时间序列分析在我国GDP预测中的应用

GDP是反映一国经济增长、经济规模、人均经济发展水平、经济结构和价格总水平变化的一个基础性指标,而且也为国家和地区在部署战略方针和制定宏观经济政策上提供了一种参考和依据。改革开放后,中国的经济实力不断提高,GDP连年增长,并呈现一定规律。如果可以准确地预测未来中国之后几年的GDP,将为国家的宏观调控工作提供巨大帮助。本文基于时间序列分析理论,以我国1986年~2016年国内生产总值为基础,利用EViews8.0软件,对数据进行拟合分析,建立模型,并利用所建模型对我国未来三年的GDP作出预测。     

ARIMA模型在我国GDP预测中的应用

国内生产总值GDP是国民经济核算的核心指标,是衡量一个国家总体经济状况重要指标,它反映了国家的经济实力、结构布局和市场规模。中国从改革开放之后,经济不断发展,GDP数据逐年增加,并呈现一定的规律。本文对我国1997~2016年的GDP数据进行了分析,建立了ARIMA模型,结果显示该模型的预测误差较小,能够较好地描述我国GDP状况,可以用来做短期预测,为政府部门制定经济计划提供依据和参考。     

基于ARMA模型对我国GDP季度数据的建模

本文利用我国1992年至2010年GDP的季度数据,建立一个能够有效模拟我国经济时间序列趋势、季节和周期变化的预测模型。分析表明包含季节虚拟变量、ARMA（1,1）的线性趋势模型能够很好地拟合我国实际GDP的值。最后本文对中国2011年一季度的GDP进行预测,并根据预测的结果分析了我国当前的景气状况。     

经营环境

宏观经济Macroeconomy经济学家预测今年中国GDP增长达到或超过10%国家统计局中国经济景气监测中心于日前进行的中国百名经济学家信心调查显示,2007年2季度经济学家信心指数为5.73,为2005年1季度以来最高值,且已连续4个季度在相对高位保持平稳,表明经济学家信心处于比较乐观且持续向好的状态。有97%的经济学家预测2007年中国GDP增长达到或超过10%,平均预测值达10.8%。     

河南省财政收入和GDP相关性分析

财政收入和GDP都是反映一个国家或地区综合经济实力的核心指标,它们之间存在必然的逻辑关系。运用Eviews 6.0软件对分税制后1995～2011年河南省财政收入和GDP进行相关性分析,建立财政收入和GDP模型,得出两者之间存在一元线性回归关系。利用该模型对2012年河南省财政收入进行预测,预测效果比较好。河南省应积极采取措施,做好转变经济增长方式,提高经济运行质量和企业经济效益等工作,以提高GDP增长,保证未来财政收入的增加和社会经济的发展。     

经营环境

宏观经济Macroeconomy世行上调今年中国GDP增幅至11.3%CPI预升4.6%继5月将2007年中国GDP增长预测由此前的9.6%上调至10.4%后,世界银行近日发布的最新《中国经济季报》宣布,再次上调今年中国GDP增长预测值至11.3%。报告同时预测,2008年中国GDP增长率为10.8%。     

基于时间序列的山西省GDP预测分析

GDP即国民生产总值,是衡量一个国家综合实力的指标。本文在时间序列分析理论的基础上,以山西省1993年到2013年21年来的GDP总值为基础,使用Eviews软件和Excel对数据进行针对时间的分析,对模型进行检验,然后利用本文所建立的模型对山西省未来的6年作出预测。     

最优加权组合法在四川省GDP预测中的运用研究

在国民经济发展的过程中,国内生产总值(GDP)是衡量一个国家经济总体经济运行状况的重要指标。GDP预测对国家的经济政策和措施的制定具有战略性意义。单一的GDP预测方法,由于模型本身的特点和适应条件的限制,测算结果只包含了所研究系统的部分信息,无法准确反映系统的全部信息和其真正的发展规律。考虑用最优加权组合法将多个单一模型的优势综合起来,组成一个预测模型,就有可能比较合理地描述系统的整体情况。文章运用最优加权组合预测法对四川省1978—2014年的GDP走势进行预测。预测结合定性、定量方法,考虑采用指数回归方法、时间序列预测方法建立四川省GDP的单一预测模型,运用SPSS,Excel,Eviews等专业软件分别对这两种预测方法进行操作。采用最小二乘法进行权重分配并进行组合预测。结果发现,相对于单一预测模型,组合预测的误差最小,预测结果更可靠。     

