混频Granger因果关系检验的功效和稳健性分析

研究目标：分析混频Granger因果关系检验的功效及其稳健性。研究方法：以货币与产出间的因果关系为例,比较分析不同情形下,混频和同低频Granger因果关系检验功效的差异性和检验结果的稳健性。研究发现：货币与产出间的因果关系不是一成不变的,混频Granger因果关系检验的水平和功效随着不同层次的货币供给、模型滞后阶数、预测期长度,变量的多寡,以及样本量的大小和所处的经济阶段的变化而呈现异质性和非对称性。研究创新：将混频技术与Granger因果关系结合起来,从不同角度全面系统地检验了货币与产出的因果关系,并分析了该方法的稳健性。研究价值：进行货币政策调控时,须评估不同情形下货币政策的作用效果,选取恰当的时机,采用更加合理的调控手段和方法。     

Systemic financial risk early warning of financial market in China using Attention-LSTM model

We propose an Attention-LSTM neural network model to study the systemic risk early warning of China. Based on text mining, the network public opinion index is constructed and used as a training set to be incorporated into the early warning model to test the early warning effect. The results show that: (i) the network public opinion is the non-linear Granger causality of systemic risk. (ii) The Attention-LSTM neural network has strong generalization ability. Early warning effects have been significantly improved. (iii) Compared with the BP neural network model, the SVR model and the ARIMA model, the LSTM neural network early warning model has a higher accuracy rate, and its average prediction accuracy for systemic risk indicators has been improved over short, medium and long terms. When the attention mechanism is included in the LSTM, the Attention-LSTM neural network model is even more accurate in all the cases.     

Markov switching causality and the money–output relationship

The causal link between monetary variables and output is one of the most studied issues in macroeconomics. One puzzle from this literature is that the results of causality tests appear to be sensitive with respect to the sample period that one considers. As a way of overcoming this difficulty, we propose a method for analysing Granger causality which is based on a vector autoregressive model with time‐varying parameters. We model parameter time‐variation so as to reflect changes in Granger causality, and assume that these changes are stochastic and governed by an unobservable Markov chain. When applied to US data, our methodology allows us to reconcile previous puzzling differences in the outcome of conventional tests for money–output causality. Copyright © 2005 John Wiley & Sons, Ltd.     

Testing for Granger Causality in Moments

In this paper, we consider a generalized approach which is flexibly applicable to testing Granger causality in various moments and in both the full‐sample and out‐of‐sample contexts. We further use this approach to establish a class of cross‐correlation tests for financial time series analysis, and show the advantages of this class of tests in unifying and generalizing Box–Pierce‐type Granger causality tests. We also conduct a Monte Carlo simulation to show the validity of our tests, and provide an empirical example to demonstrate the flexibility of our tests in exploring various types of Granger causality.     

Renewable and non-renewable energy consumption,environmental degradation and economic growth in Tunisia

The aim of this document is to investigate the dynamic relationship between economic growth, renewable energy consumption, energy consumption and CO₂ emissions in Tunisia over the period 1990–2015. Unit root tests and co-integration test was used in order to detect the order of stationary and to test the existence long run links between the used variables. We apply the Granger causality test and VECM model to discover the short and long run links between the variables. Results have shown a bidirectional causal relationship between energy use and CO₂ emissions. Economic growth affects CO₂ emission in the short and long run. While there is a unidirectional links running from energy use to economic growth at short run. The paper shares best practices from Tunisia in terms of efficient use of renewable energy policy enablers, which may be contextualized in other emerging economies in order to keep sustainability and to achieve the green economy.     

OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach

This paper provides a novel perspective to the predictive ability of OPEC meeting dates and production announcements for (Brent Crude and West Texas Intermediate) oil futures market returns and GARCH-based volatility using a nonparametric quantile-based methodology. We show a nonlinear relationship between oil futures returns and OPEC-based predictors; hence, linear Granger causality tests are misspecified and the linear model results of non-predictability are unreliable. When the quantile-causality test is implemented, we observe that the impact of OPEC variables is restricted to Brent Crude futures only (with no effect observed for the WTI market). Specifically, OPEC production announcements, and meeting dates predict only lower quantiles of the conditional distribution of Brent futures market returns. While, predictability of volatility covers the majority of the quantile distribution, barring extreme ends.     

