An efficiency Bayesian unit root test in Unobserved-ARCH models

This paper investigates the new prior distribution on the Unobserved-Autoregressive Conditional Heteroscedasticity (ARCH) unit root test. Monte Carlo simulations show that the sample size is seriously effective in efficiency of Bayesian test. To improve the performance of Bayesian test for unit root, we propose a new Bayesian test that is robust in the presence of stationary and nonstationary Unobserved-ARCH. The finite sample property of the proposed test statistic is evaluated using Monte Carlo studies. Applying the developed method, we test the policy of daily exchange rate of the German Marc with respect to the Greek Drachma.     

Phillips-Perron-type unit root tests in the nonlinear ESTAR framework

Summary In this paper, we propose Phillips-Perron type, semi-parametric testing procedures to distinguish a unit root process from a mean-reverting exponential smooth transition autoregressive one. The limiting nonstandard distributions are derived under very general conditions and simulation evidence shows that the tests perform better than the standard Phillips-Perron or Dickey-Fuller tests in the region of the null. We would like to thank conference participants of the Pfingsttagung 2005 in Münster for their helpful comments.     

Unit Roots: Bayesian Significance Test

The unit root problem plays a central role in empirical applications in the time series econometric literature. However, significance tests developed under the frequentist tradition present various conceptual problems that jeopardize the power of these tests, especially for small samples. Bayesian alternatives, although having interesting interpretations and being precisely defined, experience problems due to the fact that that the hypothesis of interest in this case is sharp or precise. The Bayesian significance test used in this article, for the unit root hypothesis, is based solely on the posterior density function, without the need of imposing positive probabilities to sets of zero Lebesgue measure. Furthermore, it is conducted under strict observance of the likelihood principle. It was designed mainly for testing sharp null hypotheses and it is called FBST for Full Bayesian Significance Test.     

A Bayesian method of distinguishing unit root from stationary processes based on panel data models with cross-sectional dependence

In this paper we develop a Bayesian approach to detecting unit roots in autoregressive panel data models. Our method is based on the comparison of stationary autoregressive models with and without individual deterministic trends, to their counterpart models with a unit autoregressive root. This is done under cross-sectional dependence among the error terms of the panel units. Simulation experiments are conducted with the aim to assess the performance of the suggested inferential procedure, as well as to investigate if the Bayesian model comparison approach can distinguish unit root models from stationary autoregressive models under cross-sectional dependence. The approach is applied to real exchange rate series for a panel of the G7 countries and to a panel of US nominal interest rates data.     

Bayesian Unit Root Testing in Unobserved-ARCH Models

This article uses a Bayesian unit-root test in Unobserved-ARCH models. This time series of interest is the volatility that is unobservable. The unit root testing is based on the posterior odds ratio, which is approximated by Markov Chain Monte Carlo methods. Simulations show that the testing procedure is efficient for moderate sample size. The unit-root hypothesis is rejected in the daily exchange rate of the Germany marc (DEM) with respect to the Greek Drachma.     

Bayesian tests for unit root and multiple breaks

A Bayesian approach is considered for identifying sources of nonstationarity for models with a unit root and breaks. Different types of multiple breaks are allowed through crash models, changing growth models, and mixed models. All possible nonstationary models are represented by combinations of zero or nonzero parameters associated with time trends, dummy for breaks, or previous levels, for which Bayesian posterior probabilities are computed. Multiple tests based on Markov chain Monte Carlo procedures are implemented. The proposed method is applied to a real data set, the Korean GDP data set, showing a strong evidence for two breaks rather than the usual unit root or one break.     

