基于STAR模型的中国货币政策对产出影响的非对称性研究

对中国GDP增长率建立以可预期到的货币冲击、未预期到的正向货币冲击和未预期到的负向货币冲击滞后三期为转移变量的LSTAR模型,拟合效果良好,分析不同类型的货币冲击对产出的非线性和非对称性影响,给出可预期到的货币冲击、未预期到的正向货币冲击和未预期到的负向货币冲击的阀值,分别为20.03%、2%和1.58%,说明不同类型的货币冲击对产出呈现不同的非对称性影响,强弱机制的转换区间存在差异,且负向货币冲击的阀值小于正向货币冲击的阀值。研究结果表明中国的货币政策存在显著的非线性和非对称性特征,且紧缩性货币政策比扩张性货币政策更有效。     

Business cycle asymmetry and duration dependence: an international perspective

The business cycle behaviour of macroeconomic variables has long been of interest to economists, and attention has recently focused on two aspects of this behaviour - the 'stylized facts' of cyclical asymmetry and duration dependence. Cyclical asymmetry is where the economy behaves differently over the expansion and recession phases of the business cycle. Duration dependence, on the other hand, concerns the question of whether, for example, the probability of a cyclical expansion is dependent on how long the expansion has been running, or whether business cycle lengths tend to cluster around a particular duration. Using an international data set containing annual output per capita for 22 countries, we focus attention on non-parametric techniques for extracting cyclical components and for modelling and testing asymmetry and duration dependence. Once outliers, primarily associated with wars, are omitted, there is little international evidence of asymmetry. There is considerably more evidence of duration dependence, which is detected in the majority of countries using a variety of non-parametric tests. There is thus widespread evidence against the constant hazard hypothesis that cyclical patterns occur simply by chance. Business cycle durations do appear to cluster around certain values, with the average duration being about 3.6 years.     

Simulation-based estimation of threshold moving average models with contemporaneous shock asymmetry and an application to Turkish business cycles

Persistence of shocks to economic time series may differ depending on the sign of the shock or on a threshold value. Threshold moving average (TMA) models, by explicitly taking into account threshold behavior, can help discriminate whether there exists persistence asymmetry. This article considers TMA models in which both contemporaneous and lagged asymmetric effects are both present and examines the properties of simulation-based efficient method of moments estimation using Monte Carlo simulation experiments. The model is applied to analyze the persistence properties of shocks to growth rates of gross domestic product and industrial production index in Turkish economy.     

经济周期中产出波动与通货膨胀不对称动态关系研究

建立不对称动态菲利普斯曲线理论研究经济周期中产出波动与通货膨胀不对称动态关系。该理论蕴含了经济扩张与收缩期中通胀持续性、产出波动对通胀的长短期影响差异特征及相关检验方法。运用该理论对中国相关季度数据进行了实证分析,结果表明:产出波动对通货膨胀短期中具有"顺周期"的正相关性,充当了"晴雨器"作用;长期中具有"逆周期"的负相关性,充当了"稳定器"作用。统计检验表明,经济周期中通货膨胀持续性及产出波动对通货膨胀的长短期影响具有显著不对称性,这种不对称性是中国经济转型期经济运行质量的历史检验,对现阶段追求经济增长质量具有深刻的政策启示。     

GDP数据修正对经济周期测定的影响

 本文在收集和建立季度GDP实时数据的基础上,利用区制转移模型对我国经济周期测定展开了实时数据分析,并深入讨论了GDP数据修正对经济周期阶段性的影响。研究结果表明GDP数据修正引致我国经济周期阶段性发生了深刻的变化,使我国经济周期在2005Q2至2006Q4间从低速增长改变为高速增长。而且,我们发现GDP数据修正对经济周期平均增长率的影响方向,与其是否影响经济周期阶段性具有相反关系,即当GDP数据修正不影响 (影响) 经济周期阶段性时,它对高、低平均增长率都具有同向 (反向) 影响。     

Business-Cycle Phases and Their Transitional Dynamics

This article examines differences in expansionary and contractionary phases of the business cycle. By extending the nonlinear Markov-switching estimation method of Hamilton to incorporate time-varying probabilities of transitions between the phases, the marginal benefits of the time-varying transition probability Markov-switching model are highlighted. Using this technique, I document the high correlation between the evolution of the phases inferred from the model and traditional reference cycles for monthly output data. Many of the economic variables that determine the time-varying probabilities help to predict turning points. The predictive power of standard leading indicators is evaluated and compared.     

