中国金融形势指数混频测度及其预警行为研究

金融形势指数在前瞻性政策制定、金融风险预警等方面发挥着重要作用。本文基于季度GDP及月度经济指标等信息构建了一种混频动态因子FCI模型以实现中国金融形势指数的混频测度,进而分析其风险预警功能。研究结果表明:首先,引入GDP指标的混频FCI模型的测度结果优于同频模型,房地产价格、GDP等是影响金融形势指数的重要指标。其次,混频金融形势指数具有明显的顺周期特征,其与经济景气先行指数的相关性更强,但宏观经济下行时,该指数与一致指数的相关性明显增强。再次,金融形势指数是一致指数的领先因子,对宏观基本面波动趋势具有预警作用。进一步地,金融形势指数还可以明显改善景气指数的样本外预测精度。最后,替换股市及货币政策指标之后,混频FCI模型的估计结果无明显差异,金融形势指数测度结果也极为相似,从而保证了本文研究结论的稳健性。     

有必要调低经济增长预期

根据2001年11月的宏观经济数据预测,2002年中国上半年的经济增长率(同比增长口径)将会有相当大幅度的下降,其中第二季度的GDP同比增长率可能会下降到5.0%左右,达到多年来的最低水平;下半年可能会有一些改变,但是也难以产生强劲的回升.我们预测,2002年全年的GDP增长率为6.5%.     

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。     

基于在线大数据及文本挖掘技术的CPI预测研究

物价在宏观调控中具有重要地位,通过新技术、新手段不断优化CPI预测,对于有效开展宏观调控具有重要意义。本文创新性的将网络数据“清数-i CPI”和文本挖掘信息引入CPI预测,并通过混频数据构建了神经网络预测模型。首先将BERT模型应用于CPI预测关键词的扩展,其次引入“清数-i CPI”这一网络价格信息,最后将高频时间引入解释维度构建混频数据的人工神经网络模型。结果显示,利用文本挖掘和“清数-i CPI”构建的混频数据预测CPI结果稳定,文本挖掘信息对预测精度有明显提升,预测模型在拐点捕捉方面表现优秀,可提前一个月左右实现对CPI的预测。     

中国经济2009年有望“保八望九”

2007年二季度,中国经济的增长率达到十五年来最高的12.6%(年同比),但此后却步入急剧的下行轨道中,连续五个季度下跌,至2008年三季度已跌至9%.最近的月度经济数据显示,2008年四季度的GDP增长率可能会更低,甚至"保七"都有风险.因此,许多市场人士都大幅度调低了对2009年经济增长的预测,很多都从9%以上调到8%以下,甚至有的下调至5%～6%.     

短期利率波动测度与预测:基于混频宏观-短期利率模型

经验研究表明宏观经济对识别短期利率及其波动现象具有重要影响。为合理引入宏观信息并精确拟合与预测短期利率波动行为,本文提出一种包含宏观因子的混频短期利率模型,即BHK-MIDAS模型。基于中国宏观及利率数据信息的研究结果表明:与传统短期利率模型相比,BHK-MIDAS模型具有更优的样本内拟合效果;相对于货币政策指标而言,宏观基本面与通胀指标对短期利率波动的贡献更大;进一步地,混频模型还可以识别出受宏观因子显著影响的短期利率波动的长期成分;特别地,BHK-MIDAS模型在短期利率波动样本外预测方面的良好表现,充分说明宏观因子在识别及预期短期利率波动行为方面的重要贡献。     

我国农产品期货市场与农业经济增长关系的实证研究

本文以2001年1月-2012年3月全国农业GDP的季度数据和全国农产品期货市场成交额的季度数据为基础,采用ADF检验法、Granger因果检验法和回归模型来揭示我国农产品期货市场发展与农业经济增长之间的内在依存关系。实验结果表明,我国农产品期货市场交易额增长率和农业GDP增长率呈逐年递增趋势,且农产品期货市场发展速度较快。我国农产品期货市场的发展与农业经济的增长互为因果关系,农产品期货市场发展对农业经济有一定影响,但反应时滞较长,其作用也有待加强,农产品期货市场的品种和市场环境需进一步完善。     