对中国GDP总量超越美国时间点的预测分析

对中国GDP“倍增”的预测及相关分析。“两个倍增”首先就是GDP总量的“倍增”，因此，基于定性分析和定量分析方法相结合对GDP进行预测是有一定科学性且具有重要的现实意义。为了进一步揭示中国GDP增长的重要意义和影响，进行了关于中国GDP总量超越美国GDP总量的时间点的预测和分析。     

今年GDP增长仍将保持7%

日前,参加2002年宏观经济预测会的大多数经济学家认为,今年中国GDP仍将保持7%或稍高于7%的增长率.     

预测中国宏观经济变量:专家与模型的组合预测

本文使用组合预测方法,探究以“朗润预测”为代表的专家预测以及计量模型对于中国宏观经济变量的预测效果,并研究对不同预测进行组合预测是否有助于改进预测效果。本文发现,对我国CPI和GDP的增长率,专家预测效果总体上优于模型预测。从原因看,一方面,专家在预测时已经考虑了计量模型的预测信息;另一方面,在经济出现“拐点”的时期,专家通过对实际经济环境和政策的把握,得出更准确的经济预测。组合预测有助于提升预测精度,对专家预测进行组合得到的预测效果优于大多数的专家预测,“模型—专家”组合预测的效果也优于所有的模型和大部分专家预测。     

2009年我国宏观经济形势分析与展望

2008年我国国民经济增速出现明显回落。本文运用ARIMA模型法和向量自回归模型,预测2009年中国经济增长率为8.1%～8.3%。2009年我国面临保经济增长和调经济结构的双重任务。当前国内出现的经济问题,其实并不在于流动性不足,而是严重的市场信心不足的问题。针对这一症结,应以财政政策为主,配套货币政策,加强政策的组合与协调,加快结构性战略调整措施,特别是应将提高国内消费和经济质量作为重中之重。     

Forecast uncertainty and the Taylor rule

In this paper, we derive a modification of a forward-looking Taylor rule by integrating two variables that measure the uncertainty of inflation and GDP growth forecasts into an otherwise standard New Keynesian model. We show that certainty-equivalence in New Keynesian models is a consequence of log-linearization and that a second-order Taylor approximation leads to a reaction function that includes the uncertainty of macroeconomic expectations. To test the model empirically, we use the standard deviation of individual forecasts around the median Consensus Forecast as a proxy for forecast uncertainty. Our sample covers the euro area, the United Kingdom, and the United States for the period 1990Q1–2016Q4. We find that the Bank of England and the European Central Bank have a significantly negative reaction to inflation forecast uncertainty. Our findings also reveal that the Federal Reserve (Bank of England) lowers (raises) its interest rate in response to higher GDP growth forecast uncertainty. We conclude by offering some implications for optimal monetary policy rules and central bank watchers.     

Accounting earnings and gross domestic product

We document that aggregate accounting earnings growth is an incrementally significant leading indicator of growth in nominal Gross Domestic Product (GDP). Professional macro forecasters, however, do not fully incorporate the predictive content embedded in publicly available accounting earnings data. As a result, future nominal GDP growth forecast errors are predictable based on accounting earnings data that are available to professional macro forecasters in real time.     

