基于BP神经网络技术的GDP预测方法

在一般的经济预测中,通常采用回归分析方法建立模型对GDP进行拟合,但由于经济系统的复杂性,许多回归模型尤其是线性回归模型难以达到要求的精度。本文采用BP神经网络方法建模,利用其自学习和非线性的突出特点,对GDP进行研究,并得到了对2009年我GDP较为满意的预测结果。     

山东省能源消费碳排放预测

基于《IPCC国家温室气体清单指南》估算了17种能源的碳排放系数,测算了1995—2009年山东省的能源消费碳排放量。选取人口、城镇化率、人均GDP、工业GDP比例、能源消耗强度作为山东省碳排放的影响因素,分别应用偏最小二乘回归和岭回归构建了预测山东省能源消费碳排放的STIRPAT模型。通过对比基于两种回归所得模型的拟合效果,最终选择岭回归模型预测了2010—2015年山东省能源消费碳排放量。最后提出了山东省碳减排的相关对策。     

基于EM算法的高斯混合模型的聚类分析——以2015年各省份GDP为例

利用高斯混合模型对2015年31个省、(市、区)的GDP和人均GDP进行聚类分析,根据贝叶斯信息准则选取最优类数,运用EM算法估计出模型的参数,用训练好的模型判断样本所属的类别,分类效果良好,证实了模型的实用性和可靠性.     

基于灰色系统模型的城乡建设用地规模预测——以常德市鼎城区为例

土地利用总体规划是区域性的综合规划,为地方性的经济发展提供科学和法律依据.建设用地的有效供给是地方经济发展基础,建设用地需求量预测是土地利用总体规划编制的核心,也是土地利用管理的依据.为了切实搞好常德市鼎城区的土地利用总体规划,使之更具有前瞻性、可操作性,必须切合实际地搞好建设用地需求规模和布局的预测.采用灰色系统模型,通过灰色关联度分析,建设用地发展与GDP、总人口、市镇人口、社会固定资产投资、人均GDP、第二产业产值、第三产业产值密切相关.基于这些因子,采用灰色系统模型法、建立GM(1,1)灰色模型,进行了2010年和2020年的城乡建设用地规模的预测.GM(1,1)模型可以进行长期预测,但预测仅反映一种趋势,综合考虑驱动因素的灰色线性组合模型更具有预测的可靠性.利用GM(1,1)模型和线性模型组合,进行城乡建设用地规模预测,可为土地利用总体规划提供可靠的保证.     

基于HP滤波法的我国人均GDP增长速度周期波动研究

本文通过对1990年至2015年剔除价格影响因素的我国人均GDP增长速度的分析,利用HP滤波法分解出我国人均GDP增长速度的循环因素,并在此基础上表征人均GDP增速周期与我国经济、政策、文化的关联,发现我国的经济政策和世界市场经济的波动对我国人均GDP增速有重大影响.建议我国在制定经济政策时能从长远出发、全面衡量利弊,在应对国际市场经济波动方面能提早预测、准备更加充分.     

基于时间序列分析的黔西南州经济增长分析与预测--基于1978-2007年的黔西南州宏观经济数据

时间序列的预测方法有着广泛的应用背景,在解决经济发展,金融市场动态,气象预报和水文预报等领域的预测问题时,都可以利用时间序列的预测方法。结合黔西南的实际州情,本文采用EVIEWS统计软件及时间序列分析法对1978—2007年的黔西南州宏观经济数据进行了系统分析,构建了GDP序列的ARIMA模型,响应变量为GDP序列的ADL模型,对投资、消费对黔西南州经济增长的相互作用进行了实证研究,并预测黔西南州未来8年经济增长的变化趋势,相比之下组合模型在拟合和预测效果具有较高的可靠性、准确性和稳定性。通过对黔西南州的宏观经济动态模型ADL模型,我们掌握黔西南州的宏观经济系统的主要特征及其运行机制,GDP与投资、消费互相影响,互相促进;通过预测,我们了解和掌握了经济增长的变化趋势,能有效地对政策制定进行合理化建议。     

基于RBF神经网络的GDP时间序列预测模型研究

对GDP进行高精度的分析预测,对制定经济发展战略、规划年度计划以及各种宏观经济政策,具有重要的理论与现实意义.本文采用RBF神经网络作为工具,建立基于RBF神经网络的GDP时间序列预测模型,并与ARIMA模型进行对比,对上海市22年的GDP数据进行了仿真实验.实验表明,ARIMA模型对上海市GDP数据进行预测的精度仅为91.8754％,而本文提出的RBF_TSF模型的预测精度则高达95.0360％.这表明本文提出的RBF TSF比ARIMA模型在GDP时间序列预测上具有更高的预测精度.同时该模型收敛迅速,具有很强的实用价值.     