如何看待当前中国经济下滑

2012年前三季度数字显示,中国经济依然延续下滑态势。GDP同比增长率,1季度为8．1％,2季度7．6％,3季度7.4％。     

基于贝叶斯混频模型的中国宏观经济预测研究

本文通过构建中国宏观经济系统的BMF-VAR模型对中国的宏观经济进行预测分析,并与相关模型的预测结果进行比较,来检验BMF-VAR模型对中国宏观经济进行预测时的适用性。研究结果表明,多变量BMF-VAR模型更适合于短期预测,多变量有效信息的增加促进了短期预测精度的提高;两变量BMF-VAR模型在预测精度方面整体上优于相对应的同频模型;通过BMF-VAR模型能够估计出潜在的月度GDP增长率,且相比于通常所使用的规模以上工业增加值月度增长率更加贴近于真实的GDP增长率走势,有效地解决了月度GDP增长率缺失的问题,据此进行决策会更加有效合理,具有重要的理论和现实意义。     

数字

7.1%据初步核算,上半年我国GDP(国内生产总值)达139862亿元,按可比价格计算,同比增长7.1%,比一季度加快1.0个百分点。分季度看,一季度增长6.1%,二季度增长7.9%。7.5%国际货币基金组织(IMF)7月8日发布的报告预测,世界经济正开始走出     

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。     

Forecasting models for the Chinese macroeconomy in a data-rich environment: Evidence from large dimensional approximate factor models with mixed-frequency data

Our paper contributes to the nascent literature on forecasting the Chinese macroeconomy in a data-rich environment. We perform a horse race among a large set of traditional models and two classes of factor models with 251 monthly and 34 quarterly macroeconomic variables over the period from January 2002 to June 2018. We find evidence that mixed-frequency factor models provide superior forecasts of the CPI, RPI, investment, and consumption when compared to the simple benchmark. During the Global Financial Crisis period, we find little evidence of superiority of factor models over the simple benchmark AR(p) model.     

中国自然利率之谜与债券市场定价——基于宏观金融模型视角

关于发达国家的研究普遍存在使用标准金融模型估计的自然利率与宏观方法估计的自然利率不相符的“自然利率之谜”现象。本文分别使用金融模型和宏观半结构模型估计中国的自然利率,发现同样存在“自然利率之谜”现象。我们通过构建一致性的宏观金融模型,采用宏观经济变量和收益率曲线信息共同估计自然利率来解决这一问题。此外,寻找债券收益率的影响因子是债券定价研究的重要方面,宏观与金融模型的结合是债券定价研究的重要趋势。宏观金融理论表明,趋势通货膨胀和自然利率是收益率曲线的基本决定因素,在宏观金融框架下,我们进一步研究了自然利率对债券收益率的影响。研究结果表明:(1)宏观金融模型能很好地解决中国“自然利率之谜”问题,宏观金融模型估计得到的自然利率略低于宏观半结构模型的结果。(2)自然利率对债券收益率有显著影响,模型中增加自然利率信息能够提高对不同期限国债收益率的拟合优度。本文对进一步加强自然利率影响因素研究,运用一致性宏观金融模型得到的自然利率信息优化货币政策效果提供了参考。     

Nowcasting and the Taylor Rule

Actual federal funds rates in the U.S. have, at times, deviated from the recommendations of a simple Taylor rule. This paper proposes a “nowcasting” Taylor rule that preserves the form of the Taylor rule but encompasses realistic assumptions on information observable to policymakers. Because contemporaneous inflation rates and output gaps are not observable at the time policy is set, policymakers must form “nowcasts.” The optimal nowcast will depend, in part, on forecast uncertainty whenever policymakers have asymmetric costs to over‐ and underpredicting inflation and output. Empirical evidence shows that actual policy rates are consistent with those recommended by a nowcasting Taylor rule.     