Nominal GDP Targeting and the Taylor Rule on an Even Playing Field

Some economists advocate nominal GDP targeting as an alternative to the Taylor Rule. These arguments are largely based on the idea that nominal GDP targeting would require less knowledge on the part of policymakers than a traditional Taylor Rule. In particular, a nominal GDP targeting rule would not require real-time knowledge of the output gap. We examine the importance of this claim by amending a standard New Keynesian model to assume that the central bank has imperfect information about the output gap and therefore must forecast the output gap based on previous information. Forecast errors by the central bank can then potentially induce unanticipated changes in the short-term nominal interest rate, distinct from a standard monetary policy shock. We show that forecast errors of the output gap by the Federal Reserve can account for up to 13% of the fluctuations in the output gap. In addition, our simulations imply that a nominal GDP targeting rule would produce lower volatility in both inflation and the output gap in comparison with the Taylor Rule under imperfect information.     

Nominal GDP futures targeting

Central banks have recently done a poor job of stabilizing the path of nominal expenditures. The adverse demand shock of 2008–2009 led to a severe recession in the United States and Europe. Monetary policy could be greatly improved with a regime of “targeting the forecast,” or setting policy so that the expected growth in nominal GDP is equal to the central bank's target growth rate. This goal could be accomplished by setting up a nominal GDP prediction market and then adjusting the monetary base to stabilize nominal GDP futures prices. The market, not central banks, would set the level of the monetary base and short-term interest rates under this sort of policy regime. Modest adjustments in such a regime could address many previous criticisms of futures targeting.     

中国季度GDP的即时预测与混频分析

及时、准确地获得GDP短期预测值对于宏观调控和企业决策至关重要。本文在收集我国实时碎尾数据集的基础上,采用混频动态因子模型,将我国季度GDP的预测频率由“季度”提高到“日度”。研究结果表明,相对于混频抽样模型以及MFVAR等现有模型,混频动态因子模型能够有效解决实时预测中需要面临的数据问题,包括混频指标、碎尾特征、数据的周期性缺失等。本文模型在每个数据发布日,均可更新GDP的预测结果,这不仅将最新的经济活动信息迅速地体现到GDP预测中,而且显著提高了GDP即时预测的准确性,且预测结果随着月度数据信息的增加趋近于GDP真实值。此外,本文还估算了拟GDP季度同比增长率和GDP月度同比增长率两个月度数据序列,为我国宏观经济监测与政策分析提供一定的数据支撑。     

The impact of banking sector stability on the real economy

This article studies the relationship between the degree of banking sector stability and the subsequent evolution of real output growth and inflation. Adopting a panel VAR methodology for a sample of 18 OECD countries, we find a positive link between banking sector stability and real output growth. This finding is predominantly driven by periods of instability rather than by very stable periods. In addition, we show that an unstable banking sector increases uncertainty about future output growth. No clear link between banking sector stability and inflation seems to exist. We then argue that the link between banking stability and real output growth can be used to improve output growth forecasts. Using Fed forecast errors, we show that banking sector stability (instability) results in a significant underestimation (overestimation) of GDP growth in the subsequent quarters.     

Forecasting demand for secondary materials: A case study of waste paper

The authors examine how best to forecast the demand for a resource, in this case waste paper, using three methods. The first is based on long or short-term GDP forecasts, the second is a basic or modified Holt-Winters model, and the third is an input-output model, which can incorporate various scenarios to allow for the effects of technical change. All three techniques are superior to time-series projection. As data availability increases, the application of a combination of these methods would improve the policy response to resource shortage.     

Aggregate accounting research and development expenditures and the prediction of real gross domestic product

The role of accounting information for public policy making has received increased attention in recent years. Konchitchki and Patatoukas, 2014a, Konchitchki and Patatoukas, 2014b demonstrate that growth in aggregate accounting earnings can predict future growth in nominal and real Gross Domestic Product (GDP). We extend the micro to macro literature by decomposing earnings into the R&D and pre-R&D components. Using the Almon (1965) finite distributed lag model, we find that both components can predict future real GDP growth with different lead-lag structures. Importantly, this decomposition significantly increases the explanatory power of the predictive model using accounting information. Aggregate accounting R&D can predict real GDP through the personal consumption, business investment, and net export channels of GDP. Our study extends prior research on the forecasting usefulness of accounting information at the aggregate level and has practical implications for macro forecasting and for public policy making regarding innovative activities of publicly listed firms.