黑龙江省GDP预测的灰色模型

灰色预测方法是经济预测中一种非常有效的方法,它的主要特点是建立预测模型所需要的原始数据不多,容易采集,这种预测方法简便并具有较高的准确性。本文运用灰色理论,以2000～2006年黑龙江省GDP情况为原始数据,建立了黑龙江省GDP预测的灰色模型,并对2007～2010年黑龙江省GDP做了预测。     

新兴市场国家金融危机预警研究——基于Logit模型的实证检验

当前部分新兴市场国家的金融风险引起国际社会高度警惕,有必要研究建立适用于新兴市场国家的金融危机预警模型,以更有效地应对金融风险.印度是典型的新兴市场国家,且在经济金融领域和中国有较多相似性,研究印度金融危机预警,对新兴市场国家尤其是中国防范金融风险具有重要意义.文章基于印度数据构建了货币危机预警模型,对货币危机的影响因素进行实证分析,并结合当前宏观数据,利用模型预测印度在未来若干年内发生货币危机的概率.Logit模型分析表明,净储蓄/GDP下降、人均GDP下降、外债/GDP升高、出口/GDP下降,会导致印度货币危机发生的概率增加.进一步利用Logit模型预测表明印度从2020年起货币危机发生的概率将逐年上升.尽管如此,若应对措施及时有效,货币危机引发金融危机爆发的可能性依然较小.     

高精度灰色模型研究及2005年GDP总量预测

本文对灰色模型做了进一步的研究,拓广了灰色模型,建立了一个新的、预测精度较高的新灰色预测模型--"对数函数-幂函数变换"模型,并利用此模型对我国GDP总量做出精度较高的灰色预测.     

武汉城市圈工业化水平测度与分析

选取人均GDP、城镇化率、农业增加值占GDP比重、农业劳动力占全社会就业人员比重、第三产业增加值占GDP比重和恩格尔系数等六项指标作为衡量城市圈工业化水平的评价标准。武汉城市圈工业化水平测度的结论是：武汉城市圈整体上处于工业化中期起步阶段：武汉市处于工业化中期的加速阶段,具备向后期发展的潜力;黄石市、鄂州市和潜江市均处于工业化中期的起步阶段;孝感市、黄冈市、咸宁市、仙挑市和天门市均处于工业化初期阶段,仙桃具备向工业化中期阶段发展的条件。     

Inequality and economic growth: the role of initial income

We estimate a panel model where the relationship between inequality and GDP per capita growth depends on countries’ initial incomes. Estimates of the model show that the relationship between inequality and GDP per capita growth is significantly decreasing in countries’ initial incomes. Results from instrumental variables regressions show that in Low Income Countries transitional growth is boosted by greater income inequality. In High Income Countries inequality has a significant negative effect on transitional growth. For the median country in the world, that in the year 2015 had a PPP GDP per capita of around 10000USD, IV estimates predict that a 1 percentage point increase in the Gini coefficient decreases GDP per capita growth over a 5-year period by over 1 percentage point; the long-run effect on the level of GDP per capita is around ??5%.     

