摘要翻译:
应用研究中的模型选择问题至关重要。由于这种研究中的真实模型尚不清楚,在各种潜在的模型中应该使用哪一个模型是一个经验问题。可能存在几种竞争模式。处理这一问题的一个典型方法是经典的假设检验,它使用一个任意选择的显著性水平,基于一个真正的零假设存在的潜在假设。在本文中,我们研究了这种方法在确定使用时间序列数据的不同数据生成过程的正确模型方面是如何成功的。提出了一种基于信息准则或交叉验证的更形式化模型选择技术的替代方法,并在时间序列环境中通过蒙特卡罗实验进行了评估。本文还探讨了在假设检验和信息标准的基础上,在有无单位根的情况下,使用各种策略来判断两个变量之间存在何种类型的一般关系(如水平关系或第一差异关系)的有效性。
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英文标题:
《Model Selection in Time Series Analysis: Using Information Criteria as
an Alternative to Hypothesis Testing》
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作者:
R. Scott Hacker and Abdulnasser Hatemi-J
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最新提交年份:
2018
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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英文摘要:
The issue of model selection in applied research is of vital importance. Since the true model in such research is not known, which model should be used from among various potential ones is an empirical question. There might exist several competitive models. A typical approach to dealing with this is classic hypothesis testing using an arbitrarily chosen significance level based on the underlying assumption that a true null hypothesis exists. In this paper we investigate how successful this approach is in determining the correct model for different data generating processes using time series data. An alternative approach based on more formal model selection techniques using an information criterion or cross-validation is suggested and evaluated in the time series environment via Monte Carlo experiments. This paper also explores the effectiveness of deciding what type of general relation exists between two variables (e.g. relation in levels or relation in first differences) using various strategies based on hypothesis testing and on information criteria with the presence or absence of unit roots.
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PDF链接:
https://arxiv.org/pdf/1805.08991