On Energy Policy Models

Many energy models cannot be relied upon in forecasting or policy analysis. The quality of the data is often poor, and the theoretical underpinnings tend to be inadequate. These points are illustrated by example.     

Forecasting With Bayesian Vector Autoregressions—Five Years of Experience

The results obtained in five years of forecasting with Bayesian vector autoregressions (BVAR's) demonstrate that this inexpensive, reproducible statistical technique is as accurate, on average, as those used by the best known commercial forecasting services. This article considers the problem of economic forecasting, the justification for the Bayesian approach, its implementation, and the performance of one small BVAR model over the past five years.     

Estimation of Current-Quarter Gross National Product by Pooling Preliminary Labor-Market Data

Two methods of using labor-market data as indicators of contemporaneous gross national product (GNP) are developed. The establishment survey data are used by inverting a partial-adjustment equation for hours. A second GNP forecast can be extracted from the household survey by using Okun's law. Using preliminary rather than final data adds about .2 to .4 percentage point to the expected value of the root mean squared errors and changes the weights that the pooling procedure assigns to the two forecasts. The use of preliminary rather than final data results in a procedure that assigns more importance to the Okun's-law forecast.     

Unit Roots,Level Shifts,and Trend Breaks in Per Capita Output: A Robust Evaluation

Determining whether per capita output can be characterized by a stochastic trend is complicated by the fact that infrequent breaks in trend can bias standard unit root tests towards nonrejection of the unit root hypothesis. The bulk of the existing literature has focused on the application of unit root tests allowing for structural breaks in the trend function under the trend stationary alternative but not under the unit root null. These tests, however, provide little information regarding the existence and number of trend breaks. Moreover, these tests suffer from serious power and size distortions due to the asymmetric treatment of breaks under the null and alternative hypotheses. This article estimates the number of breaks in trend employing procedures that are robust to the unit root/stationarity properties of the data. Our analysis of the per capita gross domestic product (GDP) for Organization for Economic Cooperation and Development (OECD) countries thereby permits a robust classification of countries according to the “growth shift,” “level shift,” and “linear trend” hypotheses. In contrast to the extant literature, unit root tests conditional on the presence or absence of breaks do not provide evidence against the unit root hypothesis.     

An Alternative to Transfer Function Forecasting Based on Subspace Methods

In the time series literature, recent interest has focused on the so-called subspace methods. These techniques use canonical correlations and linear regressions to estimate the system matrices of an ARMAX model expressed in state space form. In this article, we use subspace methods to forecast two series with the help of some exogenous variables related to them. We compare the results with those obtained using traditional transfer function models and find that the forecasts obtained with both methods are similar. This result is very encouraging because, in contrast to transfer function models, subspace methods can be considered as almost automatic.     

The Impact of Forecast Errors on Early Order Commitment in a Supply Chain*

Supply chain partnership involves mutual commitments among participating firms. One example is early order commitment, wherein a retailer commits to purchase a fixed‐order quantity and delivery time from a supplier before the real need takes place. This paper explores the value of practicing early order commitment in the supply chain. We investigate the complex interactions between early order commitment and forecast errors by simulating a supply chain with one capacitated supplier and multiple retailers under demand uncertainty. We found that practicing early order commitment can generate significant savings in the supply chain, but the benefits are only valid within a range of order commitment periods. Different components of forecast errors have different cost implications to the supplier and the retailers. The presence of trend in the demand increases the total supply chain cost, but makes early order commitment more appealing. The more retailers sharing the same supplier, the more valuable for the supply chain to practice early order commitment. Except in cases where little capacity cushion is available, our findings are relatively consistent in the environments where cost structure, number of retailers, capacity utilization, and capacity policy are varied.     

正交多项式逼近下非线性趋势序列单位根检验

 传统单位根检验方法常常假设带有线性的确定性趋势,但如果趋势是非线性的,通常将因为检验功效大幅下降而导致检验失败。本文研究用正交多项式逼近非线性趋势,然后对残差进行单位根检验的方法。研究了用正交多项式进行趋势逼近的性质,推导了这种方法进行单位根检验时统计量的极限分布,提出了正交多项式最高阶数的确定方法,仿真研究了残差相关与不相关时的检验功效。结果表明,检验方法是有效的。     

价格持续期的SEMIFAR-ACD模型

本文提出了一类同时包含确定性,差分平稳以及平稳长相关趋势的描述交易价格持续期新模型---SEMIFAR-ACD模型。研究了模型的估计方法和各估计量的渐近性质,构建了相应的估计算法,并应用实际数据,将SEMIFAR-ACD模型与普通ACD模型模拟效果进行比较,论证了SEMIFAR-ACD模型更好的描述数据的性能。     

跨国并购研究的整合框架与发展趋势探讨

随着我国“走出去”战略的加快推进,跨国并购已成为中国企业进入海外市场和开展国际化经营的重要战略方式。文章在讨论了跨国并购研究的理论假设前提的基础上,对其演化脉络进行了相应的梳理,构建了一个包容性和解释力较强的跨国并购研究整合框架,归纳总结了跨国并购研究发展的基本趋势。     

吐鲁番葡萄节旅游经济影响研究

世界各国和各个旅游目的地对节庆的浓厚兴趣和高度重视来源于节庆广泛而深入的影响与作用,对节庆影响研究是节庆研究的重要内容和前沿课题之一。本研究从吐鲁番地区旅游业发展的本底趋势出发,运用旅游本底趋势线模型,定量评估了吐鲁番葡萄节对吐鲁番地区旅游总收入和总人次的影响。结论是：2006年6月28日～8月28日,第三届＂新疆国际旅游节＂和第十五届＂中国丝绸之路吐鲁番葡萄节＂同时在吐鲁番地区举办,国际旅游节和葡萄节影响力巨大,形成了轰动效应,使得2006年吐鲁番葡萄节各个指标的影响值达到最大值。