人民币实际汇率的非线性特征研究

本文通过采用不同的线性和非线性一元时间序列模型对人民币实际汇率行为进行研究。研究结果表明，非线性的自我激励阈值自回归模型和平滑过渡自回归模型对人民币实际汇率历史数据有很好的拟合效果，且人民币实际汇率具有显著的非线性动态行为特征。     

多元线性回归与ARIMA在中国人口预测中的比较研究

参考中国统计年鉴1970-2005年的数据,文章建立了多元线性回归模型和基于ARIMA算法的时间序列模型对我国人口进行预测,将结果与实际值进行比较,得出多元线性回归模型在人口预测上具有更高的精准度。两个模型同时表明,我国人口在短期内会继续增长,并且多元线性回归模型表明增长趋势会逐渐变缓。     

基于ARIMA模型和GARCH模型的美元指数波动性分析

美元指数在金融危机前后出现了耐人寻味的变化,其波动影响着国际经济、政治格局。本文运用自回归单整移动平均时间序列(ARIMA模型)和广义自回归条件异方差时间序列(GARCH模型)的方法分析美元指数,采集大量历史样本数据,对其波动特性进行实证研究。运用ARIMA模型对未来短期美元指数走向进行预测,表明美元指数的波动有一定的规律。同时,对美元指数建立用于描述大量金融时间序列的GARCH(1,1)模型,通过模型的定阶、检验、预测发现GARCH模型有较好的预测较长期整体走势的能力。     

结构时间序列模型在经济预测方面的应用研究

本文开发了一种新的经济时间序列预测方法——利用结构时间序列模型进行预测。在结构时间序列模型中由经济指标分解得到的趋势、循环、季节及不规则因素是不可观测的变量，不能利用传统的回归分析方法求解模型，因此，本文采用状态空间方法来求解结构时间序列模型。本文通过ARIMA模型来研究经济时间序列的结构，在此基础上建立了不同形式的结构时间序列模型，并利用结构时间序列模型对我国社会消费品零售总额、狭义货币供给量(M1)和国内生产总值(GDP)等经济时间序列进行了预测。实证研究表明，结构时间序列模型具有良好的预测效果。从而为经济时间序列预测提供了一种新的有效方法。     

基于协整分析的我国货运量预测

货运量是运输系统中一个重要指标。研究货运量的变化规律,对货运量进行科学合理预测,对交通规划和经济发展具有重要意义。对货运量进行时间序列分析,建立了货运量的传统时间序列模型,观察到残差存在自相关,提出修正残差的ARMA模型．消除自相关。最后根据模型对货运量进行预测,并提出政策建议。     

基于STAR模型的中国实际汇率非线性态势预测

汇率的趋势对于一国货币政策的制定和实施至关重要,因为实际汇率是度量一国竞争力的主要指标,所以对于处于经济转轨过程中的中国,刻画汇率的变动尤为关键。实际汇率除了对不同冲击产生反应,还取决于其所在经济发展的不同阶段,传统线性模型无法体现这一过程中经济变量的动态变化。本文采用平滑转移自回归模型对中国实际汇率进行分析和预测,检验结果表明,中国实际汇率走势是非线性的并体现了非对称性。基于此模型得出的汇率预测显示,2008年人民币还将继续升值,名义汇率年底预期将达到6.69,年升值幅度为9.1%,并主要依赖于中美两国物价指数变化的情况。     

高速公路发展及投资预测

通过对各年高速公路里程数、高速公路汽车总量、CPI、GDP的数据进行整理分析,建立了一元线性回归模型、灰色预测模型、基于ARIMA与NN的模型,分别预测到以上数据在短期内的数值. 首先利用高速公路里程数与GDP之间的线性关系可以预测高速公路的规模发展,可以得知未来高速公路的规模将得到继续发展.然后对里程数、汽车总量进行预测,用预测值可以简单计算出收入、贷款本息、维护费用,进而得出高速公路未来收益情况,这部分的收益相当可观,表明高速公路的发展前景良好.在此基础上,再考虑通货膨胀率对总盈利的影响,预测出高速公路的投资收益,具有良好的投资前景.最后,由基于ARIMA与NN的模型预测GDP,以GDP达到高收入国家标准的时间来确定收费政策的改变时间节点,分别对两段时间内的收费政策进行讨论.高速公路盈利较高,所以可以适当调整收费政策,使收费仅用于维护费和新建公路的费用,将"贷款修路,收费还贷"的收费模式转换为"以路养路"模式. 综上所述,无论是否考虑通货膨胀,总盈利情况都非常可观,在未来30年内高速公路的发展和投资前景均良好.当GDP达到一定水平的时候,可以适当降低收费标准,向少收费或不收费的方向倾斜.     

