基于大数据的碳价预测

为了研究大数据是否能够帮助我们预测碳排放权价格,本文讨论了结构化数据和非结构化信息对预测碳价所起的作用。结构化数据选取了国际碳现货价格、碳期货价格和汇率,非结构化信息选择百度搜索指数和媒体指数。考虑到当解释变量很多时,平等对待每一个解释变量是不合理的,所以提出了网络结构自回归分布滞后（ADL）模型,在参数估计和变量选择的同时兼顾了解释变量之间的网络关系。实证分析表明,网络结构ADL模型明显优于其他模型,可以获得较高的预测准确性,更适合基于大数据的预测。     

人力资本、要素价格与配置效率

要素相对价格左右着要素配置效率,是影响TFP的重要因素,本文将劳动要素细分为普通劳动与人力资本的基础上,运用随机前沿超越对数生产函数模型估计31个省市TFP变动,据以观察要素配置效率对TFP的影响,模拟要素价格在其中的作用。主要结论如下：(1)2003年以来要素配置效率呈负增长,是TFP增长率下降的最主要原因;(2)东部TFP增长率高于中西部且差距趋于扩大,与此相伴随的是东西部之间要素配置效率差异以更快的速度趋于扩大,进一步表明要素配置效率对TFP增长的制约;(3)要素价格是影响要素配置效率的直接原因,要素价格的扭曲在现阶段总体上表现为物质资本要素价格相对偏高;(4)分别模拟物质资本价格下降、人力资本和普通劳动价格上升,结果显示均改善了要素配置效率并相应地实现TFP增长,其中物质资本价格下降起着最显著的改善作用。为此,要重视市场环境建设并让市场在资源配置中起决定作用,从要素配置效率上推动TFP增长。     

国际油价波动对中国经济影响的一般均衡分析

本文构建动态可计算一般均衡模型来定量研究国际石油价格上涨对我国经济的影响。研究结果表明,国际石油价格上升不利于我国实际GDP、投资、居民收入和进出口等主要经济指标;技术进步是抵消国际油价上升的重要工具。最后,本文根据数量结果提出了一些政策建议。     

Modeling Electricity Price Using A Threshold Conditional Autoregressive Geometric Process Jump Model

Electricity market prices are highly volatile and often have high spikes. Both government authorities and market participants require sophisticated models and techniques for forecasting future prices and managing relevant financial risks in such a volatile market. This article extends the conditional autoregressive geometric process (CARGP) model (Chan et al., 2012 Chan, J. S.K., Lam, C. P.Y., Yu, P. L.H., Choy, S. T.B., Chen, C. W.S. (2012). A Bayesian conditional autoregressive geometric process model for range data. Computat. Statist. Data Anal. 56:30063019.[Crossref], [Web of Science ®] , [Google Scholar]) to the CARGP model with thresholds and jumps, which is abbreviated as CARGP-TJ model in this article. We will demonstrate that the proposed CARGP-TJ model not only captures the unique features of the electricity price but also performs better than other existing models. For robustness consideration, a heavy-tailed error distribution is adopted. Model implementation relies on the powerful Bayesian Markov chain Monte Carlo simulation techniques via WinBUGS software. The analysis of the daily maximum electricity prices of the New South Wales, Australia reveals that the proposed CARGP-TJ model captures the price spikes well for both in-sample estimation and out-of-sample forecast.     

ICP与CPI关系比较和整合研究

广义上看,全球国际比较项目（ICP）和消费者价格指数（CPI）都属于价格统计的范畴。比较分析发现,虽然二者在理论基础、测度目标等方面存在差异,但在原始数据类型、指标生成过程、编制方法和原则等方面相同或非常相近,为二者关系的协调和整合提供了理论支撑。在此基础上,探讨了ICP与CPI整合的总体思路,从基本、扩展和系统三个层面研究二者整合的方法,并提出若干政策建议。     

