考虑高维宏观信息的波动率与股票价格预测

为刻画股票价格的非线性动态特征,并充分利用高维宏观经济变量对股价的预测能力,文章基于LSTM模型、LASSO降维和混频模型,研究了高维情形下利用低频宏观经济变量预测高频股价的问题。首先,使用LASSO方法对高维宏观经济变量进行筛选,并进行因子分析提取宏观因子;然后,使用该宏观因子构建混频GARCH-MIDAS模型以预测波动率;最后,以包含宏观经济信息的波动率和通过因子分析降维后的技术指标因子作为特征,输入LSTM神经网络模型来预测上证综指价格。结果表明,LSTM-GARCH-MIDAS模型具有较高的预测精度和良好的适用性。     

动态因子模型与ARMA模型的比较

在时间序列模型中,随着变量数目的增加,所要估计的参数的数量也随之加大,结果在应用中模型中通常不得不选取尽量少的变量。动态因子模型独特的优势在于,它不必考虑自由度损失问题,也不必对经济结构施加约束。文章根据中国宏观经济变量数据库中的41个变量,建立了动态因子模型预测GDP,并与ARMA模型的预测结果进行了对比。结果显示,动态因子模型的预测效果优于ARMA模型。     

互联网大数据下消费搜索指数的构建与应用

文章结合2013年1月1日至2020年6月30日的百度指数这一高频互联网大数据，利用主成分分析法构建高频居民消费搜索指数。在此基础上，将高频搜索指数拓展进ADL-MIDAS混频模型，对居民消费情况展开实证分析和预测。研究发现：所构建的消费搜索指数能较好捕捉居民在双十一、春节、新冠肺炎疫情期间的潜在消费行为变动情况，总体上，居民对消费相关事件的网络关注度变化会对居民消费水平产生显著影响，消费存在显著的耐久效应，收入增长对消费的促进作用明显。此外，引入高频消费搜索指数的混频模型，对比基准模型，在多步向前动态预测和实时预测上都呈现较高的预测精度和较好的稳定性。     

中国月度GDP同比增长率估算与经济周期分析

为了充分利用我国同比数据的优势、准确反映和及时调控宏观经济,本文提出一种估计同比数据混频近似因子模型的EM算法,并得到了我国月度GDP同比增长率的估计,然后利用此指标分析了我国经济波动的周期性.研究发现,月度数据的局部极差大于季度数据,尤其在宏观经济经历严重外部冲击时期,月度和季度GDP增长率数据相差较大,即季度GDP平滑了经济的波动性,低估了外部冲击效应.另外,月度GDP增长率数据能够更加精确、及时确定经济周期的转折点.     

我国经济预期波动的非对称性分析

文章针对9类宏观经济预警指标,利用动态因子模型构建了我国宏观经济预警指数,并将其作为我国经济预期的代理变量.在此基础上,利用MS(2)-AR(2)模型,分析了表征我国经济“景气”状况的宏观经济预警指数的非线性动态调整特征.实证结果表明,利用动态因子模型构建的宏观经济预警指数对一致指数具有预测作用.并将样本期内我国经济划分为“景气”和“不景气”两种状态,较好地反映了我国经济在此期间的“景气”状态的转换特征.     

经济景气指标与实际GDP增长率的混频预测

文章通过构建月度景气指标与季度实际GDP增长率之间的混频动态向量自回归模型,并采用期望最大值算法和卡尔曼滤波来实现混频数据和缺失数据的估计和迭代预测.大量月度景气指标的MFVAR模型的伪实时数据的多步滚动迭代样本外预测结果表明:虽然不同类别的月度景气变量在不同预测期的预测结果存在一定的差异,但实时预报、短期预测,以及组合预测结果均表明混频动态向量自回归预测模型对我国季度实际GDP增长率的实时预报和短期预测具有精确性、有效性与适用性.     

基于最优ARIMA模型的我国GDP增长预测

准确预测GDP对政府进行有效宏观调控意义重大,而ARIMA模型是预测GDP的有效工具.文章以1952-2011年不变价格GDP为研究样本,首先建立36组ARIMA模型,进而运用多重筛选准则,找到最优滞后阶数p和q,最后确定了最优ARIMA(6,1,3)模型.该模型通过了多项假设检验,对2009-2011年的GDP预测精度高.笔者还利用模型对未来几年的GDP进行了预测.     

