用于宏观经济预测的VAR、BVAR和VARMA模型的对比

向量自回归模型（VARMA）是宏观经济学家常用的一种方法。对于处理短期的宏观经济变量,贝叶斯向量自回归{BVAR}模型目前又在欧美比较流行。并且随着一些计算及计量软件的发展,VARMA方法也逐渐走入宏观经济学家的视野。本文用1985-2008年间GDP增长率,消费者物价指数和年利率,分别拟合这三种模型,通过比较它们的预测结果,得出结论,并给出建议。     

基于混频向量自回归模型的宏观经济预测

本研究利用中国宏观经济指标构建了基于贝叶斯估计的混合频率向量自回归模型(MF-BVAR),并对该模型预测中国宏观经济运行情况的效果进行了检验。本文模型在允许多变量、不同频数据共存的条件下提高了模型估计的自由度,从而实现高精度预测。实证结果显示,在对宏观经济管理部门所关注的核心经济变量CPI、RPI和GDP等进行预测时,MF-BVAR模型相对于目前广泛应用的同频向量自回归模型和MIDAS模型,预测精度都有显著改善。本文亦发现房地产投资对于模型预测能力的重要作用,从样本外预测的角度佐证了房地产部门对于中国宏观经济的重要影响。本文也验证了中国股票市场表现不能对预测宏观经济运行提供额外贡献。     

基于VAR模型的我国大豆期货市场价格影响因素分解

大豆期货作为我国最早一批设立的农产品期货市场,在套期保值、价格发现等发挥了多方面的作用,但是一直以来期货价格波动频繁,因此本文基于VAR模型对大豆期货市场价格的影响因素进行分析。依次进行了平稳性检验、协整检验、建立向量自回归模型、脉冲响应函数和方差分解等步骤进行实证分析。结果表明,我国大豆期货价格与大豆现货价格、进口价格、相关商品期货价格以及国际大豆期货价格之间存在协整关系,且大豆期货价格受自身影响程度最大,相关商品期货价格和大豆进口价格也有明显的影响,而我国大豆期货价格受国外的大豆期货价格影响程度在削弱。最后根据结论给出相关的建议。     

内蒙古经济增长与居民储蓄、投资关系动态分析——基于VAR模型的实证研究

本文利用1985-2008年的时间序列数据,基于宏观和动态的视角,对我区经济增长及其影响因素进行了实证分析。Johansen协整检验结果表明内蒙古经济增长与消费、投资关系之间存在一种长期均衡关系。向量自回归VAR模型的动态分析表明,内蒙古经济增长对自身的冲击产生持续响应,经济增长呈现出一定程度的＂惯性＂特征;消费、投资对GDP的冲击是正向的,居民储蓄对GDP的冲击是反向的。方差分析显示消费对内蒙古经济发展有重要影响。     

基于VAR模型的集合信托预期收益率与宏观经济动态关系研究

集合信托预期收益率受到多种因素影响,宏观经济变量的作用不可忽视,将影响集合信托定价的资金成本、风险成本。基于VAR模型,利用2007年1月至2014年5月数据研究了集合信托预期收益率与宏观经济动态关系。实证研究表明,二者存在长期协整关系,集合信托定价主要以1年期国债收益率为基准,根据CPI水平进行调整,同时不同类型集合信托产品的预期收益率与宏观经济标量的动态关系存在一定差别,差异化定价策略非常必要。     

我国股票市场与宏观经济变量关系的实证研究

本文利用格兰杰因果检验、向量自回归模型等方法,对我国股票市场与宏观经济变量之间的关系进行实证研究。结果表明,股价变动会在一定程度上影响实体经济的变动,尤其是影响产出,但产出并不能十分显著地引起股价的变化,我国股市的＂晴雨表＂功能未能得到很好地发挥。相比货币供应量,利率政策对股价影响的时滞虽然较长,但效力更显著。     

基于VAR模型的河北省经济增长波动分析

本文通过建立向量自回归模型（VAR）,运用Granger检验、脉冲响应函数和VAR模型的预测方差分解等方法对河北省的经济增长波动情况进行了实证分析。研究结果表明：河北省的经济增长类型属于出口导向型,另外固定资产投资对经济增长的影响较大,并有继续加大的趋势,消费没有对经济增长起到应有的作用,在河北省经济未来的发展过程中应扩大内需,刺激消费,促进经济增长。     

