economy, its growth and, above all, the reduction of unemployment in the short term.     

Asymptotic confidence bands for the estimated autocovariance and autocorrelation functions of vector autoregressive models

This paper provides closed-form formulae for computing the asymptotic covariance matrices of the estimated autocovariance and autocorrelation functions of stable VAR models by means of the delta method. These covariance matrices can be used to construct asymptotic confidence bands for the estimated autocovariance and autocorrelation functions to assess the underlying estimation uncertainty. The usefulness of the formulae for empirical work is illustrated by an application to inflation and output gap data for the U.S. economy indicating the existence of a significant short-run Phillips-curve tradeoff.First version received: November 2002/Final version received: September 2003