全球价值链嵌入与国际经济周期非对称联动

剔除第三方效应测度双边价值链嵌入度,考察其与国际经济周期非对称联动间关联。研究发现:融入全球价值链使中国制造业陷入低增值率出口与中间投入高比例进口的困境,进而削弱了中国与贸易伙伴国经济扩张的联动,加强了中国与贸易伙伴国经济紧缩的联动,这在一定程度上导致国际经济周期呈扩张联动低于紧缩联动的非对称特征。该现象在金融危机前突出,危机后有所缓解但仍存在。相对而言,融入全球价值链大幅削弱了中国与经济增长较为稳健的发展中国家、非欧盟国家经济扩张的联动,又大幅加强了中国与发达国家、欧盟国家等危机多发国经济紧缩的联动,影响中国经济稳定增长。要规避全球价值链嵌入对稳增长的阻碍,中国须提升制造业出口国内增加值份额,并着力降低中间投入进口比例。本文发现了国际经济周期联动的非对称特征,并基于中国融入全球价值链的典型特征解释成因,研究结论可为制定实施稳增长政策提供决策参考。     

粮食价格波动对物价水平的非对称影响——基于STR模型的实证分析

依据中国1998年1月-2011年6月的季度数据,运用平滑转换回归模型研究粮食价格波动对物价水平的影响,研究表明:粮食价格对物价水平存在单向Granger因果关系,其影响呈现非线性和不对称特征;当粮食价格处于高机制状态时,粮价波动对物价水平影响程度较大,CPI自我驱动能力较小;当粮食价格处于低机制状态时,粮价波动对物价水平影响程度微弱,CPI自我驱动能力较强.     

中国潜在产出估算模型的设计与应用——基于Kalman滤波的实证分析

在传统滤波法仅对实际GDP变量进行趋势和周期成分分解的基础上,从中国国情出发,考虑国际联系、货币政策、通货膨胀预期等影响因素,构建多参数动态系统模型进行Kalman滤波,估算中国1978-2009年潜在产出、产出缺口、潜在产出增长率及均衡增长率。在此基础上,讨论模型在中国经济周期阶段划分、长期增长趋势与可接受增长区间及拐点预测等方面的应用,为制定经济政策提供参考依据。     

Dynamic Asymmetries in U.S. Unemployment

We examine dynamic asymmetries in U.S. unemployment using nonlinear time series models and Bayesian methods. We find strong statistical evidence in favor of a two-regime threshold auto-regressive model. Empirical results indicate that, once we take into account both parameter and model uncertainty, there are economically interesting asymmetries in the unemployment rate. One finding of particular interest is that shocks that lower the unemployment rate tend to have a smaller effect than shocks that raise the unemployment rate. This finding is consistent with unemployment rises being sudden and falls gradual.     

Nonlinear semiparametric autoregressive model with finite mixtures of scale mixtures of skew normal innovations

We propose data generating structures which can be represented as the nonlinear autoregressive models with single and finite mixtures of scale mixtures of skew normal innovations. This class of models covers symmetric/asymmetric and light/heavy-tailed distributions, so provide a useful generalization of the symmetrical nonlinear autoregressive models. As semiparametric and nonparametric curve estimation are the approaches for exploring the structure of a nonlinear time series data set, in this article the semiparametric estimator for estimating the nonlinear function of the model is investigated based on the conditional least square method and nonparametric kernel approach. Also, an Expectation–Maximization-type algorithm to perform the maximum likelihood (ML) inference of unknown parameters of the model is proposed. Furthermore, some strong and weak consistency of the semiparametric estimator in this class of models are presented. Finally, to illustrate the usefulness of the proposed model, some simulation studies and an application to real data set are considered.     