特有冲击、共同冲击与通货膨胀持续性

中国分类价格指数的通货膨胀持续性有何特征？如何理解分类价格指数和加总价格指数通货膨胀持续性之间的关系？我们对2001年1月至2011年12月的数据进行实证分析,结果表明分类价格指数比较明显的表现出行业间异质的通货膨胀波动性和通货膨胀持续性,而与加总价格指数相比,它的通货膨胀波动性更大而通货膨胀持续性更低。通货膨胀持续性的这些现象可以从特有冲击和共同冲击的角度加以理解。共同冲击的相对重要性存在差异解释了分类价格指数的通货膨胀持续性呈现行业间异质性;特有冲击的重要性在数据加总时被削弱,解释了分类价格指数具有比加总价格指数更低的通货膨胀持续性。     

The Bias in Price Elasticity Estimates Under Homothetic Separability: Implications for Analysis of Peak-Load Electricity Pricing

This article demonstrates that the assumption of a homothetically separable utility function places a priori restrictions on the parameters of the demand system. If these restrictions are unwarranted, an open question if they are not explicitly tested, they will lead to biased price elasticity estimates. In particular, we show that the uncompensated own-price elasticities must be smaller than the negative of the expenditure shares; that is, the price elasticity of peak electricity demand must be less than the negative of the share of expenditure devoted to peak electricity. This finding is probably not new to economists familiar with consumer demand analysis. Nevertheless, many recent studies of consumer demand for electricity under time-of-day rates explicitly impose this restriction. The resulting price elasticity estimates are usually quite large in absolute value (.5 to .8); but they are the product of restrictive a priori assumptions as well as information embodied in the sample data. The results of two analyses of time-of-day experiments, where the researchers imposed the untested assumption of homothetic separability, are examined more closely. We find that the reported price elasticities are strongly influenced by that a priori assumption. A Monte Carlo experiment demonstrates that using this model will lead to the reported price elasticities even if the consumption data are perfectly random with respect to price.     

Nonresponse Assessment of a Consumer Price Index

A household budget survey often suffers from a high nonresponse rate and a selective response. The bias that may be introduced in the estimation of budget shares because of this nonresponse can affect the estimate of a consumer price index, which is a weighted sum of partial price index numbers (weighted with the estimated budget shares). The bias is especially important when related to the standard error of the estimate. Because of the impossibility of subsampling nonrespondents to the budget survey, no exact information on the bias can be obtained. To evaluate the nonresponse bias, bounds for this bias are calculated using linear programming methods for several assumptions. The impact on a price index of a high nonresponse rate among people with a high income can also be assessed by using the elasticity with respect to total expenditure. Attention is also given to the possible nonresponse bias in a time series of price index numbers. The possible nonresponse bias is much larger than the standard error of the estimate.     

房价波动对我国城镇居民消费的影响研究

 本文将房价、住房面积、消费习惯及借贷约束等变量引入消费者最优选择模型中,构建出在综合考虑各个因素的条件下,能够检验房价波动对居民消费影响的动态面板模型,运用动态系统广义矩阵方法,采用我国29个省市的年度数据进行多角度的实证分析。研究显示:(1)我国城镇居民受到较强消费习惯与收入敏感性的影响;(2)房价波动对居住消费的影响为负,对非居住消费的影响为正,且均存在明显的非对称性;(3)中东西地区房价波动对居住消费的影响均为负,但影响系数差异较大;对非居住消费的影响差异就更为明显,东西部地区影响为正,中部地区为负。分析也表明,我国城镇高房价收入比、地区经济发展不平衡及居民收入差距的不断拉大是导致实证结果的主要原因。     

Weighting Elementary Prices in Consumer Price Index Construction Using Spatial Autocorrelation

The Consumer Price Indexes (CPI) are used in current economic systems to measure inflation. When constructing CPIs, however, official institutions have systematically overlooked the spatial dimension of elementary prices. Ignoring the fact that prices are collected at geographical locations implicitly implies considering prices as spatially independent, when in fact they are not. To solve this problem, this article proposes to weight basic price data by taking into account the spatial correlation they display. The weighted geometric and arithmetic means suggested generalize and improve the simple geometric and arithmetic means currently in use.