Nonlinear Granger Causality: Guidelines for Multivariate Analysis

We propose an extension of the bivariate nonparametric Diks–Panchenko Granger non‐causality test to multivariate settings. We first show that the asymptotic theory for the bivariate test fails to apply to the multivariate case, because the kernel density estimator bias and variance cannot both tend to zero at a sufficiently fast rate. To overcome this difficulty we propose to reduce the order of the bias by applying data sharpening prior to calculating the test statistic. We derive the asymptotic properties of the ‘sharpened’ test statistic and investigate its performance numerically. We conclude with an empirical application to the US grain market, using the price of futures on heating degree days as an additional conditioning variable. Copyright © 2015 John Wiley & Sons, Ltd.     

交叉网络外部性在证券交易所定价中的应用分析

本文利用双边市场理论对证券交易所进行实证检验和模型研究,采用格兰特因果检验方法对上海交易所数据进行了实证分析,研究结果表明相比上市公司而言,投资者是“鸡蛋相生”问题的关键点,因此根据实证结果提出扩大投资者规模是证券市场发展的关键所在.然后针对上市公司和投资者的自网络外部性特征及投资者内部之间交易的特点,采用模型研究得出较强的负自网络外部性使得交易所会降低上市公司的注册费来吸引公司;当注册费太高时,交易所会降低交易费来保留住投资者,反之亦然.     

Statistical inference for continuous-time Markov processes with block structure based on discrete-time network data

A widely used approach to modeling discrete-time network data assumes that discrete-time network data are generated by an unobserved continuous-time Markov process. While such models can capture a wide range of network phenomena and are popular in social network analysis, the models are based on the homogeneity assumption that all nodes share the same parameters. We remove the homogeneity assumption by allowing nodes to belong to unobserved subsets of nodes, called blocks, and assuming that nodes in the same block have the same parameters, whereas nodes in distinct blocks have distinct parameters. The resulting models capture unobserved heterogeneity across nodes and admit model-based clustering of nodes based on network properties chosen by researchers. We develop Bayesian data-augmentation methods and apply them to discrete-time observations of an ownership network of non-financial companies in Slovenia in its critical transition from a socialist economy to a market economy. We detect a small subset of shadow-financial companies that outpaces others in terms of the rate of change and the desire to accumulate stocks of other companies.     

Can economic development boost the active female labor force?

This study explores whether the female labor force participation rate (FLFPR) can promote economic development in Asian countries. We apply the method of bootstrap panel Granger causality in order to consider the cross-sectional dependency and heterogeneity to detect specific patterns of the interactive relationships between the two variables. The estimation results point out that the interaction patterns vary across countries, which is consistent with the U-shaped hypothesis. Specifically, when the economy develops, FLFPR declines in Vietnam and India, whereas economic development promotes the FLFPR in Korea, Malaysia, Singapore and Thailand. More specifically, when GDP per capita lies at a relatively low level, the income effect dominates the substitution effect, resulting into the FLFPR changing in opposite direction. Nevertheless, the substitution effect holds the dominant position when the GDP per capita reaches a high level. The increase of FLFPR is accompanied by economic development due to availability of more jobs and increasing level of education. Therefore, policy makers should formulate plans in order to benefit from the potential of the female labor force by stimulating economic development.

     

Multi-scale interactions between economic policy uncertainty and oil prices in time-frequency domains

Dynamic interactions between policy uncertainty and economic activity, including oil prices, have attracted increasing amounts of scholarly interest, but few studies have considered the inherent feature that the entire market is composed of different stakeholders operating in different time horizons. To fill this gap and address this issue, this paper proposes a multi-scale correlation framework. Specifically, we use the wavelet coherence method and scale-by-scale linear Granger causality tests to explore the co-movement and causality of pairs of economic policy uncertainty indices of G7 countries, China, Brazil, and Russia and West Texas Intermediate (WTI) oil prices. Our results show that the interaction between economic policy uncertainty and oil prices in the short-term is weak but gradually strengthens towards the long-term, especially when significant historical political or financial events occurred. Moreover, a consistent conclusion is that the interaction is negative in the medium-term, while it is positive in the long-term. Further, Granger causality tests at different time-scales show that no Granger causality from economic policy uncertainty to oil prices exists in the short-term for all sample countries, except the US, while there is a strong unidirectional or bidirectional Granger causality for all researched countries in the medium- and the long-term.     

Measuring and testing Granger causality over the spectrum: An application to European production expectation surveys

Decomposing Granger causality over the spectrum allows us to disentangle potentially different Granger causality relationships over different frequencies. This may yield new and complementary insights compared to traditional versions of Granger causality. In this paper, we compare two existing approaches in the frequency domain, proposed originally by Pierce [Pierce, D. A. (1979). R-squared measures for time series. Journal of the American Statistical Association, 74, 901–910] and Geweke [Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association, 77, 304–324], and introduce a new testing procedure for the Pierce spectral Granger causality measure. To provide insights into the relative performance of this test, we study its power properties by means of Monte Carlo simulations. In addition, we apply the methodology in the context of the predictive value of the European production expectation surveys. This predictive content is found to vary widely with the frequency considered, illustrating the usefulness of not restricting oneself to a single overall test statistic.     