ARIMA模型在债券收益率中的应用

通过对债券即期收益率的分析,可以更加精确地知道债券收益率的变化情况.本文采用统计学家Box和Jenlins提出的ARIMA模型对我国债券收益率数据进行分析,得出ARIMA模型不但适合非平稳的时间序列,而且适合分析债券收益率,表明ARMA(1,1,1)膜型的效果是比较好的. 债券的收益率与多种因素有关,并且各因素之间又存在相互制约的关系,通过检验知道该债券的收益率是非平稳的时间序列,不能用自回归移动平均ARMA模型进行分析,本文用ARIMA模型进行分析,数据来源于聚源数据库中的债券到2008年2月18日的即期收益率数值,采用的分析软件为Eviews6.0.     

中国人均GDP时间序列模型建模研究

本文对中国人均GDP1949年至2004年时间序列进行研究,建立了用于预测的时间序列模型,结果为ARIMA(1,2,1)。     

基于非线性主成分分析的信用评估模型研究

传统的主成分分析（PCA）本质上是一种线性映射算法,无法有效处理非线性关系的数据。本文在分析自联想神经网络（AANN）的基础上,借鉴传统PCA方法中的序数主成分概念,提出了基于顺序自联想神经网络（SAANN）的非线性主成分分析法（NLPCA）。进一步,结合神经网络（NN）和Logisitic模型,以我国上市公司为研究对象,分别构建了基于NLPCA-NN和NLPCA-Logisitic的信用评估模型。实证结果及ROC曲线分析表明,本文构建的NLPCA相比传统的线性PCA方法能有效地实现数据的非线性特征提取与降维,提高模型预测性能。此外,实证结果还表明,在相同PCA方法处理数据的条件下,神经网络模型的信用评估效果要好于Logisitic模型。     

Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.     

多基准、多边人民币汇率评估统计研究

汇率评估是一种汇率合理性评估,合理汇率是多方利益均衡的结果,据此,可以定义汇率评估基准及其测算模型,并通过相关数据的搜集与整理,最终完成多基准、多边人民币汇率的评估任务。实证研究结果表明,目前人民币汇率的走势是合理的。     

Combining filter design with model-based filtering (with an application to business-cycle estimation)

Filters used to estimate unobserved components in time series are often designed on a priori grounds, so as to capture the frequencies associated with the component. A limitation of these filters is that they may yield spurious results. The danger can be avoided if the so-called ARIMA-model-based (AMB) procedure is used to derive the filter. However, parsimony of ARIMA models typically implies little resolution in terms of the detection of hidden components. It would be desirable to combine a higher resolution with consistency of the structure of the observed series.We show first that for a large class of a priori designed filters, an AMB interpretation is always possible. Using this result, proper convolution of AMB filters can produce richer decompositions of the series that incorporate a priori desired features of the components and fully respect the ARIMA model for the observed series (hence no additional parameter needs to be estimated).The procedure is discussed in detail in the context of business-cycle estimation by means of the Hodrick-Prescott filter applied to a seasonally adjusted series or a trend–cycle component.     

Forecasting monthly and quarterly time series using STL decomposition

This paper is a re-examination of the benefits and limitations of decomposition and combination techniques in the area of forecasting, and also a contribution to the field, offering a new forecasting method. The new method is based on the disaggregation of time series components through the STL decomposition procedure, the extrapolation of linear combinations of the disaggregated sub-series, and the reaggregation of the extrapolations to obtain estimates for the global series. Applying the forecasting method to data from the NN3 and M1 Competition series, the results suggest that it can perform well relative to four other standard statistical techniques from the literature, namely the ARIMA, Theta, Holt-Winters’ and Holt’s Damped Trend methods. The relative advantages of the new method are then investigated further relative to a simple combination of the four statistical methods and a Classical Decomposition forecasting method. The strength of the method lies in its ability to predict long lead times with relatively high levels of accuracy, and to perform consistently well for a wide range of time series, irrespective of the characteristics, underlying structure and level of noise of the data.     

