广东经济增长协整分析

本文通过对广东省1978～2007年的GDP、全社会固定资产投资和税收三个时间序列进行数量分析,发现它们之间存在协整关系,基于这个协整关系建立误差修正模型(ECM),在对模型进行分析后,对广东的经济发展提出一些建议。     

NONLINEAR TIME SERIES MODELS IN ECONOMICS

Abstract. In recent years there has been great interest in developing nonlinear extensions to the basic Autoregressive Integrated Moving Average model popularised by Box and Jenkins. Many of these have been in response to observed nonlinear behaviour in scientific areas such as electronic engineering, geology and oceanography and, as a consequence, have found little application in economics. Economic time series have features peculiar to themselves, and thus often require models to be developed in response to their own special nonlinear character. This paper therefore surveys those nonlinear time series models that have been developed in other disciplines and which have found to be useful for analysing economic time series, such as power transformations, fractional integration and deterministic chaos, and those that have been developed directly in response to nonlinear economic behaviour: for example, logistic transformations, asymmetric models, Markov models for business cycles and time deformation models. Also discussed are various tests for the presence of nonlinearity in time series and the evidence concerning the prevalence of such nonlinearity in economic time series is surveyed.     

基于灰色-ARIMA的金融时间序列智能混合预测研究

提出了一种基于灰色-ARIMA的金融时间序列智能混合预测模型。首先建立金融时间序列灰色预测模型,并采用PSO算法对灰色模型的三个参数进行优化;利用ARIMA算法对预测模型的残差进行分析,同时采用遗传算法对ARIMA的系数进行优化;最后用ARIMA的残差预测结果对灰色预测模型进行补偿。结果表明,以较好的精度拟合一段时期内MA107的时间序列,预测误差控制在5%以上,与单纯的灰色预测算法和神经网络算法相比,在平均绝对误差、均方根误差和趋势准确率三项评价指标上,具有明显优势。     

共生理论与“走出去”战略初探

本文从生态学、经济学的视角,运用共生理论,分析了“走出去”战略实施的原因及其具体措施,并设计了一系列指标,为“走出去”战略提供新的依据。     

An accurate and fully-automated ensemble model for weekly time series forecasting

Many businesses and industries require accurate forecasts for weekly time series nowadays. However, the forecasting literature does not currently provide easy-to-use, automatic, reproducible and accurate approaches dedicated to this task. We propose a forecasting method in this domain to fill this gap, leveraging state-of-the-art forecasting techniques, such as forecast combination, meta-learning, and global modelling. We consider different meta-learning architectures, algorithms, and base model pools. Based on all considered model variants, we propose to use a stacking approach with lasso regression which optimally combines the forecasts of four base models: a global Recurrent Neural Network (RNN) model, Theta, Trigonometric Box–Cox ARMA Trend Seasonal (TBATS), and Dynamic Harmonic Regression ARIMA (DHR-ARIMA), as it shows the overall best performance across seven experimental weekly datasets on four evaluation metrics. Our proposed method also consistently outperforms a set of benchmarks and state-of-the-art weekly forecasting models by a considerable margin with statistical significance. Our method can produce the most accurate forecasts, in terms of mean sMAPE, for the M4 weekly dataset among all benchmarks and all original competition participants.