信贷政策效应的非对称性、信贷扩张与经济增长

 本文从银行贷款的角度出发,运用马尔可夫区制转移向量自回归模型（MS-VAR）对1992～2008年间的信贷增长率与经济增长率以及通货膨胀率的关系分别进行总量研究。检验结果表明,我国的经济增长路径和通货膨胀路径均有明显的三区制特征;各区制的持续期、转移概率均存在非对称的特点;信贷增长率与经济增长率以及通货膨胀率在各区制的同期相关系数明显不同,并且表现出结构性变化。     

中国通货膨胀率的非线性动态行为研究——来自MS—ARFIMA模型的新证据

使用允许长记忆参数d服从区制转换的MS—ARFIMA模型对中国月度通货膨胀路径的动态行为进行新的实证研究,结果显示：中国通货膨胀不仅均值水平和不确定性存在着“低通胀”区制和“高通胀”区制,而且更为重要的是,通货膨胀序列的平稳性也表现出显著的区制转换动态。“低通胀”区制下,长记忆参数d1=0．361,说明通货膨胀是协方差平稳序列,“高通胀”区制下,长记忆参数d2=1．145,说明通货膨胀是非平稳序列。这一新的研究结论意味着中国通货膨胀冲击的持久性效应也存在相应的区制转移变化。这要求央行在管控通货膨胀过程中,既要考虑均值和不确定性的区制变化,又要兼顾平稳性和持久性的区制变化。     

我国通货膨胀运动阶段的非线性平滑转换

本文分别构建了两、三以及四机制C-STAR模型来研究我国通货膨胀的非线性运动特征。实证结果表明：我国通胀率是平稳的非线性均值回归过程,依据三机制模型划分的通缩、通缩-通胀中间态以及通胀的三阶段能很好地刻画我国通货膨胀的运动特点;但是把我国通胀率划分为通缩、通胀适中、温和通胀和高通胀的四阶段能进一步提高模型的解释与拟合能力。我们详细阐述了通货膨胀在不同阶段的转换特点以及持久性与不确定性特征,发现通货膨胀的持久性与不确定性成反向非线性关系,与水平值的大小没有必然联系。在温和通胀阶段,其持久性最强,不确定性最小;在通胀适中阶段,其持久性最弱,不确定性最大。另外,温和通胀阶段也是最优通胀目标区间,为了保持经济的稳定增长,央行应把通胀率控制在该区间内。最后,本文给出了实证结果所蕴含的政策涵义。     

基于ARCH类模型的我国通货膨胀率波动性分析

通货膨胀和通货紧缩总是在经济中交替出现,在不同阶段通货膨胀率的波动程度也有差异.文章利用GARCH、EGARCH和TGARCH模型对我国通货膨胀率的波动特征进行了实证分析.结果表明正态分布下的GARCH和t分布下的EGARCH模型拟合效果较好,外部冲击对通货膨胀率的波动具有持久性影响.     

分位数单位根检验的拓展及其应用研究

在不同的分位下对时间序列数据是否含有结构性变化进行甄别,对于准确识别数据的动态变化及其分布特征具有重要的意义.本文首次在Koenker和Xiao(2004)研究的基础上提出傅立叶分位数单位根检验模型,并以此捕捉时间序列中存在的结构突变点,进而刻画数据在不同分位下的动态变化特征.本文通过构建傅立叶QKS统计量并采用蒙特卡罗方法对傅立叶分位数模型的临界值、样本容量和检验“势”进行模拟,发现含有傅立叶级数的分位数单位根检验对刻画“尖峰厚尾”特征数据的非线性偏离动态调节特征具有更高的检验“势”.最后,本文利用拓展后的模型对我国通货膨胀的持久性和失业的回滞效应进行再检验,结果发现我国通货膨胀具有平稳的特征,而失业率却包含单位根过程.本研究为分位数单位根检验的拓展及其应用提供了一定的启示.     

门限自回归下的单位根检验可靠性研究

在非对称的门限自回归模型下,由于传统单位根检验式的误设,会导致单位根检验势下降。本文通过一系列的Monte-Carlo模拟表明：非对称性对ADF和PP检验的检验势会产生较大影响,而对其他四种常用的单位根检验势产生的影响较小,也就是说,在非对称的门限自回归下,非对称性对退势单位根检验势产生的影响较小。模拟中也发现：NP单位根检验对TAR模型和持久性都具有稳健性。     

非线性协整检验与“费雪效应”机制分析

本文分别在线性Engle-Granger协整模型和非线性指数平滑迁移自回归误差修正模型 (ESTAR-ECM) 的框架下,对我国名义利率与通货膨胀率序列进行了长期均衡关系的检验。发现线性协整模型不能捕捉到我国名义利率与通货膨胀率的长期均衡关系,而对于ESTAR-ECM模型,无论利用商业银行1年期贷款利率还是7天期银行间同业拆借利率作为名义利率的代理变量,均证实名义利率与通货膨胀率具有长期稳定的均衡关系,表明“费雪效应”在我国是成立的。但由于“费雪效应”系数小于1,表明名义利率与通货膨胀率之间仅存在弱的“费雪效应”。其意义在于,我国利率政策对稳定通胀预期、抑制通货膨胀具有一定的正面效应,但由于利率对通货膨胀反应不足,导致完全依靠利率政策控制目前较高的通货膨胀有一定的困难。     

