A revisitation of the export-led growth hypothesis in Malaysia using the leveraged bootstrap simulation and rolling causality techniques

According to the neoclassical growth theory, export expansion could stimulate economic growth because it promotes specialisation and raises factor productivity. Thus, many developing countries depend heavily on export-orientated businesses to accelerate economic growth. Nevertheless, the causality evidences on the export-led growth hypothesis remain elusive and controversial. Two primary empirical questions emerged in the international trade and development literatures are: (a) Does the export-led growth hypothesis still valid? (b) Why causality evidences are inconsistent among studies? In light of these, the present study attempts to contribute to the export-led growth literature by using the Malaysian data set. This study covers the monthly data set from January 1975 to August 2010. To achieve the objectives of this study, we employ the leveraged bootstrap simulation causality test and also the rolling regression-based causality tests. The leveraged bootstrap simulation causality results suggest that exports and output growth are bilateral causality in nature. However, the rolling causality results demonstrate that the causality inferences for export-led growth hypothesis are unstable over time. For this reason, policy initiative to promote exports may not always stimulate economic growth and development in Malaysia. Therefore, balancing policy is urged to ensure that the economic growth in Malaysia can be materialised.     

人民币汇率、出口贸易与经济增长——基于中美、中日双边贸易的比较分析

以中美、中日1994—2007年的双边贸易数据为基础,建立人民币汇率、出口贸易和经济增长的联立方程模型,研究三者之间的作用机制。研究结果表明：人民币汇率贬值通过扩大出口促进经济增长,而经济增长和出口导致人民币实际汇率升值,符合巴拉萨效应;出口与经济增长之间的ELG假说成立,而GLE假说不成立;中美贸易的参数检验效果明显优于中日贸易。为此,应采取稳定出口,促进消费、投资和出口协调发展;优化贸易结构,提升贸易竞争力;合理控制人民币波动幅度,加快人民币国际化进程等政策措施。     

国际商业周期对中国出口的影响

 国际商业周期变化对中国出口有着怎样的影响？本文在重力方程中引入不可观测的周期性因素,用于分析中国出口的变化及其决定因素。与已有研究相比,本文允许周期性因素的影响具有个体异质性,因此更具一般性,也更符合典型贸易事实。在此基础上,本文使用Pesaran (2006)的异质性面板、Bai（2003）的共同因子等新提出方法分别估计重力方程和周期性因素。结果表明,中国出口具有明显的供给推动特征,国内产出是引起出口增长的最主要因素;国外产出和汇率是导致出口波动的重要因素;周期性因素的影响因贸易伙伴而异,中国对美国、日本等贸易伙伴的出口受周期性影响较为明显。进一步分析发现,金融危机对中国出口的负面冲击在2009年还将进一步显现。     

Estimation of structural impulse responses: short-run versus long-run identifying restrictions

There is evidence that estimates of long-run impulse responses of structural vector autoregressive (VAR) models based on long-run identifying restrictions may not be very accurate. This finding suggests that using short-run identifying restrictions may be preferable. We compare structural VAR impulse response estimates based on long-run and short-run identifying restrictions and find that long-run identifying restrictions can result in much more precise estimates for the structural impulse responses than restrictions on the impact effects of the shocks.     

Testing Wagner's Law in India: A cointegration and causality analysis

The present study empirically analyzes the validity of Wagner's Law for Indian economy. With the use of annual time series data from 1970–71 to 2013–14, all the six versions of Wagner's Law have been analyzed to test the relationship between government expenditure and gross domestic product. Wagner's Law states that the economic growth is the causative factor of the growth of the public expenditure. The study applied the unit root test and cointegration test to find the long-run relationship between government expenditure and gross domestic product. The present study employed the various econometric techniques such as unit root test, cointegration, and causality analysis for empirical analysis. The empirical analysis under study inferred mixed results of Wagner's Law for Indian economy, where four versions, namely Peacock, Gupta, Guffman, and Musgrave, found valid for Indian economy. The study concluded that the Wagner's Law is valid for the Indian economy except the Pryor and Mann Versions of the Wagner's Law.     

Panel—Data下Granger因果检验的理论和应用发展综述

Panel-Data下Granger因果检验的相关理论是最近几年才发展起来的,现有的研究提出了关于Panel-Data下Granger因果检验的四个基本假设:同质无因果关系假设(HNCH)、同质因果关系假设(HCH)、异质因果关系假设(HECH)以及异质无因果关系假设(HENCH),根据检验参数的特点给出三种类型的检验模型:固定系数模型、随机系数模型和混合固定随机系数模型。目前,还只有固定系数模型的相关理论较为完善,另外两种模型的检验还都存在一定的难度。因此,只有从理论研究和实际应用两个方面对该理论进行阐述,并对现有的理论进行简要的评述,才可指出其存在的不足及可能的改进方向。     

Estimation of Short-Run and Long-Run Elasticities of Energy Demand From Panel Data Using Shrinkage Estimators

This article discusses the problem of obtaining short-run and long-run elasticities of energy demand for each of 49 states in the United States using data for 21 years. Estimation using the time series data by each state gave several wrong signs for the coefficients. Estimation using pooled data was not valid because the hypothesis of homogeneity of the coefficients was rejected. Shrinkage estimators gave more reasonable results. The article presents in a unified framework the classical, empirical Bayes, and Bayes approaches for deriving these estimators.     

