Testing for Flexible Nonlinear Trends with an Integrated or Stationary Noise Component

This paper proposes a new test for the presence of a nonlinear deterministic trend approximated by a Fourier expansion in a univariate time series for which there is no prior knowledge as to whether the noise component is stationary or contains an autoregressive unit root. Our approach builds on the work of Perron and Yabu ( 2009a ) and is based on a Feasible Generalized Least Squares procedure that uses a super‐efficient estimator of the sum of the autoregressive coefficients α when α = 1. The resulting Wald test statistic asymptotically follows a chi‐square distribution in both the I(0) and I(1) cases. To improve the finite sample properties of the test, we use a bias‐corrected version of the OLS estimator of α proposed by Roy and Fuller ( 2001 ). We show that our procedure is substantially more powerful than currently available alternatives. We illustrate the usefulness of our method via an application to modelling the trend of global and hemispheric temperatures.     

Testing for Panel Cointegration with Multiple Structural Breaks*

This paper proposes a Lagrange multiplier (LM) test for the null hypothesis of cointegration that allows for the possibility of multiple structural breaks in both the level and trend of a cointegrated panel regression. The test is general enough to allow for endogenous regressors, serial correlation and an unknown number of breaks that may be located at different dates for different individuals. We derive the limiting distribution of the test and conduct a small Monte Carlo study to investigate its finite sample properties. In our empirical application to the solvency of the current account, we find evidence of cointegration between saving and investment once a level break is accommodated.     

Seasonal Unit Root Tests with Structural Breaks in Deterministic Seasonality

The seasonal root tests of Hylleberg et al (1990) are extended using the sequential approach of Zivot and Andrews (1992). This paper presents Monte Carlo evidence to support a sequential approach to estimation and critical values are estimated. It is demonstrated that non-stationary data with structurally unstable deterministic seasonality can lead to low power in standard tests for seasonal roots. The sequential tests are applied to US agricultural price data and macroeconomic data and compared with the standard tests. Seasonal roots are rejected in all series.     

ADF与PP单位根检验法对非线性趋势平稳序列的伪检验

ADF与PP单位根检验对无趋势或线性趋势平稳过程可给出正确的结果。但蒙特卡罗实验表明,对非线性趋势而言,它们趋向于将平稳过程误判为有单位根。在一定条件下,各种非线性趋势可以看成准线性的,从而利用ADF与PP检验得出正确的结论。信噪比小于15倍时,PP检验可得出正确的结果;信噪比小于4倍时,ADF检验可得出正确的结果。对非线性趋势平稳序列的检验而言,PP优于ADF检验。随着干扰相对强度的增加,正确检验的可能性也大大增加。     

Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors

Asymptotic distributions and critical values are computed for several residual-based tests of the null of no cointegration in panels for the case of multiple regressors, including regressions with individual-specific fixed effects and time trends. The associated cointegrating vectors and the dynamics of the underlying error processes are permitted considerable heterogeneity across individual members of the panel.     

Testing for Hysteresis in Unemployment in OECD Countries: New Evidence using Stationarity Panel Tests with Breaks*

This paper tests hysteresis effects in unemployment using panel data for 19 Organization for Economic Co‐operation and Development (OECD) countries covering the period 1956–2001. The tests exploit the cross‐sectional variations of the series, and additionally, allow for a different number of endogenous breakpoints in the unemployment series. The critical values are simulated based on our specific panel sizes and time periods. The findings stress the importance of accounting for exogenous shocks in the series and support the natural‐rate hypothesis of unemployment for the majority of the countries analysed.     

PRACTITIONERS CORNER: Tests for Cointegration in Models with Regime and Trend Shifts

ABSTRACT Recently Gregory and Hansen (1996) proposed a number of residual-based tests for cointegration in models with the possibility of a structural break. They considered three models: (i) level shift; (ii) level shift with trend; and (iii) regime shift (both level shift and slope coefficients can change). We introduce a more general model that permits a trend shift as well as a regime shift and we provide the critical values appropriate for testing this hypothesis.     

