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测量计量经济模型的结构稳定性
时间序列预测最简单最重要的想法计量经济模型的结构稳定性后测量首先出现在走向数据科学上。
来源:走向数据科学In this article I want to take a page out of a data scientist’s book and explore an important guardrail that the data science community has implemented as part of their modeling process – defining model stability independently of model performance.
随着大数据的兴起，可用于对给定问题进行建模的变量数量呈指数级增长。 For instance, models used to identify songs or movies no longer just use generic variables like previously watched movies or liked songs etc. Now we can include a whole slew of metrics that make the user profile more dynamic –time of day,weather,history,potential mood,songs liked but not listened to again, etc. The list can grow in perpetuity and it probably will.我们每天拥有的数据量是有史以来最少的，也是有史以来最多的。每天都会发现更多的相关性。有些是因果关系，有些则不是。
我们如何知道要使用哪些变量以及要忽略哪些变量？如果某些变量对于预测一个用户的行为很重要，但对于另一个用户的行为则不那么重要，该怎么办？
一旦我们开始定义稳定性，就很容易使其变得越来越细致。 Many machine learning models utilize k-fold or n-fold cross validation to measure stability or variables chosen and we can easily adapt these methods for econometric modelling. However, since econometrics works primarily in the “frequency” domain i.e. the data used to train models has temporal relationships, it would behoove us to more closely examine how we can define stability in this new variable space.
For the purposes of this article I want to focus only on one temporal representation – AR structures and how stable the algorithm implied in the function“auto.arima”in R is. The function is a part of the forecast package.我希望读者能够就如何最好地定义我提到的其他结构的稳定性提出想法。
ARMA过程可以表示如下：
我希望读者能够受到启发，想出新的方法来探索这个主题！