正确认识出口贸易对中国经济增长的贡献

  出口贸易自改革开放以来在中国经济的发展中一直扮演着重要角色,出口贸易对中国经济发展的影响究竟有多大是本文关注的主要议题。本文在原始竞争型投入产出表、只分离出进口的非竞争型投入产出表以及同时分离出进口及加工贸易的非竞争型投入产出表的基础上建立了测算三大需求因素对GDP形成与增长的三类投入产出表模型,并通过2007年中国投入产出表进行了测算。研究表明,如果采用竞争型投入产出表及其投入产出模型,仍不能较准确地测算消费、投资与出口对GDP形成与增长的贡献;分离出进口产品的非竞争型投入产出表给出了一个较好的分析框架,但由于未区分加工贸易和普通贸易,出口贡献存在高估现象;而采用区分加工贸易和普通贸易的非竞争投入产出模型,能够较客观地反映国内产品出口与加工贸易出口对GDP形成与增长的实际贡献。     

中国股票市场新股长期价格行为研究

文章考察了沪深市场1997年1月1日至2001年6月30日间上市的A股股票在上市后三年内的收益状况,发现中国股票市场上的新股表现出显著的短期弱势、长期强势特征;与自编指数相比较,若以中信综指、中信风格指数为市场基准,将造成对大盘、中盘和小盘新股长期收益状况的认识偏差;在考察了相关因素对新股长期收益状况的影响后发现,上市首日抑价水平和换手率较低的新股具有较高的投资价值,而抑价水平和换手率较高的新股投资价值较低。     

Do Flow Rates Respond Asymmetrically to Water Level? Evidence from the Edwards Aquifer

This research examines the time series relationship between the Comal Springs flow rate and the water level in the Edwards Aquifer (Well J-17). The empirical methodology utilizes threshold autoregression (TAR) and momentum-TAR models that allow for asymmetry in responses and adjustments to a disequilibrium in the long-run cointegrating relationship. Based on the results, an asymmetric error-correction model (AECM) is proposed to characterize the short-run and long-run dynamic relationship between spring flow and water level. The results have implications for the management of water resources, water demand, and ecosystems.     

Estimation of long-run inefficiency levels: a dynamic frontier approach

Cornwell, Schmidt, and Sickles (1990) and Kumbhakar (1990), among others, developed stochasticfrontier production models which allow firm specific inefficiency levels to change over time. These studies assumed arbitrary restrictions on the short-run dynamics of efficiency levels which have little theoretical justification. Further, the models are inappropriate for estimation of long-run efficiencies. We consider estimation of an alternative frontier model in which firmspecific technical inefficiency levels are autoregressive. This model is particularly useful to examine a potential dynamic link between technical innovations and production inefficiency levels. We apply our methodology to a panel of US airlines.     

Estimation of long-run inefficiency levels: a dynamic frontier approach

Cornwell, Schmidt, and Sickles (1990) and Kumbhakar (1990), among others, developed stochasticfrontier production models which allow firm specific inefficiency levels to change over time. These studies assumed arbitrary restrictions on the short-run dynamics of efficiency levels which have little theoretical justification. Further, the models are inappropriate for estimation of long-run efficiencies. We consider estimation of an alternative frontier model in which firmspecific technical inefficiency levels are autoregressive. This model is particularly useful to examine a potential dynamic link between technical innovations and production inefficiency levels. We apply our methodology to a panel of US airlines.     

Comment: The Future of Administrative Records in the Economic Programs of the Census Bureau

Using recently developed statistical tools for analyzing cointegrated 1(2) data, this article models money, income, prices, and interest rates in Denmark. The final model describes the dynamic adjustment to short-run changes of the process, to deviations from long-run steady states, and to several political interventions. It provides new insights about the effects of the liberalization of trade and capital in a small open European economy.     

汇率与出口换汇成本关系的实证分析

一、引言一般而言,西方各主要货币的汇率是由外汇市场的供求关系自发决定的。而我国的人民币汇率则是国家根据经济形势和政策的需要来制定和调整的。特别是改革开放以来,汇率制度改革是中央政府改善国际收支状况的主要政策选择。直至１９９４年汇率并轨前,汇率政策的目...     