基于VAR模型的财政支出关于财政收入的预测分析

本文首先通过Granger因果关系检验证明财政收入是财政支出的决定因素.在此基础上,建立财政支出关于财政收入的VAR模型,对未来5年即2017---2021年的财政支出数值进行预测.由结果可知,未来5年我国财政支出将呈稳步增长态势,为我国合理控制财政支出规模提供了客观依据.在分析的过程中,我们同时可以看出此VAR模型预测的相对误差较小,预测精度较高.     

业绩预告披露与盈余管理研究

盈余管理存在的根本原因在于投资者与管理层之间的信息不对称。业绩预告作为上市公司未来经营成果、财务状况与现金流量的预测,在很大程度上会影响投资者对上市公司的评估及其投资决策。从业绩预告披露的特征方面出发,研究业绩预告披露与盈余管理之间的关系,包括业绩预告的性质、预告精确度、预告误差分别与盈余管理程度的关系,结果发现：发布业绩预告的公司,盈余管理水平更高。预告精确度以及预告期间与预测当期盈余管理水平正相关,预测误差与盈余管理水平负相关。当消息类型不同的时候,预测的强制性与否以及＂变脸＂对盈余管理水平的影响不同。结论支持了上市公司财务报告迎合业绩预告披露的说法。     

Strategic Trading When Agents Forecast the Forecasts of Others

We analyze a multi-period model of trading with differentially informed traders, liquidity traders, and a market maker. Each informed trader's initial information is a noisy estimate of the long-term value of the asset, and the different signals received by informed traders can have a variety of correlation structures. With this setup, informed traders not only compete with each other for trading profits, they also learn about other traders' signals from the observed order flow. Our work suggests that the initial correlation among the informed traders' signals has a significant effect on the informed traders' profits and the informativeness of prices.     

Analysts' Rationality and Forecast Bias: Evidence from Sales Forecasts

When optimistic forecasts can improve access to management, rational analysts have incentives to issue optimistically-biased forecasts (Lim, 2001). This paper proposes that the extent of this optimistic forecast bias will depend on the forecast's importance to management. If management attaches less importance to a forecasted measure, analysts should decrease their forecast bias because the expected benefits of issuing optimistic forecasts are less. We examine analysts' earnings and sales forecasts, and predict that analysts' optimistic bias will be greater for earnings than for sales. Results are consistent with our predictions and contribute to the evidence that analysts' forecast bias is rational and intentional.     

A Performance Comparison Between Cross-Sectional Stochastic Dominance and Traditional Event Study Methodologies

In this study, the performance of cross-sectional stochastic dominance (SD), first proposed by Falk and Levy (FL) (1989), is compared with three traditional event study methodologies: the Mean Adjusted model, the Market Adjusted model, and the Market and Risk Adjusted Returns model. The comparison technique we use is a simulations approach similar to that of Brown and Warner (BW) (1980). BW show that the Mean Adjusted and Market Adjusted Returns models perform as well as the more sophisticated Market and Risk Adjusted Returns model. FL, however, provide a very compelling argument against the three traditional event study methodologies. The problem, they note, is not the theoretical need for risk adjustment; it is the definition and measurement of risk. FL assert that the observed abnormal returns (or lack thereof) may be due to omitted variables, a market proxy effect, or other specification errors in implementing the traditional event study methodologies.The present research finds that SD analysis without the bootstrap method for statistical testing is not very useful at any level of abnormal return. However, when the bootstrap method of statistical testing is employed, SD is found to perform as well as, and sometimes better than, the three traditional models in detecting simulated abnormal performance at all test levels. The results are consistent with FL's assertion that the improved performance may result from the SD methodology being free from the specification errors inherent in the three traditional event study models.     