How Much Will It Cost to Join the Club? The Extra Costs of Approximating Lithuanian Environmental Laws with Those of the European Union

This paper presents estimates of costsassociated with approximating Lithuanianenvironmental protection legislation with thatof the European Union (EU). Lithuania is oneof twelve EU associate members in Central andEastern Europe that is currently preparing foraccession by approximating their legislationwith that of the EU. The costs of fifteen EUdirectives are considered and details are givenon four directives. Necessary investments maytotal Euro 1500 million by 2015 and the presentvalue of all annualized costs is expected to beabout Euro 2200 million. In 2015, annualized costsare estimated to be approximately Euro 500 million,but these costs are only part of the totalcosts of approximation. Non-environmentalcosts are not considered. Assuming a modestlyambitious average annual growth of GDP of 2.0%per year implies that approximation with thefifteen directives analyzed will cost roughly3.5% of GDP in 2015. This level of additional commitment to environmentalprotection is itself much higher than the 2.0%of GDP being spent on average by OECD countriesand suggests the possibility of a substantialeconomic burden on the Lithuanian economy. Public budgets and households are expected tocarry a substantial portion of this cost,because many directives are the responsibilityof national and local governments. Making theright choices that are expected to be part ofapproximation with the environmental acquis is likely to benefit from carefulcomparisons of costs and willingness to pay forthe environmental benefits of approximation.     

新加坡实施以知识为基础、创新为驱动的国家战略

本文描述了新加坡建设以知识为基础、创新为驱动国家所做的努力,尤其是受世界金融危机冲击的2010年间所出台的重大举措。在第四个科技五年计划结束、第五个科技五年计划即将启动的关键时刻,新加坡总结了前五年推动科技进步的成就,决定采取增加R＆D投入,到2015年把新加坡R＆D总支出提高到占GDP的3．5％等综合措施,并继续强力推行国家研究一创新一创业战略。     

Beyond GDP: an analysis of the socio-economic diversity of European regions

ABSTRACT

This paper aims to analyze the socioeconomic diversity of the European Union (EU-28) regions from a dynamic perspective. For that purpose, we combine a series of exploratory space-time analysis approaches to multiple Factor Analysis (MFA) applied to a large range of indicators collected at the NUTS-2 level for the period 2000–2015 for the EU-28. First, we find that the first factor of MFA, interpreted as economic development (ECO-DEV), is spatially clustered and that a moderate convergence process is at work between European regions from 2000 to 2015. Second, when comparing these results with those obtained for Gross Domestic Product (GDP) per capita, we show that the convergence pattern detected with GDP per capita is more pronounced: ECO-DEV adjusts slower over time compared to GDP per capita. Third, pictures provided by the remaining interesting factors, capturing educational attainment, population dynamics and employment, are very different.     

合肥城市生活垃圾产量预测研究

通过分析合肥市城市生活垃圾产量(1994—2005年)以及影响垃圾产量的主要相关因素,采用多元回归分析方法建立了垃圾产量预测模型。分析结果表明,国内生产总值、生活消费支出和住宅使用面积是影响垃圾产量的主要因素,且都是正相关关系。采用该模型对合肥市2006—2015年生活垃圾产量进行了预测,结果显示未来十年内合肥市生活垃圾产量继续缓慢增长,预计2015年垃圾产量将达到102.61万t,人均日产量为1.42kg。     

Global value chain and growth convergence: Applied especially to China

We propose a new convergence potential indicator for standard growth convergence regressions: the global value chain (GVC) position index, measured by the contribution of high-skilled labour in total domestic value added. Our convergence indicator can avoid the lagged dependent variable problem discussed in Barro (2015 and 2016) and deliver more accurate forecasts for China’s GDP growth than GDP per capita does. Using the GVC position index, we predict that China’s growth potential remains at 7–8% between 2010 and 2015, while the predictions using GDP per capita as the convergence potential indicator are much lower.     

Foreign Direct Investment,Financial Development and Their Impact on the GDP Growth in Low-income Countries

ABSTRACT

This essay empirically studies the effects and causal links between foreign direct investment (FDI), financial development (FD) and economic growth. The sample consists of the main economies of low-income countries and the study covers the period 1990–2015. The results of the estimate show that, under certain specific economic conditions, FDI affects positively the level of long-term economic growth; it thus makes it possible to improve the economic situation of these countries. Using Johansen’s cointegration technique, the results find that FD; FDI and GDP growth are cointegrated, that shows the pursuit of the long-term equilibrium relationship between them. The error correction model confirms the existence of a double causal relationship between FDI and GDP growth, and between FD and FDI and between GDP growth and FD.     

Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)

Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007–08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015–16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid–19 shock of 2020–21.