Energy consumption and economic activities in Iran

The causal relationship between overall GDP, industrial and agricultural value added and consumption of different kinds of energy are investigated using vector error correction model for the case of Iran within 1967–2003. A long-run unidirectional relationship from GDP to total energy and bidirectional relationship between GDP and gas as well as GDP and petroleum products consumption for the whole economy was discovered. Causality is running from value added to total energy, electricity, gas and petroleum products consumption and from gas consumption to value added in industrial sector. The long-run bidirectional relations hold between value added and total energy, electricity and petroleum products consumption in the agricultural sector. The short-run causality runs from GDP to total energy and petroleum products consumption, and also industrial value added to total energy and petroleum products consumption in this sector.     

Testing for the Cointegrating Rank of a VAR Process with Level Shift and Trend Break

Abstract. A test for the cointegrating rank of a vector autoregressive (VAR) process with a possible shift and broken linear trend is proposed. The break point is assumed to be known. Our test is not a likelihood ratio test but the deterministic terms including the broken trends are removed first by a generalized least squares procedure. Then, a likelihood ratio‐type test is applied to the adjusted series. The asymptotic null distribution of the test is derived and it is shown by a Monte Carlo experiment that the test has better small‐sample properties in many cases than a corresponding Gaussian likelihood ratio test for the cointegrating rank. Moreover, response surface techniques can be used to easily obtain p‐values of the test for any possible break date.     

The relationship between energy and production: Evidence from Turkish manufacturing industry

This paper investigates the relationship between energy and production at the industry level in an emerging market, Turkey, in a multivariate framework. The electricity consumption and value added relation is examined in the Turkish manufacturing industry, while also accounting for labor and fixed investment. We find that labor, fixed investment, electricity consumption, and value added are related via three cointegrating vectors. The VEC results indicate uni-directional causality running from electricity consumption to value added. Generalized impulse response and variance decomposition analyses confirm these results. Thus, energy input appears to be closely related to production. Hence, energy saving technologies and increased energy efficiency may increase the growth in manufacturing value added.     

基于小波神经网络的非线性误差校正模型及其预测

针对非线性系统的预测问题,在线性和非线性协整理论涵义的基础上,提出利用小波神经网络进行非线性协整系统的非线性误差校正模型的研究,并给出该模型的建模方法．对沪深股市进行实证研究,与线性向量自回归模型进行比较．研究证明,小波神经网络所建立的非线性误差校正模型有较好的预测效果,能够有效地预测非线性经济系统．     

RECOGNIZING OVERDIFFERENCED TIME SERIES

Abstract. Differencing is often used to render a time series stationary. The decision of how much differencing to do is usually based on plots of data, the autocorrelation function or a statistical test. Hence, it may happen that an analyst mistakenly differences a stationary series. When that happens, the inverse autocorrelation function takes on a specific pattern. We characterize this pattern and discuss the behavior of sample estimates of the inverse autocorrelation function for such overdifferenced series.     

NONLINEAR TRANSFORMATIONS OF INTEGRATED TIME SERIES:A RECONSIDERATION

Abstract. In this paper I reconsider two of the questions raised by Granger and Hallman (Nonlinear transformations of integrated time series. J. Time Ser. Anal. 12 (1991), 207–24):(i) If X_t is I(1) and Z_t=h(X_t), is Z_t also I(1)? (ii) Can X_t and h(X_t) be cointegrated? The distinction between I(1) and I(0) processes is replaced by the distinction between long memory and short memory processes, where for short memory I mean strong mixing. By exploiting the fact that random walks (with positive trend component) are martingales (submartingales) and are also first-order Markov, I show that (a) unbounded convex (concave) and strictly monotonic transformations of random walks are always long memory processes, (b) polynomial, strictly convex (concave) transformations of random walks display a unit root component, but the first differences of such transformations need not be short memory, and (c) X_t and h(X_t), with h an unbounded convex (concave) or strictly monotonic function, can never be cointegrated.     

Bootstrapping the likelihood ratio cointegration test in error correction models with unknown lag order

The finite-sample size and power properties of bootstrapped likelihood ratio system cointegration tests are investigated via Monte Carlo simulations when the true lag order of the data generating process is unknown. Recursive bootstrap schemes are employed which differ in the way in which the lag order is chosen. The order is estimated by minimizing different information criteria and by combining the corresponding order estimates. It is found that, in comparison to the standard asymptotic likelihood ratio test based on an estimated lag order, bootstrapping can lead to improvements in small samples even when the true lag order is unknown, while the power loss is moderate.     

基于协整分析与误差修正模型的经济增长与能源消费关系的研究

选取1985年～2011年的数据,通过对内蒙古经济与能源消费的分析,运用协整检验、建立模型、Granger因果关系检验方法确定国民经济增长与能源消费的关系,得出分析结论：短期内能源消费与经济增长之间存在单向作用关系,长期来看,能源消费与经济增长存在相互作用的关系。结合分析结论并针对内蒙古的实际情况提出相应建议。     

马尔可夫协整回归模型的动态滤波估计

本文提出动态滤波估计方法估计马尔可夫协整回归模型的参数.利用领先和滞后方法构造辅助的动态回归模型,以消除解释变量和误差序列间的相关性以及误差自相关性对估计结果的影响.在Hamilton滤波基础上,应用极大似然方法估计辅助模型的参数.模拟计算结果表明动态滤波估计方法能降低误差序列相关性造成的估计偏差.对1990年1月至2011年10月的中国进出口贸易数据,利用所提方法建立了马尔可夫协整回归模型.     

Cointegration between oil spot and future prices of the same and different grades in the presence of structural change

The purpose of this study is to examine whether crude oil spot and futures prices of the same and different grades are cointegrated using a residual-based cointegration test that allows for one structural break in the cointegrating vector and high-frequency data. We choose the US WTI and the UK Brent as the representative crudes for this analysis since these two crudes have well-established spot and futures markets. We find that spot and future prices of the same grade as well as spot and futures prices of different grades are cointegrated. We examine potential causes of structural change as revealed by the cointegration test in terms of events that have impacted on world oil markets as well as discuss the implications of the results for hedge managers, investors and regulators.