Refining the Degree of Earnings Surprise: A Comparison of Statistical and Analysts' Forecasts

This paper compares the relative predictive ability of several statistical models with analysts' forecasts. It is one of the first attempts to forecast quarterly earnings using an autoregressive conditional heteroskedasticity (ARCH) model. ARCH and autoregressive integrated moving average models are found to be superior statistical forecasting alternatives. The most accurate forecasts overall are provided by analysts. Analysts have both a contemporaneous and timing advantage over statistical models. When the sample is screened on those firms that have the largest structural change in the earnings process, the forecast accuracy of the best statistical models is similar to analysts' predictions.     

A Comparison of Alternative Forecast Models of REIT Volatility

This study compares the relative performance of several well-known models in the forecasting of REIT volatility. Overall our results suggest that long-memory models (ARFIMA & FIGARCH) provide the best forecasts. Using either a large sample or some statistically justified small subsamples, we find that long memory models consistently outperform their short-memory counterparts (GARCH & Stochastic Volatility models) over a variety of forecast horizons. We also find that asymmetric models (EGARCH & FIEGARCH) are the worst performers among all models. Our study complements and extends a recent study of Cotter and Stevenson (2008) which demonstrates the usefulness of long-memory models in modeling REIT volatility. We conclude that in addition to modeling REIT volatility, long-memory models should also be adopted to forecast REIT volatility.     

Statistical Inference for Lee-Carter Mortality Model and Corresponding Forecasts

Although the Lee-Carter model has become a benchmark in modeling mortality rates, forecasting mortality risk, and hedging longevity risk, some serious issues exist on its inference and interpretation in the actuarial science literature. After pointing out these pitfalls, this article proposes a modified Lee-Carter model, provides a rigorous statistical inference, and derives the asymptotic distributions of the proposed estimators and unit root test when the mortality index is nearly integrated and errors in the model satisfy some mixing conditions. After a unit root hypothesis is not rejected, future mortality forecasts can be obtained via the proposed inference. An application of the proposed unit root test to U.S. mortality rates rejects the unit root hypothesis for the female and combined mortality rates but does not reject the unit root hypothesis for the male mortality rates.     

Performance and Merton-type default risk of listed banks in the EU: A panel VAR approach

This paper provides empirical evidence that sheds new light into the dynamic interactions between risk and efficiency, a highly debated issue. First, we estimate three alternative measures of bank performance, by employing a directional distance function framework, along with a cost frontier and a profit function. As a second step, we calculate a Merton-type bank default risk. Then, we employ a panel VAR analysis, which allows the examination of the underlying relationships between efficiency and risk without applying any a priori restrictions. Most evidence shows that the effect of a one standard deviation shock of the distance to default on inefficiency is negative and substantial. There is some evidence of a reverse causation. As part of a sensitivity analysis, we extent our study to investigate the relationship between efficiency and default risk for banks with different types of ownership structures and across financial systems with different levels of development.     

人民币跨境流动与金融失衡——基于VAR和门限模型的经验分析

基于2010~2014年月度数据,从汇率波动、资本流动以及资产价格波动等渠道,利用 VAR 模型和门限模型研究人民币跨境流动对金融失衡的影响机制。研究表明：从冲击效应看,人民币国际化背景下,金融失衡对汇率波动、资本流动和资产价格波动的反应程度显著,其中,对资产价格的反应程度最强;人民币跨境流动程度和汇率波动程度对金融失衡存在显著的门限效应。因此,为推动人民币跨境流动,应强调在推进汇率市场化改革和加强国际资本流动监管的基础上,加大稳定国内资产价格的力度,防范金融失衡。     

Impact of Adoption of Long-Term Performance Plans on Financial Analysts' Long-Term Forecasts

Prior research demonstrates that firms adopting long-term performance plans experience increased capital investment, earnings, and risk in the post-adoption period. However, these results are subject to distortions that may result from exogenous factors over the long time period examined. To avoid these potential distortions we examine financial analysts' forecasts in the periods immediately preceding and following the adoption of the performance plan. We find that projected long-term capital expenditures per share, earnings per share and cash flow per share are revised upward in the post-adoption period. These results are consistent with the premise that the adoption of long-term performance plans is expected to favorably affect managers' decisions. In addition, we find that the revisions are primarily attributable to firms that were performing poorly in the period prior to plan adoption and in greatest need of change. This finding has not been documented in previous studies.