几类非线性协整模型研究综述

协整分析方法经过20多年的发展成为计量经济学界的一个前沿工具,在经济与金融领域得到了广泛的应用。线性协整分析已经成熟,而非线性协整的理论与方法仍在持续研究中。本文回顾了最近20年非线性协整的发展历史,其中包括结构变化、门限非线性、马尔可夫转换和平滑转换等几类非线性协整模型,强调了这些非线性机制的本质区别,总结了已取得的一些重要研究成果,最后对该问题的最新发展动向加以概括。     

非线性计量经济模型的非参数估计方法研究

本文提出使用核估计的方法构造平滑转移模型(STR)的非参数模拟最大似然估计(NPSML),给出了NPSML估计量的构造方法、渐近性质以及相应的核函数和窗宽的选择准则,并利用滑动窗宽算法对估计量的构造过程进行了改进。通过Monte Carlo实验证明,该方法是可靠的,并且当误差项存在序列相关时,此种估计量是稳健的。     

Time Series Modelling of Daily Tax Revenues

We provide a detailed discussion of time series modelling of daily data in general and daily tax revenues in particular. The main feature of the daily tax revenue series is the pattern within calendar months. Standard time series methods for seasonal adjustment and forecasting cannot be used since the number of banking days per calendar month varies and because there are two levels of seasonality: between months and within months. We propose a daily time series model based on unobserved components that allows for the classic decomposition into trend, seasonal plus irregular, but it also includes components for intra-monthly, trading-day and length-of-month effects. Such components typically rely on stochastic cubic spline, polynomial and dummy variable functions. State space techniques are used for the recursive computation of the likelihood and forecasts functions with special allowance for irregular spacing. The model is operational for daily forecasting at the Dutch Ministry of Finance. We present the model specification and discuss estimation and forecasting results up to December 1999. A comparative forecast evaluation is also presented.     

政府规模、政府支出增长与经济增长关系的非线性研究

本文运用面板平滑转换回归模型(PSTR),在非线性的框架下对政府规模与经济增长关系的渐进演变展开深入研究,并对可能引发两者关系结构性转变的警戒政府规模进行有效估算。研究结果表明,政府规模与经济增长之间存在着非线性关系,即随着政府规模的逐步增大,由于税负增加等因素的影响,使得政府支出增加所产生的负效应影响逐步凸显,而政府规模进一步扩大并超过警戒水平时,过度拥挤的政府支出对经济增长将由促进作用转变为阻碍作用。研究还发现,尽管我国政府支出增长与经济增长的关系参数值有所下降,但由于基础设施落后,公共物品与公共服务供给仍然相对不足,政府支出的增加仍有助于促进经济的进一步发展。     

Estimating Transition Probabilities from a Time Series of Independent Cross Sections

This paper considers the implementation of a nonstationary, heterogeneous Markov model for the analysis of a binary dependent variable in a time series of independent cross sections. The model, previously considered by M offitt (1993), offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time-constant and time-varying covariates on the hazards. We show how ML estimates of the parameters can be obtained by Fisher's method-of-scoring and how to estimate both fixed and time-varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch women using data from the Socio-economic Panel (SEP) study conducted in the Netherlands between 1986 and 1995. We treat the panel data as independent cross sections and compare the employment status sequences predicted by the model with the observed sequences in the panel. Some open problems concerning the application of the model are also discussed.     

Autocorrelation and Bias in Short Time Series: An Alternative Estimator

The conventional first-order autocorrelationcoefficient r₁ generates an empiricalbias when it is applied to short time series.The properties of this estimator have beenexamined with a Monte Carlo simulation studyusing the MATLAB program (version5.2). This study also analyzes the functionof the empirical bias with the polynomicregression and derives a polynomic fittingmodel for different sample sizes. In thisway, a new estimator that has been correctedby the absolute value of the fitting model(r_1') is proposed. Having analyzed thestatistical properties of the estimator r_1',it is shown that the empirical bias generatedby r_1' is less in relationship to r₁ andr₁₊. The results of the study make itpossible to verify that the mean squared errorassociated to the estimator r₁ isless than that of r₁. Thus, the coefficient r_1'is recommended to estimate the lag-oneautocorrelation coefficient in samples under 50observations.     