分位数单位根检验的有限样本绩效及其应用

利用蒙特卡洛模拟方法,在不同的数据产生过程下比较了分位数单位根检验与传统的ADF和PP单位根检验的绩效。研究发现:当误差项服从正态分布时,传统单位根检验与分位数单位根检验的检验功效相差不大,前者甚至略优于后者;但当误差项服从t分布时,分位数单位根要优于传统的单位根检验。在此基础上,采用中国商品价格指数(增长率)数据,给出分位数单位根检验的实例应用,实证结果显示中国商品价格指数具有非对称的惯性特征。     

Bayesian unit-root tests for Stochastic Volatility models

In this article, we consider Bayesian inference procedures to test for a unit root in Stochastic Volatility (SV) models. Unit-root tests for the persistence parameter of the SV models, based on the Bayes Factor (BF), have been recently introduced in the literature. In contrast, we propose a flexible class of priors that is non-informative over the entire support of the persistence parameter (including the non-stationarity region). In addition, we show that our model fitting procedure is computationally efficient (using the software WinBUGS). Finally, we show that our proposed test procedures have good frequentist properties in terms of achieving high statistical power, while maintaining low total error rates. We illustrate the above features of our method by extensive simulation studies, followed by an application to a real data set on exchange rates.     

平滑转换自回归模型的单位根检验问题研究

 内容提要：针对非线性模型的单位根检验中存在的问题,本文认为非线性模型的单位根检验不应该在AR模型中进行,而应该在非线性模型中进行。以LSTAR(1)模型为例,本文给出了在其中进行单位根检验的统计量及其临界值。用蒙特卡洛试验证实,本文提出的单位根检验统计量的功效明显高于DF单位根检验,只有当非平稳特征十分明显时,DF检验才能检测出其中的单位根,因此,在非线性模型中进行单位根检验是必要的。     

基于分形理论的LSTAR模型检验

针对非线性时间序列的单位根检验和非线性检验中存在的问题,引入分形理论中的Hurst指数进行研究,以LSTAR模型为代表,讨论了相关样本性质。通过蒙特卡洛模拟实验证实,只有当非平稳特征十分明显时,DF检验才能检测出单位根。Hurst指数改善了单位根检验功效,且作为一种非参数检验方法,具有较好的稳健性。     

The effects of additive outliers on the seasonal KPSS test: a Monte Carlo analysis

This article builds on the test proposed by Lyhagen [The seasonal KPSS statistic, Econom. Bull. 3 (2006), pp. 1–9] for seasonal time series and having the null hypothesis of level stationarity against the alternative of unit root behaviour at some or all of the zero and seasonal frequencies. This new test is qualified as seasonal-frequency Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test and it is not originally supported by a regression framework.

The purpose of this paper is twofold. Firstly, we propose a model-based regression method and provide a clear illustration of Lyhagen's test and we establish its asymptotic theory in the time domain. Secondly, we use the Monte Carlo method to study the finite-sample performance of the seasonal KPSS test in the presence of additive outliers. Our simulation analysis shows that this test is robust to the magnitude and the number of outliers and the statistical results obtained cast an overall good performance of the test finite-sample properties.     

趋势平稳过程与单位根过程的趋势特征研究

为了深入研究具有高次趋势特征序列的单位根(平稳性)检验问题,研究了高次趋势平稳过程和带高次趋势的单位根过程的概念及其时间趋势特征。结果表明,带漂移的单位根过程实际具有线性趋势,带k(k≥1)次趋势的单位根过程实际具有k+1次趋势;而k(k≥0)次(趋势)平稳过程则具有k次趋势。无论是趋势平稳过程,还是单位根过程,都可以通过差分变换确定其时间趋势特征。     

Testing stationarity and trend stationarity against the unit root hypothesis

In this paper we propose a family of relativel simple nonparametrics tests for a unit root in a univariate time series. Almost all the tests proposed in the literature test the unit root hypothesis against the alternative that the time series involved is stationarity or trend stationary. In this paper we take the (trend) stationarity hypothesis as the null and the unit root hypothesis as the alternative. The order differnce with most of the tests proposed in the literature is that in all four cases the asymptotic null distribution is of a well-known type, namely standard Cauchy. In the first instance we propose four Cauchy tests of the stationarity hypothesis against the unit root hypothesis. Under H1 these four test statistics involved, divided by the sample size n, converge weakly to a non-central Cauchy distribution, to one, and to the product of two normal variates, respectively. Hence, the absolute values of these test statistics converge in probability to infinity 9at order n). The tests involved are therefore consistent against the unit root hypothesis. Moreover, the small sample performance of these test are compared by Monte Carlo simulations. Furthermore, we propose two additional Cauchy tests of the trend stationarity hypothesis against the alternative of a unit root with drift.     