Credit Indicators as Predictors of Economic Activity: A Real‐Time VAR Analysis

Using readily available indicators of the profitability, price, and availability of credit—the term spread, junk‐bond spread, and banks’ “willingness to lend” as reported by the Federal Reserve—we show that it is possible to significantly improve on the real‐time output and employment predictions of forecasting professionals at the medium‐run horizons that are most relevant to policymakers and private decision makers. Key to this improvement is a flexible state–space model of data revisions. The willingness‐to‐lend variable is the best real‐time predictor of GDP growth. For forecasting job growth, all three credit indicators prove helpful.     

Imperfect Information and Stock Market Volatility

The purpose of this paper is to (1) develop a model to show how imperfect information can create excess volatility in asset returns and (2) provide empirical evidence consistent with the model. In this framework, variations in information quality cause the market prices to fluctuate more than the corresponding economic fundamentals. Using high‐frequency data from 1988 to 2002, the empirical evidence supports the predictions of the model by showing that economic volatility, defined as squared deviations of the quarterly gross domestic product (GDP) growth rate from its long‐run trend, can explain about half of the variation in S&P 500‐stock index quarterly volatility.     

The real-time information content of macroeconomic news: implications for firm-level earnings expectations

This paper investigates the usefulness of the real-time macroeconomic news-flow as a leading indicator of firm-level end-of-quarter realized earnings. Using recent advances in macroeconomics, I develop a nowcasting model for quarterly earnings and provide two main findings. First, I show that my model provides out-of-sample expectations that are as accurate as analysts’ forecasts. Second, macroeconomic news embedded in my nowcasts is not fully incorporated into investors’ earnings expectations and predicts future abnormal returns around earnings announcements. These findings have three main implications for capital markets research. First, real-time macroeconomic news can be used to update earnings expectations in real-time. Second, there are economic benefits of doing so, as evidenced by the magnitude of risk-adjusted returns around earnings announcements. Third, after three decades of almost nonexistent research on time-series models for quarterly earnings, the door is open again for fruitful research in this area.     

Time series decomposition and measurement of business cycles, trends and growth cycles

A study of business cycles does not require trend estimation and elimination, but a study of growth cycles does. Major cyclical slowdowns and speedups deserve to be analyzed, but the needed time series decomposition presents difficult problems, mainly because trends and cycles influence each other. We compare cyclical movements in levels, deviations from trend, and smoothed growth rates for both the quarterly real GDP and the monthly U.S. Coincident Index—using the phase average trend (PAT). Then we compare alternative trend estimates, deterministic and stochastic, linear and nonlinear, and the corresponding series of deviations from these trends. We discuss how the resulting estimates differ for U.S. growth cycles in the post-World War II period. The results of PAT show great similarity to the results obtained with the Hodrick-Prescott, local linear trend, band-pass filtering methods.     

Does Real-Time Macroeconomic Information Help to Predict Interest Rates?

We analyze the predictive ability of real-time macroeconomic information for the yield curve of interest rates. We specify a mixed-frequency macro-yields model in real time that incorporates interest rate surveys and treats macroeconomic factors as unobservable components. Results indicate that real-time macroeconomic information is helpful to predict interest rates, and that data revisions drive a superior predictive ability of revised macro data over real-time macro data. We also find that interest rate surveys can have significant predictive power over and above real-time macroeconomic variables.     

Exploiting the monthly data flow in structural forecasting

A quarterly stochastic general equilibrium (DSGE) model is combined with a now-casting model designed to read timely monthly information as it becomes available. This implies (1) mapping the structural quarterly DSGE with a monthly version that maintains the same economic restrictions; (2) augmenting the model with a richer data set and (3) updating the estimates of the DSGE׳s structural shocks in real time following the publication calendar of the data. Our empirical results show that our methodology enhances the predictive accuracy in now-casting. An analysis of the Great Recession also shows that our framework would have helped tracing the DSGE׳s structural shocks in real time, obtaining, for example, a more timely account of the 2008 contraction.