向量分整序列非线性变换的研究

自Granger提出整数阶单整向量序列的协整概念以来,协整理论在国内外学者的共同努力下不断得到完善。关于向量时间序列的分数维单整的研究主要集中在分整的线性协整的存在性条件及性质研究。文中通过引入交换条件数学期望算法(ACE),研究分整时间序列的非线性变换的协整性,对最优非线性变换函数进行估计。     

金融发展与经济增长的非线性关联研究——基于门限模型的实证检验

本文对Odedokun(1996)的理论模型进行了扩展,并利用多元门限模型对我国金融发展与经济增长之间的非线性关联进行了考察。实证结果表明金融发展和经济增长之间呈现出显著的非线性关联,这就否定了通常将两者关系设定为线性的研究模式。进一步的分析表明单纯在总量关系上讨论我国金融发展和经济增长关联性的意义并不明显,这启示我们在日后的研究中应该更加重视金融发展作用于经济增长的途径和机制问题。     

An Updated Literature Review of Distance Correlation and Its Applications to Time Series

The concept of distance covariance/correlation was introduced recently to characterise dependence among vectors of random variables. We review some statistical aspects of distance covariance/correlation function, and we demonstrate its applicability to time series analysis. We will see that the auto‐distance covariance/correlation function is able to identify non‐linear relationships and can be employed for testing the i.i.d. hypothesis. Comparisons with other measures of dependence are included.     

Short Time Series Analysis: C Statistic vs Edgington Model

Young's C statistic (1941) makes it possible to compare the randomization of a set of sequentially organized data and constitutes an alternative of appropriate analysis in short time series designs. On the other hand, models based on the randomization of stimuli are also very important within the behavioral content applied. For this reason, a comparison is established between the C statistic and the Edgington model. The data analyzed in the comparative study have been obtained from graphs in studies published in behavioral journals. According to the results obtained, it is concluded that the Edgington model in experimental designs AB involves many measurements while the C statistic requires fewer observations to reach the conventional significance level.     

Time Series Analysis of Repeated Surveys: The State–space Approach

Cross sectional estimates from repeated surveys form a time series { y_t }. These estimates can be viewed as the sum y _t = Y _t + e _t of two processes, { Y _t }, the population process and { e _t }, the survey error process. Serial correlations in the latter series are usually present, mainly due to sample overlap. Other sources of data such as censuses, administrative records and demographic population counts are also available. The state–space modelling approach to the analysis of repeated surveys allows combining information from different sources, incorporating benchmarking constraints in a natural way. Results from these methods seem to compare favourably with those from X-11-ARIMA in filtering out survey errors.     

中国金融发展与全要素生产率——基于时间序列的经验证据

全要素生产率问题是研究中国经济可持续增长的核心问题。本文利用界限检验法、基于ARDL法的协整系数估计、向量误差修正模型及其Granger因果关系检验等计量技术,考察了全要素生产率与金融发展等变量的关系。实证结果表明,全要素生产率与金融发展、外商直接投资、经济自由度三个变量存在长期均衡关系,并且从长期来看,金融发展、外商直接投资、经济自由度皆是全要素生产率变动的原因;而从短期来看,只有经济自由度才是全要素生产率变动的Granger原因。     

Comparing Time Series Cross-Section Model Specifications: The Case of Welfare State Development

In recent years, an impressive number of pooled time series (TSCS) cross-section models have been estimated in order to test hypotheses on welfare state development. Although most of these models share several of the variables, they can often be distinguished by the model specification adopted. This begs the question: what is the appropriate specification for modeling welfare state development? In order to answer this question some leading specifications are evaluated with respect to their ability to meet the theoretical assumptions about the theory of welfare state evolution in addition to the econometric canons on panel analysis. The main conclusions of this paper are the following. First, all specifications in levels are econometrically unfounded because most of the variables typically used for analyzing this topic cannot be considered to be stationary. Second, although a first difference model performs better from an econometric point of view, it is unable to test the hypothesized long-term relationships underlying welfare state dynamics. Third, and more importantly, the single equation error correction model represents the best pooled TSCS specification for modeling welfare state development since it is able tocapture long-run effects even in the presence of nonstationary processes.     

Advances in the Analysis of Non-stationary Time Series: An Illustration of Cointegration and Error Correction Methods in Research on Crime and Immigration

Time series data of interest to social scientists often have the property of random walks in which the statistical properties of the series including means and variances vary over time. Such non-stationary series are by definition unpredictable. Failure to meet the assumption of stationarity in the process of analyzing time series variables may result in spurious and unreliable statistical inferences. This paper outlines the problems of using non-stationary data in regression analysis and identifies innovative solutions developed recently in econometrics. Cointegration and error-correction models have recently received positive attention as remedies to the problems of ``spurious regression' arising from non-stationary series. In this paper, we illustrate the relevant statistical concepts concerning these methods by referring to similar concepts used in cross-sectional analysis. An historical example is used to demonstrate how such techniques are applied. It illustrates that ``foreign' immigrants to Canada (1896–1940) experienced elevated levels of social control in areas of high police discretion. ``Foreign' immigration was unrelated to trends in serious crimes but closely related to vagrancy and drunkenness. The merits of cointegration are compared to traditional approaches to the regression analysis of time series.