Export-led growth: a survey of the empirical literature and some non-causality results. Part 2

This paper continues the investigation of Giles and Williams (2000) on export-led growth (ELG). In the first part, we surveyed the empirical export-led growth literature; it was evident that Granger non-causality tests are commonly applied as a test for ELG. In this paper, we explore the sensitivity of the test for exclusions restrictions often used as the Granger non-causality test for ELG by reconsidering two applications: Oxley's (1993) study for Portugal and Henriques and Sadorsky's (1996) analysis for Canada. We focus on the robustness of the method adopted to deal with non-stationarity, including the choice of deterministic trend degree. We show that different noncausality outcomes are easy to obtain, and consequently we recommend that readers interpret the empirical ELG literature with care. Our analysis also highlights the importance of examining the robustness of Granger non-causality test results to avoid spurious outcomes in applications.     

Improving forecasting for telemarketing centers by ARIMA modeling with intervention

In this study we analyze existing and improved methods for forecasting incoming calls to telemarketing centers for the purposes of planning and budgeting. We analyze the use of additive and multiplicative versions of Holt–Winters (HW) exponentially weighted moving average models and compare it to Box–Jenkins (ARIMA) modeling with intervention analysis. We determine the forecasting accuracy of HW and ARIMA models for samples of telemarketing data. Although there is much evidence in recent literature that “simple models” such as Holt–Winters perform as well as or better than more complex models, we find that ARIMA models with intervention analysis perform better for the time series studied.     

A Long-run Disaggregated Cross-section and Time-series Demand System: An Application to Italy

In this paper, we study a long-run disaggregated model of consumption following an approach based on an integrated cross-section and time-series demand system. The study consists of three steps. First, a cross-section analysis is performed on data from household budget surveys. At this stage, the problem of 'zero expenditures' is solved. The cross-section results are transformed into variables for use in the time-series system of demand. Then, this demand system is built and estimated. Some results for Italy concerning both the cross-section and the time-series analyses are presented.     

基于相位一频率曲线的压电晶体阻抗数据分析

本文根据压电晶体的谐振等效电路特性，首先从理论上分析了压电晶体的串联谐振频率和并联谐振频率，由此求出在某一相位下的串、并联谐振频率的平均值，进而获得在多个相位下所得平均频率的总平均值。经过系统分析，得出采用总平均频率的噪音水平比传统的零相位串联谐搌频率的噪音水平大为降低。     

磷肥中放射性核素活度浓度简易检测方法研究

推导建立了一种相对简易的磷肥中铀系放射性核素活度浓度检测方法。根据所采用的观测量不同，方法细分为3种。选择第3种方法进行实验测算，利用基于G-M 计数管的Inspector A lert放射性探测器，在密闭环境对0.0642 g磷肥样品进行α射线探测，并最终推算得出在最不利情况下，226R a的活度浓度为0.2293 Bq/g，238U 的活度浓度为0.4586 Bq/g。     

基于时序关联规则的客户需求预测方法研究

客户的需求预测是企业非常重视的一个方面,如何对客户的需求进行预测,数据挖掘作为一种最新的数据处理和分析技术,越来越受到重视和应用。对客户的消费行为的数据挖掘主要是关联分析,而普通的关联分析或购物篮分析只考虑到同一时间下客户的消费模式,本文在此基础上初步研究了时间序列中的关联规则,即不同时间客户需求的相关性。     

小波分析结合ARIMA组合模型的2014~2017年石油贸易量预测研究

选取2002~2013年我国石油进出口贸易量的数据进行建模分析。首先运用小波分析理论将贸易量数据进行分解,识别出数据的主要特征和细节特征,针对不同特征进行识别和平稳性检测和参数估计,建立相应的ARIMA模型,并进行预测加权合成。仿真结果表明,小波分析结合ARIMA组合模型的预测精度远远大于为改进的ARIMA预测模型,从而为科学合理的决策提供更为精确的预测模型。     

G@RCH 2.2: An Ox Package for Estimating and Forecasting Various ARCH Models

This paper discusses and documents G@RCH 2.2, an Ox package dedicated to the estimation and forecast of various univariate ARCH–type models including GARCH, EGARCH, GJR, APARCH, IGARCH, FIGARCH, HYGARCH, FIEGARCH and FIAPARCH specifications of the conditional variance and an AR(FI)MA specification of the conditional mean.
These models can be estimated by Approximate (Quasi) Maximum Likelihood under four assumptions: normal, Student– t , GED or skewed Student errors. Explanatory variables can enter both the conditional mean and the conditional variance equations. h –step–ahead forecasts of both the conditional mean and the conditional variance are available as well as many mispecification tests.
We first propose an overview of the package's features, with the presentation of the different specifications of the conditional mean and conditional variance. Then further explanations are given about the estimation methods. Measures of the accuracy of the procedures are also given and the GARCH features provided by G@RCH are compared with those of nine other econometric softwares. Finally, a concrete application of G@RCH 2.2 is provided.     

区间时间序列预测准确度探讨

区间时间序列在决策过程中提供重要的信息,特别是在经济发展、人口政策、规划管理或金融监管等方面,因此如何计算出预测区间的精确度成为一个重要议题。本文提出两种区间预测准确度分析的方法,通过估计预测结果的平均区间误差平方和及平均相对区间误差和,比较不同预测方法的优劣。并由预测区间与实际区间的重叠位置,充分说明预测方法所具有的有效性。这些分析预测区间准确度的方法,将为管理者提供更客观的决策空间。     

金融时序数据建模的模型设定问题分析

W．J．Granger与D．F．Hendry（2004）关于建模思路的对话引起了国际计量经济学界关于模型设定问题的争论，本文就这一问题分析讨论了在金融时序数据实证研究中得以广泛应用的ARCH／GARCH模型的设定问题，认为在金融时序数据的建模中，ARMA族模型不宜作为数据生成过程的模型设定，其统计性质也不能直接扩展到ARMA-GARCH族数据生成过程。虽然ARCH／GARCH族模型作为金融时序数据的生成过程有着良好的统计性质，但不宜单纯采用一般到特殊的建模思路，而应是一般到特殊和特殊到一般两种建模思路的结合。ARCH／GARCH族模型的设定应当包含事前检验、事后检验等设定检验步骤。