A dynamic double asymmetric copula generalized autoregressive conditional heteroskedasticity model: application to China's and US stock market

Modeling the relationship between multiple financial markets has had a great deal of attention in both literature and real-life applications. One state-of-the-art technique is that the individual financial market is modeled by generalized autoregressive conditional heteroskedasticity (GARCH) process, while market dependence is modeled by copula, e.g. dynamic asymmetric copula-GARCH. As an extension, we propose a dynamic double asymmetric copula (DDAC)-GARCH model to allow for the joint asymmetry caused by the negative shocks as well as by the copula model. Furthermore, our model adopts a more intuitive way of constructing the sample correlation matrix. Our new model yet satisfies the positive-definite condition as found in dynamic conditional correlation-GARCH and constant conditional correlation-GARCH models. The simulation study shows the performance of the maximum likelihood estimate for DDAC-GARCH model. As a case study, we apply this model to examine the dependence between China and US stock markets since 1990s. We conduct a series of likelihood ratio test tests that demonstrate our extension (dynamic double joint asymmetry) is adequate in dynamic dependence modeling. Also, we propose a simulation method involving the DDAC-GARCH model to estimate value at risk (VaR) of a portfolio. Our study shows that the proposed method depicts VaR much better than well-established variance–covariance method.     

Bayesian inference of multiple structural change models with asymmetric GARCH errors

Structural change in any time series is practically unavoidable, and thus correctly detecting breakpoints plays a pivotal role in statistical modelling. This research considers segmented autoregressive models with exogenous variables and asymmetric GARCH errors, GJR-GARCH and exponential-GARCH specifications, which utilize the leverage phenomenon to demonstrate asymmetry in response to positive and negative shocks. The proposed models incorporate skew Student-t distribution and prove the advantages of the fat-tailed skew Student-t distribution versus other distributions when structural changes appear in financial time series. We employ Bayesian Markov Chain Monte Carlo methods in order to make inferences about the locations of structural change points and model parameters and utilize deviance information criterion to determine the optimal number of breakpoints via a sequential approach. Our models can accurately detect the number and locations of structural change points in simulation studies. For real data analysis, we examine the impacts of daily gold returns and VIX on S&P 500 returns during 2007–2019. The proposed methods are able to integrate structural changes through the model parameters and to capture the variability of a financial market more efficiently.

     

经济增速放缓与经济波动对居民福利影响的阶段差异分析

 本文基于中国1952-2007年时序数据定量研究经济增速放缓的福利损失和经济波动的福利损失,并侧重考察两种福利损失的大小关系在改革开放和经济体制改革目标确立前后的阶段差异。研究发现,无论是总体而言,还是在中国经济发展的不同阶段,经济波动的福利损失并不必然远小于经济增速放缓的福利损失,在相关参数的合理取值范围内,经济波动的福利损失大于经济增速放缓的福利损失是相当普遍的情形。因此,中国政府部门在重视长期经济增长的同时,不能草率否定短期经济稳定的重要性。     

ASSESSING AND TESTING FOR THRESHOLD NONLINEARITY IN STOCK RETURNS

This paper proposes a test for threshold nonlinearity in a time series with generalized autore‐gressive conditional heteroscedasticity (GARCH) volatility dynamics. This test is used to examine whether financial returns on market indices exhibit asymmetric mean and volatility around a threshold value, using a double‐threshold GARCH model. The test adopts the reversible‐jump Markov chain Monte Carlo idea of Green, proposed in 1995, to calculate the posterior probabilities for a conventional GARCH model and a double‐threshold GARCH model. Posterior evidence favouring the threshold GARCH model indicates threshold nonlinearity with asymmetric behaviour of the mean and volatility. Simulation experiments demonstrate that the test works very well in distinguishing between the conventional GARCH and the double‐threshold GARCH models. In an application to eight international financial market indices, including the G‐7 countries, clear evidence supporting the hypothesis of threshold nonlinearity is discovered, simultaneously indicating an uneven mean‐reverting pattern and volatility asymmetry around a threshold return value.     