通货膨胀的宏观经济影响因素分析

本文首先利用贝叶斯向量自回归（BVAR）模型,分析了通货膨胀对宏观经济的冲击响应及其剧烈程度。然后,利用门限模型验证了通货膨胀在不同变量作为门限变量情况下的门限效应。结果表明：通货膨胀对六个因素冲击的反应程度各异,其中,对流动性过剩的反应程度最强,对股票价格、产出缺口和国际油价的反应适度,对实际有效汇率和房价的反应较弱;股票价格、汇率和国际油价具有明显的门限特征,它们分别将通货膨胀分为高低两种区制状态。以上结果有利于我们更好地认识通货膨胀的反应机制,采用合理的经济政策应对通货膨胀。     

中国通货膨胀、通货膨胀波动与产出增长：基于MGARCH模型分析

通货膨胀、通货膨胀波动和产出增长及其波动之间存在复杂的影响关系。基于一种多元自回归条件异方差模型（MGARCH模型）,采用中国1993—2003年的月度通货膨胀率和产出增长数据检验通货膨胀、通货膨胀波动和产出增长及其波动的关系。结论表明：高通货膨胀水平引起高的通货膨胀波动,而高通货膨胀率和通货膨胀波动导致低的产出增长和产出增长波动,结论的政策含义是价格稳定的货币政策有利于经济健康发展。     

中国的通货膨胀及其不确定性

采用1990年1月以来居民消费价格指数(CPI)的月度数据,运用随机域回归模型、系列随机域的非线性检验方法和贝叶斯估计方法,对中国通货膨胀率与通货膨胀不确定性的关系进行了实证分析。研究发现:通货膨胀率与通货膨胀之间具有双向关系。通货膨胀率引起了通货膨胀不确定性,两者呈现U型关系;较高的通货膨胀不确定性引起通货膨胀率先升后降,呈现倒U曲线关系。     

Tests for Unit-Root versus Threshold Specification With an Application to the Purchasing Power Parity Relationship

We consider modeling the real exchange rate by a stationary three-regime self-exciting threshold autoregressive (SETAR) model with possibly a unit root in the middle regime. This representation is consistent with purchasing power parity in the presence of trading costs. Our main contribution is to provide statistical tools for testing unit root versus a SETAR. First, we show that a SETAR with a unit root in the middle regime is stationary and mixing under reasonable assumptions. Second, we derive analytically the asymptotic distribution of our unit-root test under the null. Using monthly real exchange rate data, our test rejects the null of unit-root against a threshold process for five European series.     

A new nonlinear unit root test with Fourier function

Abstract

Traditional unit root tests display a tendency to be nonstationary in the case of structural breaks and nonlinearity. To eliminate this problem this paper proposes a new flexible Fourier form nonlinear unit root test. This test eliminates this problem to add structural breaks and nonlinearity together to the test procedure. In this test procedure, structural breaks are modeled by means of a Fourier function and nonlinear adjustment is modeled by means of an exponential smooth threshold autoregressive (ESTAR) model. The simulation results indicate that the proposed unit root test is more powerful than the Kruse and KSS tests.     

Unit-Root Tests and Asymmetric Adjustment With an Example Using the Term Structure of Interest Rates

This article develops critical values to test the null hypothesis of a unit root against the alternative of stationarity with asymmetric adjustment. Specific attention is paid to threshold and momentum threshold autoregressive processes. The standard Dickey–Fuller tests emerge as a special case. Within a reasonable range of adjustment parameters, the power of the new tests is shown to be greater than that of the corresponding Dickey–Fuller test. The use of the tests is illustrated using the term structure of interest rates. It is shown that the movements toward the long-run equilibrium relationship are best estimated as an asymmetric process.     

Control charts based on dependent count data with deflation or inflation of zeros

The process of serially dependent counts with deflation or inflation of zeros is commonly observed in many applications. This paper investigates the monitoring of such a process, the first-order zero-modified geometric integer-valued autoregressive process (ZMGINAR(1)). In particular, two control charts, the upper-sided and lower-sided CUSUM charts, are developed to detect the shifts in the mean process of the ZMGINAR(1). Both the average run length performance and the standard deviation of the run length performance of these two charts are investigated by using Markov chain approaches. Also, an extensive simulation is conducted to assess the effectiveness or performance of the charts, and the presented methods are applied to two sets of real data arising from a study on the drug use.     

Testing for coefficient stability of AR(1) model when the null is an integrated or a stationary process

In this paper, we propose a new test for coefficient stability of an AR(1) model against the random coefficient autoregressive model of order 1 neither assuming a stationary nor a non-stationary process under the null hypothesis of a constant coefficient. The proposed test is obtained as a modification of the locally best invariant (LBI) test by Lee [(1998). Coefficient constancy test in a random coefficient autoregressive model. J. Statist. Plann. Inference 74, 93–101]. We examine finite sample properties of the proposed test by Monte Carlo experiments comparing with other existing tests, in particular, the LBI test by McCabe and Tremayne [(1995). Testing a time series for difference stationary. Ann. Statist. 23 (3), 1015–1028], which is for the null of a unit root process against the alternative of a stochastic unit root process.     

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 inference and forecasting in the stationary bilinear model

A stationary bilinear (SB) model can be used to describe processes with a time-varying degree of persistence that depends on past shocks. This study develops methods for Bayesian inference, model comparison, and forecasting in the SB model. Using monthly U.K. inflation data, we find that the SB model outperforms the random walk, first-order autoregressive AR(1), and autoregressive moving average ARMA(1,1) models in terms of root mean squared forecast errors. In addition, the SB model is superior to these three models in terms of predictive likelihood for the majority of forecast observations.