不同样本量下rMDL、eMDL、nMDL、gMDL、AIC和BIC的非对称价格传导线性模型比较评价

I. K. Amponsah, H. Acquah, Nathaniel K. Howard
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引用次数: 3

摘要

最小描述长度(MDL)是一个鲜为人知的标准,与广泛使用的信息标准(AIC, BIC等)相比,它在模型选择方面取得了很大的进步。本研究首次使用r函数开发了MDL标准来评估不对称价格传导(APT)模型(Complex、Standard和Houck’s)。在不同样本量的条件下,评估所有六个标准恢复真实DGP的能力。1000蒙特卡罗模拟程序显示,MDL标准的平均值指向真实的DGP,并且在研究条件下与AIC和BIC相当(如果不是更好的话)。一般来说,所有模型选择标准(rMDL、nMDL、gMDL、eMDL、AIC和BIC)的性能随着样本量的增加而提高,它们对标准模型和复杂模型的真实DGP的恢复能力也随之提高。本研究建议在模型选择中使用MDL标准,考虑到约束(财政、时间和资源不足),150个样本量足以对不对称价格模型做出合理的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Evaluation of Asymmetric Price Transmission Linear Models Using rMDL, eMDL, nMDL, gMDL, AIC and BIC Across Varying Sample Sizes
The Minimum Description Length (MDL), a less known criterion, is making great strides in model selection as compared to the widely known and used information criteria (AIC, BIC, etc). This study developed the MDL criterion using R-functions to evaluate Asymmetric Price Transmission (APT) models (Complex, Standard and Houck’s) for the first time ever. All six criteria’s ability to recover the true DGP was assessed under the condition of varying sample size. A 1000 Monte Carlo simulation procedure revealed that the MDL criteria on the average points to the true DGP and are comparable (if not better) to both AIC and BIC under study condition. Generally, the performances of all model selection criteria (rMDL, nMDL, gMDL, eMDL, AIC and BIC) improved with increasing sample size in their ability to recover the true DGP for both standard and complex models. This study recommends the use of MDL criterion in model selection and in the light of constraint (financial, time and inadequate resources), a sample size of 150 is sufficient in making sound decisions on asymmetric price models.
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