Fitting probability distributions to economic growth: a maximum likelihood approach

The growth rate of the gross domestic product (GDP) usually carries heteroscedasticity, asymmetry and fat-tails. In this study three important and significantly heteroscedastic GDP series are examined. A Normal, normal-mixture, normal-asymmetric Laplace distribution and a Student's t-Asymmetric Laplace (TAL) distribution mixture are considered for distributional fit comparison of GDP growth series after removing heteroscedasticity. The parameters of the distributions have been estimated using maximum likelihood method. Based on the results of different accuracy measures, goodness-of-fit tests and plots, we find out that in the case of asymmetric, heteroscedastic and highly leptokurtic data the TAL-distribution fits better than the alternatives. In the case of asymmetric, heteroscedastic but less leptokurtic data the NM fit is superior. Furthermore, a simulation study has been carried out to obtain standard errors for the estimated parameters. The results of this study might be used in e.g. density forecasting of GDP growth series or to compare different economies.     

Comment

Using postwar annual data through 1987 from 46 countries, we confirm our earlier finding that the maximum impact (χ) of monetary shocks on real output is negatively correlated across countries with the variance of such shocks (σ ) [the Lucas proposition (LP)]. This holds whether the time series specification for each country is the one we reported in Kormendi and Meguire (1984) (KM), one selected by a Bayesian pretest (BPT) suggested by Poirier's results, or a uniform specification that nests both. Using the LP to restrict the coefficients of monetary shocks in the real output equation significantly improves forecasts of real output growth over the period 1978–1987. Over the same period, predictions of money and real output growth made from the BPT specifications often do not outperform comparable predictions made from the KM specifications.     

On time-irreversibility and other non-linear features in time series

Time-irreversibility, asymmetry of the distribution, and the occurrence of sudden bursts are considered, amongst others, as non-linear features in time series modeling. The implication is often made that time series showing these features must be analyzed using non-linear models. In contrast, this paper shows that time-irreversible asymmetric time series showing certain types of sudden bursts may be generated by linear models with adequate input sequences. Thus some non-linear time series features may be caused by the pattern in the input sequence rather than by non-linearity in the model. Examples are considered to illustrate the situation.     

Bayesian quantile regression for parametric nonlinear mixed effects models

We propose quantile regression (QR) in the Bayesian framework for a class of nonlinear mixed effects models with a known, parametric model form for longitudinal data. Estimation of the regression quantiles is based on a likelihood-based approach using the asymmetric Laplace density. Posterior computations are carried out via Gibbs sampling and the adaptive rejection Metropolis algorithm. To assess the performance of the Bayesian QR estimator, we compare it with the mean regression estimator using real and simulated data. Results show that the Bayesian QR estimator provides a fuller examination of the shape of the conditional distribution of the response variable. Our approach is proposed for parametric nonlinear mixed effects models, and therefore may not be generalized to models without a given model form.     

Inference on Filtered and Smoothed Probabilities in Markov-Switching Autoregressive Models

ABSTRACT

We derive a statistical theory that provides useful asymptotic approximations to the distributions of the single inferences of filtered and smoothed probabilities, derived from time series characterized by Markov-switching dynamics. We show that the uncertainty in these probabilities diminishes when the states are separated, the variance of the shocks is low, and the time series or the regimes are persistent. As empirical illustrations of our approach, we analyze the U.S. GDP growth rates and the U.S. real interest rates. For both models, we illustrate the usefulness of the confidence intervals when identifying the business cycle phases and the interest rate regimes.