Power of Tests for Unit Roots in the Presence of a Linear Trend*

Dickey and Fuller [Econometrica (1981) Vol. 49, pp. 1057–1072] suggested unit‐root tests for an autoregressive model with a linear trend conditional on an initial observation. TPower of tests for unit roots in the presence of a linear trendightly different model with a random initial value in which nuisance parameters can easily be eliminated by an invariant reduction of the model. We show that invariance arguments can also be used when comparing power within a conditional model. In the context of the conditional model, the Dickey–Fuller test is shown to be more stringent than a number of unit‐root tests motivated by models with random initial value. The power of the Dickey–Fuller test can be improved by making assumptions to the initial value. The practitioner therefore has to trade‐off robustness and power, as assumptions about initial values are hard to test, but can give more power.     

含结构突变的趋势平稳过程的虚假单位根研究

本文发现Perron（1989）在趋势突变情形下的结论“统计量的极限分布会随着突变点位置参数的变化收敛在0到1/2之间”值得商榷,原因在于模型设定中出现了错误,导致在结构突变的趋势平稳过程的数据生成过程下,统计量的极限分布在截距突变的情况下发散而在斜率突变的情况下退化。本文对其进行修正并补充推导了三种含结构突变的趋势平稳过程的单位根检验统计量的分布,并给出能够证实和证伪的蒙特卡洛模拟结果。     

A Simple Test for Cointegration in Dependent Panels with Structural Breaks*

This paper develops a very simple test for the null hypothesis of no cointegration in panel data. The test is general enough to allow for heteroskedastic and serially correlated errors, unit‐specific time trends, cross‐sectional dependence and unknown structural breaks in both the intercept and slope of the cointegrated regression, which may be located at different dates for different units. The limiting distribution of the test is derived, and is found to be normal and free of nuisance parameters under the null. A small simulation study is also conducted to investigate the small‐sample properties of the test. In our empirical application, we provide new evidence concerning the purchasing power parity hypothesis.     

Trend and Initial Condition in Stationarity Tests: The Asymptotic Analysis

In this article, we investigate the behaviour of stationarity tests proposed by Müller [Journal of Econometrics (2005) Vol. 128, pp. 195–213] and Harris et al. [Econometric Theory (2007) Vol. 23, pp. 355–363] with uncertainty over the trend and/or initial condition. As different tests are efficient for different magnitudes of local trend and initial condition, following Harvey et al. [Journal of Econometrics (2012) Vol. 169, pp. 188–195], we propose decision rule based on the rejection of null hypothesis for multiple tests. Additionally, we propose a modification of this decision rule, relying on additional information about the magnitudes of the local trend and/or the initial condition that is obtained through pre‐testing. The resulting modification has satisfactory size properties under both uncertainty types.     

综合物流时代现代港口的发展趋势

现代港口作为全球综合运输最关键的节点,发挥着日益重要的作用,并且为适应综合物流时代要求,出现大型化、深水化、集装箱化、高效高科技化、信息网络化及成为综合物流服务中心的发展趋势。     

Testing the Null of Cointegration with Structural Breaks*

We propose a Lagrange Multiplier‐type statistic to test the null hypothesis of cointegration allowing for the possibility of a structural break, in both the deterministic and the cointegration vectors. Our proposal focuses on the presence of endogenous regressors. The test complements the usual non‐cointegration tests so as to obtain stronger evidence of cointegration. We consider the cases of known and unknown dates of the break. In the latter case, we show that minimizing the Sum of Squared Residuals results in a super‐consistent estimator of the break fraction. Finally, the behaviour of the tests is studied through Monte Carlo experiments.     