Granger因果关系检验应用中的若干问题

分析了实践中应用Granger因果关系检验存在的一些问题,如信息遗漏,变量变换改变因果关系的性质、变量单整性对检验程序的影响以及检验模型的选择等,并提出在线性投影上有初步证据的因果概念。进而,应用单整变量之间Granger因果关系的一般检验程序对1978~2013年我国货币供给量与价格水平、产出之间的Granger因果性重新进行检验。     

中国外汇储备和物价水平关系的实证分析

本文采用1994～2010年的年度数据,在单整和协整检验的基础上,利用Granger因果检验对中国外汇储备与物价水平的内在联系进行实证分析。结果表明,外汇储备变动和物价水平变动之间不存在格兰杰因果关系。因此.通过人民币升值来减少外汇储备并不是抑制国内通货膨胀的有效方法。     

Interactions between investors’ fear and greed sentiment and Bitcoin prices

This paper examines the relationship between investor fear in the cryptocurrency market and Bitcoin prices by considering the potential effects of the ongoing COVID-19 pandemic during the period of May 5, 2018 and December 10, 2020. The existence of structural changes in the time series for the full sample reveals a non-constant causality between fear sentiment and Bitcoin prices, which leads us to apply a bootstrap rolling window Granger causality test. Our results show that both negative and positive interactions between fear sentiment and Bitcoin prices occur during several subperiods. The nature of these interactions changes significantly before and during the pandemic. Thus, we contribute to the fast-growing literature on the financial effects of the COVID-19 global pandemic, as well as to the debate on whether to classify Bitcoin as a new asset, speculative investment, currency, or safe haven asset.     

An international study of causality-in-variance: Interest rate and financial sector returns

We demonstrate that causality-in-variance test could be employed to model the direction and lags in information flow between two variables and to avoid misspecifications. We apply this methodology to test the causality between the financial sector returns and interest rates of the G7 countries and show that the direction and the lead/lag structure of causality in the mean and the variance are more complex and dynamic than that have previously been reported. In most cases, we found two-way information flow both at the mean and the volatility level. Causality results give us insights into (i) how and when information is impacted on different market segments, and (ii) design more objective bi-variate models with the appropriate lag structure.     

Trade networks and economic fluctuations in Asian countries

The paper presents a new methodology, based on tensor decomposition, to map dynamic trade networks and to assess its strength in forecasting economic fluctuations at different periods of time in Asia. Using the monthly merchandise import and export data across 33 Asian economies, together with the US, EU and UK, we detect the community structure of the evolving network and we identify clusters and central nodes inside each of them. Our findings show that data are well represented by two communities, in which People's Republic of China and Japan play the major role. We then analyze the synchronisation between GDP growth and trade. Furthermore we apply our model to the prediction of economic fluctuations. Our findings show that the model leads to an increase in predictive accuracy, as higher order interactions between countries are taken into account.     

浙江省区域物流与经济增长的实证研究

基于浙江省1990~2012年统计数据,运用ADL模型及格兰杰因果检验对浙江省区域物流与经济增长的相互关系进行了研究。结果表明:从长期来看,浙江省GDP、全社会货物周转量及港口货物吞吐量三者之间存在着单向的因果关系。港口货物吞吐量是全社会货物周转量增加的格兰杰原因,全社会货物周转量和港口货物吞吐量平均每增长1%,分别带动浙江省GDP增长0.8672%和0.9903%。     

农村金融生态环境与金融效率指标体系构建及互动分析

分别建立农村金融生态环境和金融效率评价指标体系,并从理论上分析二者的互动机制。然后运用Granger因果检验法,对1985-2008年农村金融生态环境和金融效率关系进行实证分析,结果显示二者存在Granger因果关系,建议通过优化农村金融生态环境来促进金融效率提高,并带来自身进一步改善。     

Housing prices and transaction volume

We use annual, quarterly and monthly data from the US to show that the correlation between housing prices and transaction volume (number of existing houses sold) differs across different frequencies. While the correlation is high at the low frequencies it declines to the levels close to zero at high frequencies. Granger causality tests for different frequencies show that the way of causality in housing market changes from region to region. Our findings provide a litmus test for the existing theories that are proposed to explain the positive correlation between transaction volume and housing prices.