出口隐含碳排放的产业分布及优化对策研究

文章依据非竞争型投入产出表,在剔除进口中间投入所节省的能耗和碳排放的基础上,对2002-2011年各产业出口隐含的CO2排放量重新进行了测算。结果表明,我国出口隐含的国内碳排放量比同类文献的测算结果要低;而且,某些碳排放强度高的产业其出口隐含碳排放的占比并不高,相反,某些碳排放强度低的产业其出口隐含碳排放的占比却较高。这种反差主要是由于不同产业的出口规模不同造成的。因此,要减少出口隐含的碳排放量,不仅要鼓励节能和清洁技术的研发,降低各产业的能耗强度和碳排放强度,还应逐步压缩高耗能、高排放产业的产能和出口规模,不断优化出口结构。     

Vector autoregression modelling and forecasting growth of South Korea

In this paper, we have estimated vector autoregression (VAR), Bayesian vector autoregression (BVAR) and vector error-correction models (VECMs) using annual time-series data of South Korea for 1950-94. We find evidence supporting the view that growth of real per-capita income has been aided by income, investment and export growth, as well as government spending and exchange rate policies. The VECMs provide better forecasts of growth than do the VAR and BVAR models for both short-term and long-term predictions.     

Conditional Correlation Models of Autoregressive Conditional Heteroscedasticity With Nonstationary GARCH Equations

In this article, we investigate the effects of careful modeling the long-run dynamics of the volatilities of stock market returns on the conditional correlation structure. To this end, we allow the individual unconditional variances in conditional correlation generalized autoregressive conditional heteroscedasticity (CC-GARCH) models to change smoothly over time by incorporating a nonstationary component in the variance equations such as the spline-GARCH model and the time-varying (TV)-GARCH model. The variance equations combine the long-run and the short-run dynamic behavior of the volatilities. The structure of the conditional correlation matrix is assumed to be either time independent or to vary over time. We apply our model to pairs of seven daily stock returns belonging to the S&P 500 composite index and traded at the New York Stock Exchange. The results suggest that accounting for deterministic changes in the unconditional variances improves the fit of the multivariate CC-GARCH models to the data. The effect of careful specification of the variance equations on the estimated correlations is variable: in some cases rather small, in others more discernible. We also show empirically that the CC-GARCH models with time-varying unconditional variances using the TV-GARCH model outperform the other models under study in terms of out-of-sample forecasting performance. In addition, we find that portfolio volatility-timing strategies based on time-varying unconditional variances often outperform the unmodeled long-run variances strategy out-of-sample. As a by-product, we generalize news impact surfaces to the situation in which both the GARCH equations and the conditional correlations contain a deterministic component that is a function of time.     

The Multistep Beveridge–Nelson Decomposition

The Beveridge–Nelson decomposition defines the trend component in terms of the eventual forecast function, as the value the series would take if it were on its long-run path. The article introduces the multistep Beveridge–Nelson decomposition, which arises when the forecast function is obtained by the direct autoregressive approach, which optimizes the predictive ability of the AR model at forecast horizons greater than one. We compare our proposal with the standard Beveridge–Nelson decomposition, for which the forecast function is obtained by iterating the one-step-ahead predictions via the chain rule. We illustrate that the multistep Beveridge–Nelson trend is more efficient than the standard one in the presence of model misspecification, and we subsequently assess the predictive validity of the extracted transitory component with respect to future growth.     

A bayesian analysis of trend determination in economic time series

In this paper we provide a comprehensive Bayesian posterior analysis of trend determination in general autoregressive models. Multiple lag autoregressive models with fitted drifts and time trends as well as models that allow for certain types of structural change in the deterministic components are considered. We utilize a modified information matrix-based prior that accommodates stochastic nonstationarity, takes into account the interactions between long-run and short-run dynamics and controls the degree of stochastic nonstationarity permitted. We derive analytic posterior densities for all of the trend determining parameters via the Laplace approximation to multivariate integrals. We also address the sampling properties of our posteriors under alternative data generating processes by simulation methods. We apply our Bayesian techniques to the Nelson-Plosser macroeconomic data and various stock price and dividend data. Contrary to DeJong and Whiteman (1989a,b,c), we do not find that the data overwhelmingly favor the existence of deterministic trends over stochastic trends. In addition, we find evidence supporting Perron's (1989) view that some of the Nelson and Plosser data are best construed as trend stationary with a change in the trend function occurring at 1929.     

A bayesian analysis of trend determination in economic time series

In this paper we provide a comprehensive Bayesian posterior analysis of trend determination in general autoregressive models. Multiple lag autoregressive models with fitted drifts and time trends as well as models that allow for certain types of structural change in the deterministic components are considered. We utilize a modified information matrix-based prior that accommodates stochastic nonstationarity, takes into account the interactions between long-run and short-run dynamics and controls the degree of stochastic nonstationarity permitted. We derive analytic posterior densities for all of the trend determining parameters via the Laplace approximation to multivariate integrals. We also address the sampling properties of our posteriors under alternative data generating processes by simulation methods. We apply our Bayesian techniques to the Nelson-Plosser macroeconomic data and various stock price and dividend data. Contrary to DeJong and Whiteman (1989a,b,c), we do not find that the data overwhelmingly favor the existence of deterministic trends over stochastic trends. In addition, we find evidence supporting Perron's (1989) view that some of the Nelson and Plosser data are best construed as trend stationary with a change in the trend function occurring at 1929.