当前宏观经济现时预测研究综述

宏观经济现时预测具有重要的现实意义。如何有效地利用大数据时代丰富的数据资源进行宏观经济现时预测已成为一个重要的研究课题。在梳理现有研究的基础上,对主流的宏观经济现时预测模型进行系统介绍,并就其预测能力进行分析比较,然后阐述当前现时预测可用的信息集及预测变量选取方法,再对中国宏观经济现时预测的现状做出评价,最后指出当前研究的不足与未来研究的主要方向,旨在为实证研究者提供技术参考。     

基于ARIMA-GMDH的GDP预测模型

文章先对四川省GDP分别建立了ARIMA时间序列模型和GMDH变量自回归模型来进行预测;然后利用GMDH自组织建模方法建立ARIMA-GMDH组合预测模型来预测;最后使用Bonferroni-Dunn方法对三个模型的稳定性进行分析检验。模型预测结果和稳定性检验结果表明:基于ARIMA-GMDH组合的GDP预测模型的拟合和预测都优于另外两种单预测模型。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。     

中国混频金融状况指数的经济增长预测效果与检验

基于TVP-FAVAR模型及修订式测算中国FCI,并刻画SF-FCI和MF-FCI的经济增长预测效果与冲击效应,进一步运用混频Granger方法检验FCI与经济增长之间的因果关系,结果表明:合成FCI的各金融变量缺口存在较大差异,简单同频转换存在一定误差;SF-FCI领先经济增长大约1～2季度,MF-FCI领先经济增长大约2～4个季度,MF-FCI的经济增长效应与拟合效果更好,"典型时期"预测经济增长的先导性更强;SF-FCI和MF-FCI是经济增长的混频Granger原因,经济增长不是SF-FCI的混频Granger原因;经济增长是MF-FCI的混频Granger原因,混频Granger检验下MF-FCI和经济增长的因果关系更密切。     

利用互联网大数据预测季度GDP增速的方法研究

大数据为季度GDP走势预测创新研究带来重要突破口。本文利用百度等网站的互联网大数据,基于代表性高维数据机器学习(和深度学习)模型,对我国2011-2018年季度GDP增速深入进行预测分析。研究发现,对模型中的随机干扰因素作出一定分布的统计假设,有助于降低预测误差,任由模型通过大量数据机械地学习和完善并不总是有利于模型预测能力的提升;采用对解释变量集添加惩罚约束的方法,可以有效地处理互联网大数据维度较高的棘手问题;预测季度GDP增速的最优大数据解释变量集的稳定性较高。     

Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH

The performance of different information criteria – namely Akaike, corrected Akaike (AICC), Schwarz–Bayesian (SBC), and Hannan–Quinn – is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.     

Evaluation of realized volatility predictions from models with leptokurtically and asymmetrically distributed forecast errors

Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized volatility. The forecasting evaluation is valid for standardized forecast errors with leptokurtic distribution as well as with leptokurtic and asymmetric distributions. Additionally, the widely applied forecasting evaluation function, the predicted mean-squared error, fails to select the adequate model in the case of models with residuals that are leptokurtically and asymmetrically distributed. Hence, the realized volatility forecasting evaluation should be based on the standardized forecast errors instead of their unstandardized version.     

Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence

Most existing reduced-form macroeconomic multivariate time series models employ elliptical disturbances, so that the forecast densities produced are symmetric. In this article, we use a copula model with asymmetric margins to produce forecast densities with the scope for severe departures from symmetry. Empirical and skew t distributions are employed for the margins, and a high-dimensional Gaussian copula is used to jointly capture cross-sectional and (multivariate) serial dependence. The copula parameter matrix is given by the correlation matrix of a latent stationary and Markov vector autoregression (VAR). We show that the likelihood can be evaluated efficiently using the unique partial correlations, and estimate the copula using Bayesian methods. We examine the forecasting performance of the model for four U.S. macroeconomic variables between 1975:Q1 and 2011:Q2 using quarterly real-time data. We find that the point and density forecasts from the copula model are competitive with those from a Bayesian VAR. During the recent recession the forecast densities exhibit substantial asymmetry, avoiding some of the pitfalls of the symmetric forecast densities from the Bayesian VAR. We show that the asymmetries in the predictive distributions of GDP growth and inflation are similar to those found in the probabilistic forecasts from the Survey of Professional Forecasters. Last, we find that unlike the linear VAR model, our fitted Gaussian copula models exhibit nonlinear dependencies between some macroeconomic variables. This article has online supplementary material.     

Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset

Many recent articles have found that atheoretical forecasting methods using many predictors give better predictions for key macroeconomic variables than various small-model methods. The practical relevance of these results is open to question, however, because these articles generally use ex post revised data not available to forecasters and because no comparison is made to best actual practice. We provide some evidence on both of these points using a new large dataset of vintage data synchronized with the Fed’s Greenbook forecast. This dataset consist of a large number of variables as observed at the time of each Greenbook forecast since 1979. We compare realtime, large dataset predictions to both simple univariate methods and to the Greenbook forecast. For inflation we find that univariate methods are dominated by the best atheoretical large dataset methods and that these, in turn, are dominated by Greenbook. For GDP growth, in contrast, we find that once one takes account of Greenbook’s advantage in evaluating the current state of the economy, neither large dataset methods, nor the Greenbook process offers much advantage over a univariate autoregressive forecast.     

我国季度GDP实时数据预测与评价

传统实证研究中使用的当期特定数据存在滞后信息和噪音信息缺陷,导致模型估计结果存在偏误。应用宏观经济实时数据可以有效的剔除造成模型偏误的滞后信息和噪音信息,得到更为准确的估计结果。MIDAS模型可将低频的关键经济数据与高频数据同时估计,较好的解决了应用一般模型存在的高频数据信息损失问题。本文应用M-MIDAS-DL模型与季度GDP实时数据建立我国季度GDP预测模型,实证表明,应用实时数据与组合预测方法,能及时准确预测出2008年以来中国经济增长率的下滑与反弹走势,能起到较好的提前预警作用,是当前较为有效的经济预测手段之一。     

Comment

We propose a sequential test for predictive ability for recursively assessing whether some economic variables have explanatory content for another variable. In the forecasting literature it is common to assess predictive ability by using “one-shot” tests at each estimation period. We show that this practice leads to size distortions, selects overfitted models and provides spurious evidence of in-sample predictive ability, and may lower the forecast accuracy of the model selected by the test. The usefulness of the proposed test is shown in well-known empirical applications to the real-time predictive content of money for output and the selection between linear and nonlinear models.     

2005年中国税收收入预测模型比较

为探索一种较为有效的工具来提高税收收入预测精度，利用1985-2004年的样本数据，建立了五个模型来预测中国2005年的税收收入。结果表明：ARMA（1，1）模型中，以GDP为外生变量的自回归模型、以政策因素为虚拟外生变量的自回归模型以及对数线性移动平均模型都是预测税收收入的有效模型，但以GDP为外生变量的自回归模型在预测2005年税收收入时，预测值与实际值的预测偏差仅有1．23％，此模型在预测税收收入时预测精度最高，是预测税收收入的一种较为有效的工具。     

基于Boosting-ARMA的碳价预测

自回归滑动平均(ARMA)模型是最流行的预测模型之一,而模型选择却是使用ARMA进行预测的难点,尤其是当真实模型的阶数较高时,因此提出Boosting-ARMA预测算法,利用Boosting算法进行最优子集ARMA寻找,自动且高效地完成ARMA模型的识别。模拟实验显示,Boosting-ARMA优于其他方法,用新算法预测碳价实证分析发现,Boosting-ARMA算法可以获得较高的碳价预测准确性并且方便快捷。     

变权重组合预测模型的局部加权最小二乘解法

随着科学技术的不断进步，预测方法也得到了很大的发展，常见的预测方法就有数十种之多。而组合预测是将不同的预测方法组合起来，综合利用各个方法所提供的信息，其效果往往优于单一的预测方法，故得到了广泛的应用。而基于变系数模型的思想研究了组合预测模型，将变权重的求取转化为变系数模型中系数函数的估计问题，从而可以基于局部加权最小二乘方法求解，利用交叉证实法选取光滑参数。其结果表明所提方法预测精度很高，效果优于其他方法。