ADF单位根检验中联合检验LM统计量研究

 本文研究了ADF单位根检验中参数联合约束的拉格朗日乘数检验。首先,本文构建了4个LM统计量并推导了它们的极限分布;然后,运用蒙特卡罗试验,模拟了有限样本容量常用检验水平下的临界值,拟合了临界值关于样本容量的响应面函数,并总结了LM统计量有限样本容量下的统计特性;比较分析了这4个LM统计量的检验功效及实际检验水平;最后,一个实例分析简要说明了这几个统计量在单位根检验中的应用。     

A Multivariate Unit Root Test Based on the Modified Weighted Symmetric Estimator for VAR(p)

Multivariate unit root tests for the VAR model have been commonly used in time series analysis. Several unit root tests were developed. Most of the estimators of coefficient matrices developed in the VAR model are obtained using ordinary least squares estimators. In this paper, we suggest a multivariate unit root test based on a modified weighted symmetric estimator. Using a limited Monte Carlo simulation, we compare the powers of the new test statistic and the test statistic suggested in Fuller (1996).     

Generalized fixed‐T panel unit root tests

Panel data unit root tests, which can be applied to data that do not have many time series observations, are based on very restrictive error and deterministic component specification assumptions. In this paper, we develop a new, doubly modified estimator, based on which we propose a panel unit root test that allows for multiple structural breaks, linear and nonlinear trends, heteroscedasticity, serial correlation, and error cross‐section heterogeneity, when the number of time series observations is finite. The test has the additional perk that it is invariant to the initial condition.     

一种对初始值稳健的单位根检验及其应用

常见单位根检验方法对初始值都做了适当的约束,而经验研究中的数据往往由于各种冲击的存在无法满足相应的假定条件。所以,有必要讨论检验功效对初始值稳健的单位根检验方法。本文在研究初始值对单位根检验功效影响的基础上,基于Fisher统计量提出了检验功效关于初始值较稳健的组合p值单位根检验方法并研究了其小样本性质。并且,对我国CPI月环比时间序列的检验发现,随着我国宏观经济调控政策的完善,CPI逐渐趋于平稳。     

Ng—Perron单位根检验理论与应用——中国宏观经济序列的平稳性分析

从理论和实证两个角度对Ng—Perron单位根检验进行了系统的分析和阐述，并应用该检验研究了中国名义GDP、实际GDP和实际利率的平稳性。通过分析，以期Ng—Perron单位根检验能在实证分析中得到更为规范和广泛的应用。     

Testing a Unit Root Based on Aggregate Time Series

Many time series encountered in practice are nonstationary, and instead are often generated from a process with a unit root. Because of the process of data collection or the practice of researchers, time series used in analysis and modeling are frequently obtained through temporal aggregation. As a result, the series used in testing for a unit root are often time series aggregates. In this paper, we study the effects of the use of aggregate time series on the Dickey–Fuller test for a unit root. We start by deriving a proper model for the aggregate series. Based on this model, we find the limiting distributions of the test statistics and illustrate how the tests are affected by the use of aggregate time series. The results show that those distributions shift to the right and that this effect increases with the order of aggregation, causing a strong impact both on the empirical significance level and on the power of the test. To correct this problem, we present tables of critical points appropriate for the tests based on aggregate time series and demonstrate their adequacy. Examples illustrate the conclusions of our analysis.     

退势单位根检验的小样本性质

在经验研究中,尽管Dickey-Fuller提出的 统计量是应用最广泛的单位根检验,但是,它的检验功效偏低是众所周知的。为了改善Dickey-Fuller检验的功效,本文将时间序列的四种退势方法和 检验、 检验、MAX检验和 检验相结合,通过蒙特卡洛模拟试验研究了16种退势单位根检验的小样本性质。研究发现,退势单位根检验均不同程度地改善了 型检验的功效,特别是退势单位根检验 -KGLS、MAX-KGLS、 -RLS和MAX-RLS具有更理想的小样本性质。