Testing for Fractional Integration Versus Short Memory with Structural Breaks*

Although it is commonly accepted that most macroeconomic variables are non‐stationary, it is often difficult to identify the source of the non‐stationarity. Integrated processes and short‐memory models with trending components, possibly affected by structural breaks, imply similar features in the data and, accordingly, are hard to distinguish. The goal of this article is to extend the classical testing framework of I(1) versus I(0) + trends and/or breaks by considering a more general class of models under the null hypothesis: fractionally integrated (FI) processes. The asymptotic properties of the proposed tests are derived and it is shown that they are very well‐behaved in finite samples. An illustration using US inflation data is also provided.     

一种结构突变面板数据单位根的联合检验

本文根据Banerjee等(1992)的模型变换方法和内生突变点选择原理,得到了一种纵剖面时间序列相互独立面板数据的内生结构突变单位根的联合检验——■_T~*(λ)检验。并且,基于泛函中心极限定理推导出了该检验统计量的渐近分布;通过蒙特卡洛模拟实验讨论了该检验的实际检验水平以及面板数据的样本大小、异质性、截距突变的幅度、斜率突变的幅度和结构突变位置等因素对检验功效的影响。最后,根据1952～2006年中国27个省份的数据讨论了人均实际收入的稳定性。     

Tests for changing mean with monotonic power

Several widely used tests for a changing mean exhibit nonmonotonic power in finite samples, due to “incorrect” estimation of nuisance parameters under the alternative. In this paper, we study the issue of nonmonotonic power in testing for changing mean. We investigate the asymptotic power properties of the tests, using a new framework where alternatives are characterized as having “large” changes. The asymptotic analysis provides a theoretical explanation to the power problem. Modified tests that have monotonic power against a wide range of alternatives of structural change are proposed. Instead of estimating the nuisance parameters based on ordinary least squares residuals, the proposed tests use modified estimators, based on nonparametric regression residuals. It is shown that tests based on the modified long-run variance estimator provide an improved rate of divergence of the tests under the alternative of a change in mean. Tests for structural breaks based on such an estimator are able to remain consistent, while still retaining the same asymptotic distribution under the null hypothesis of constant mean.     

Testing for Unit Roots with Breaks: Evidence on the Great Crash and the Unit Root Hypothesis Reconsidered

Nelson and Plosser (1982), in a classic paper, failed to find strong evidence against the null hypothesis of a generating process with a unit autoregressive root for thirteen US macroeconomic time series. Perron (1989) claimed that such evidence was available for a majority of these series if the alternative hypothesis was of trend stationarity with a break in 1929. Zivot and Andrews (1992) treated the break date as endogenous, then finding strong evidence agcainst the null for a minority of these series. Our own analysis extends theirs by permitting a break under the null as well as the alternative hypothesis, and allowing for the sequential nature of the testing. Our empirical findings complete the circle. We find no strong evidence against the unit root hypothesis for any of the thirteen Nelson–Plosser series.     

斯柯达汽车2010年全球销量创新纪录

2010年,斯柯达汽车创下公司史上最佳销售业绩,全球汽车销量达762,600辆（2009年为684,200辆）,同比增长达11．5％,首次冲破75万销量大关。斯柯达品牌在中国市场的表现尤为抢眼,2010年再度刷新销量纪录,年销量超过18万辆,同比增长47％。     

中国城乡融合发展的演进、反思与趋势

城市与乡村是一个有机体,城乡关系是最基本的经济社会关系,也是国家和区域内最重要的相互依赖关系,只有两者均实现可持续发展,才能相互支撑。如何防止“空壳化”及其带来的农村萧条,党的十九大报告明确提出建立健全城乡融合发展体制机制和政策体系,这是中央文件首次提出城乡融合发展的概念,也是实施乡村振兴战略、加快推进农业农村现代化的根本保障。因此,将城市和乡村置于“人口—产业—土地”整体性的网络中重新审视两者的逻辑关系,科学分析研判城乡发展的演进过程,系统甄别城乡融合机理特征,有效把握城乡融合发展的趋势规律,构建城乡融合与乡村振兴这一新型解释框架意义重大。