Exchange-rate forecasts with simultaneous nearest-neighbour methods: evidence from the EMS

In this paper we extend nearest-neighbour predictors to allow for information content in a wider set of simultaneous time series. We apply these simultaneous nearest-neighbour (SNN) predictors to nine EMS currencies, using daily data for the 1st January 1978–31st December 1994 period. When forecasting performance is measured by Theil's U statistic, the (nonlinear) SNN predictors perform marginally better than both a random walk and the traditional (linear) ARIMA predictors. Furthermore, the SNN predictors outperform the random walk and the ARIMA models when producing directional forecasts.When formally testing for forecast accuracy, in most of the cases the SNN predictor outperforms the random walk at the 1% significance level, while outperforming the ARIMA model in three of the nine cases. On the other hand, our results suggest that the probability of correctly predicting the sign of change is higher for the SNN predictions than the ARIMA case.     

人民币汇率波动对我国HS分类商品出口的影响

本文以1997~2006年HS分类商品出口的月度数据为样本,采用边限检验方法判别长期协整关系,并采用自回归多元滞后分布-误差修正模型(ARDL-ECM)分析人民币实际有效汇率波动,对不同类别商品出口的长期和短期影响。估计结果显示,不同类别商品出口受人民币汇率水平和波动率变化的影响有较大差异。     

Real exchange rate misalignment: Prelude to crisis?

A model of the long-run equilibrium real exchange rate based upon macroeconomic fundamentals is employed to calculate real exchange rate misalignments for Poland and Russia during the 1990s using the Beveridge and Nelson (Beveridge, S., Nelson, C., 1981. A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle. J. Monetary Econ. 7, 151–74) decomposition of macrofundamentals into transitory and permanent components. Short-run movements of the real exchange rate are estimated with ARIMA and GARCH error correction specifications. The different nominal exchange rate regimes of the two countries generate different levels of misalignment and different responses to exogenous shocks. The average misalignment in Russia is substantially greater than that in Poland, indicating incipient pressures to devalue the ruble immediately preceding the August 1998 crisis. The half-life of an exogenous shock is found to be much shorter for Poland than for Russia in the pre-crisis period. Dynamic forecasts indicate that the movements of the real exchange rate in the post-crisis period are significantly different from those in the pre-crisis period. Thus, the currency crisis in Russia could not be anticipated with the movements of the real exchange rate estimated with the macroeconomic fundamentals.     

人民币实际汇率失调程度研究:1994～2004

本文运用行为均衡汇率理论模型对人民币均衡实际汇率和人民币汇率失调程度进行了实证研究，样本区间为1994年1季度至2004年1季度。研究表明，当前时期人民币实际汇率存在较为严重的低估现象，人民币存在升值预期。对人民币汇率失调背后的经济原因分析表明，钉住美元的汇率政策是造成人民币汇率失调的一个主要因素。为了避免人民币汇率出现长时期的失调，建议央行进一步改革现行的汇率制度，改变汇率过于固定的现状，适当扩大人民币汇率的浮动区间，实行更加积极和更具应变能力的汇率政策。     

The impact of offshore exchange rate expectations on onshore exchange rates: The case of Chinese RMB

This paper examines the impact of offshore RMB exchange rate expectations on onshore RMB (CNY) exchange rates. Employing data for the period of 2005–2018, we show that overall offshore market expectations influence onshore RMB rates, but this effect is significant only for the period after the “Second exchange rate regime reform” in 2010. The non-uniform nature of this impact is also confirmed by the existence of a threshold effect of the expectations in the same period. The study improves our understanding of how the offshore RMB market influences onshore RMB spot rates as a result of the marketization reform of the RMB exchange rate regime.