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Theoretical basis – TEI@I methodology
Published in Yafei Zheng, Kin Keung Lai, Shouyang Wang, Forecasting Air Travel Demand, 2018
Yafei Zheng, Kin Keung Lai, Shouyang Wang
Guided by the economic theories and based on the facts, econometrics is a discipline studying the quantitative rule and relationship between economic activities by some mathematical and statistical methods, mostly with the help of computer techniques. In those studying processes, many econometrical models are applied and constructed for the quantitative modeling purposes. In terms of the research contents, econometrics could be classified into two main groups, i.e., theoretical econometrics and applied econometrics. The former focuses the exploration of new econometrical models and methods, while the latter mainly deals with empirical studies to discover the economic operation rule. The econometrical model is the key element for this discipline. Common categories of econometrical models are listed in Table 3.2.
Analytics Toolsets
Published in Ali Soofastaei, Data Analytics Applied to the Mining Industry, 2020
Russell Molaei, Ali Soofastaei
Time series are widely used in different domains such as statistics, signal processing, pattern recognition, econometrics, finance, supply chain management, production planning, weather forecasting, and in any subject of applied science and engineering that includes discrete-time measurements. It has a well-established theoretical basis in statistics and dynamic systems theory.
Aggregate Production Planning
Published in Katsundo Hitomi, Manufacturing Systems Engineering, 2017
Econometrics indicates economic measurement by establishing quantitative relationships among economic variables with the aid of statistics. Econometric models are explicit systems which assemble and weigh economic information.
Application of a Hybrid Model Based on ICEEMDAN, Bayesian Hyperparameter Optimization GRU and the ARIMA in Nonferrous Metal Price Prediction
Published in Cybernetics and Systems, 2023
Yu-ting Huang, Yu-long Bai, Lin Ding, Ya-Jie Zhu, Yong-Jie Ma
Early studies mainly focus on econometric models, and its methodology is “logical positivism”. The traditional econometric methods take empirical observation information as the research object. Based on specific mathematical, statistical and logical theories, the observation is reasoned and summarized, from which the inherent laws or patterns of data sequences are obtained, and summarized in the way of models to make it approach the empirical reality. Finally, the results are tested and applied (Li and Qi 2010). The commonly used models mainly include generalized autoregressive conditional heteroscedasticity (GARCH) model (Hou and Suardi 2012), autoregressive comprehensive moving average (ARIMA) model (Xiang and Zhuang 2013) and autoregressive conditional heteroscedasticity (ARCH) model (Cheong 2009). Zhu and Ye et al. used GARCH model to predict the high-frequency subsequence of original carbon price data, and LSSVM predicted the low-frequency subsequence and achieved good prediction results (Zhu et al. 2018b); At the same time, Byun and Cho proved the superior performance of GARCH -type model in carbon price prediction (Byun and Cho 2013); Zhu and Chevallier (2017) used the combination of ARIMA and LSSVM to predict the carbon price, which proved the superiority of ARIMA model. Based on wavelet decomposition, ARIMA model is used to predict the price of nonferrous metals and good prediction results are obtained (Kriechbaumer et al. 2014). Politis applied the ARCH model to more complex income square prediction problem (Politis 2004). The econometric model must meet the basic assumptions of the classical Gaussian linear regression model in practical applications. However, the real world is complex and changeable, and cannot be described by limited mathematical formulas, making it difficult to obtain good application in practical applications.