行政区划调整对住宅价格变化的影响效应分析——以南汇并入浦东新区为例

行政区划的调整往往是为了适应城市化和区域经济一体化的要求而进行的。除经济和生态环境因素外,住宅价格还受社会、文化和政治等因素的影响和作用,但行政因素对住宅价格变化影响的实证研究并不多见。以上海市南汇区并入浦东新区为例,运用Hedonic模型、多元线性回归分析、半弹性系数等方法,从时间和空间两个方面,定量测度行政区划调整对住宅市场价格变化的影响程度。结果表明:从时间变化上看,行政区划调整以后,浦东新区各板块的住宅均价呈现上涨趋势,住宅价格比调整之前上涨2.74个百分点,且前阶段的涨幅较之后要大;从空间变化上看,外郊环、郊环外涨幅潜力更大。总体而言,行政区划调整对区域住宅价格变化有一定的正面影响,但其影响程度有限且不及区位、交通和住宅特征等因素显著。因此,地方政府和房地产开发商应当理性对待行政区划调整策略,不能过于奢望通过短期的行政区划调整来提升房地产价值,而应当更多地关注开发区位的选址及区域交通和环境条件的改善,提升物业管理水平与服务质量,完善社区公共服务配套设施建设。     

Technology level evaluation methodology based on the technology growth curve

Technology has been the driving force of development for knowledge-based economies. As competition in technology innovation among nations becomes more intense, there is a growing need for improved judgment, evaluation and prediction of scientific technology capacity in order to enhance national competitiveness. Until now, a country's technology level has been evaluated on a relative basis by comparing it with that of the country with the world's best technology. However, this kind of static methodology makes the interpretation of results unclear and makes time series analysis difficult. One of the most important limitations of this methodology is that it cannot be used to establish a strategy to improve the technology level. This paper examines the methodological problems of technology level evaluation and develops a dynamic methodology by applying the technology growth curve model. We also analyze the real technology level by using a new model application and review the relevance of this method. Finally, we discuss how to use the results in order to create a dynamic technology strategy.     

Where have all the flowers gone? Forecasting green trends in the automobile industry with a choice-based conjoint adoption model

With rising gas prices, global warming, and green thinking, all-electric vehicles are currently considered the automobile technology of the future. However, besides their advantages electric drive trains also exhibit several disadvantages. Moreover, history shows several failed attempts to establish electric vehicles. Thus, a reliable forecasting model is needed that predicts if the current trend is sustainable. We develop and empirically test a choice-based conjoint adoption model that uses individual-level preferences as a basis for prediction. Predictions are mapped to the time of the next planned purchase in order to establish the adoption process. The model extends existing research in several ways. First, no prior information, e.g., historical market data or a functional form of the adoption process, has to be integrated. Second, the model allows dynamic modifications of product specifications or competition at different points in time. Third, a no-choice option can be integrated so that a technology switch is not forced by the model itself and switching costs can be considered. The empirical results reveal different critical factors for the adoption of all-electric vehicles, such as purchase price, range, timing of the market entry, or environmental evolution, which could lead to a solid base of consumers preferring this option.     

On the forecasting of the challenging world future scenarios

Logistic and power law methodologies for both retrospective and prospective analyses of extended time series describing evolutionary growth processes, in environments with finite resources, are confronted. While power laws may eventually apply only to the early stages of said growth process, the Allee logistic model seems applicable over the entire span of a long range process. On applying the Allee logistic model to both the world population and the world gross domestic product time series, from 1 to 2008 AD, a projection was obtained that along the next few decades the world should experience a new economic boom phase with the world GDP peaking around the year 2020 and proceeding from then on towards a saturation value of about 142 trillion international dollars, while the world population should reach 8.9 billion people by 2050. These results were then used to forecast the behavior of the supply and consumption of energy and food, two of the main commodities that drive the world system. Our findings suggest that unless the currently prevailing focus on economic growth is changed into that of sustainable prosperity, human society may run into a period of serious economical and social struggles with unpredictable political consequences.     

江西省产业部门碳排放特征及减排途径——基于1992-2007年投入产出分析

根据江西省1992—2007年投入产出表,运用投入产出模型,对江西省产业部门直接碳排放强度和完全碳排放强度进行测算和分析。结果表明：产业部门的直接碳排放强度和完全碳排放强度的排序不一致,产业部门的向后关联效应所引发的隐含碳排放影响极大。然后,根据直接碳排放强度和完全碳排放强度对产业部门进行分类,将产业部门划分为高碳排放部门、隐含高碳排放部门和低碳排放部门,并对不同类型产业部门碳排放特征进行分析。在此基础上提出控制高碳排放部门的能源消耗,优化隐含高碳排放部门的中间投入以及合理协调发展低碳排放产业的政策建议,以期为江西省节能减排政策的制定提供决策参考。     

我国财政支出对经济增长非线性效应——基于省级面板数据的平滑转移模型实证分析

文章基于我国1997-2009年省级面板数据,采用数据本身隐含信息进行内分组的面板平滑转移模型来检验财政支出与经济增长之间的非线性效应,并进一步拓展了Armey曲线推论。研究发现,我国省级政府财政一般预算支出规模和财政支出结构均与经济增长之间存在Armey曲线所描绘的非线性效应,其中财政一般预算支出最优规模为9.32%,财政支出最优结构为1.643,并且财政一般预算支出规模与财政支出结构都在最优值两侧对经济增长的影响具有不对称性,相比而言,财政一般预算支出规模的转换速度似乎更快。这意味着现阶段我国财政一般预算支出规模与财政支出结构不合理,需要政府提高财政支出效率。     

香港和内地证券市场的动态竞争关系研究——基于A+H交叉上市公司的实证分析

交叉上市引起不同证券市场对有限投资者和资金资源的竞争。文章在阐述证券市场竞争的Lotka-Volterra模型基础上,通过2006-2010年38家A+H交叉上市企业的日交易数据实证分析了香港和内地证券市场之间的动态竞争过程。结果表明,两市场之间的动态竞争关系从交叉上市初期的捕食—诱饵关系逐步演化为目前的竞争互惠关系,且这种动态演化过程与企业交叉上市顺序没有显著的相关性。这说明内地证券市场正在不断完善,鼓励企业境外上市和A股回归将有利于内地证券市场的发展,而且互惠合作应是未来证券市场竞争的发展趋势。     

国防支出与经济增长均衡关系的理论和实证研究——基于门槛回归模型的检验分析

文章首先将国防支出纳入Solow增长模型进行理论分析,假定国防支出通过影响技术进步对经济增长产生影响。与现有实证分析不同,文章基于1952-2008年中国的有关时间序列数据,尝试运用非线性门槛回归模型来分析国防支出与经济增长之间的数量关系。研究发现:国防支出与经济增长之间存在门槛效应,国防支出占GDP的比例低于3.434%时,国防支出占GDP的比例的增加不利于经济增长,且这种负面作用较为显著;当该比例高于3.434%时,该比例的增加将显著促进经济增长。当然,该比例并非越低越好,也并非越高越好,其局部最优规模由次级门槛值决定。     

海峡两岸证券业经营效率比较研究:基于Metafrontier方法

本文将Metafrontier思想与BCC-DEA模型相结合,综合应用Metafrontier DEA方法、Malmquist全要素生产率指数和Tobit模型,客观地评价具有不同生产技术的两岸证券商的经营效率。结果表明:台湾证券商发展水平趋同,大陆证券商良莠不齐,但竞争力逐渐提升;台湾证券商技术水平落后于大陆;大陆全要素生产率逐渐改善,台湾逐渐衰退;资产总额、负债比、地区生产总值是影响技术效率的关键因素,负债比、经济自由度、地区生产总值是影响技术落差的关键因素;金融危机对两岸证券业冲击显著。     

商业银行信用风险与宏观经济——基于压力测试的研究

本文采用压力测试框架,研究了宏观经济波动对商业银行信用风险的影响。文章以不良贷款率度量信用风险,以名义GDP增长率、广义货币供应量（M2）增速、居民消费价格指数（CPI）以及房地产销售价格指数作为宏观经济变量,建立了合适的宏观压力测试模型。在GDP增速放缓、CPI上升、M2增速下降的压力情景下,预测了2011年第一季度到第四季度的不良贷款率的变化路径。实证结果表明在压力情景下商业银行的不良贷款率将会显著上升。