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Brian邵彬 · 2024年06月03日

这道题不是非平稳时间序列么，应该是下一章的练习题把

  1. FRM I
  2. Quantitative

NO.PZ2020011101000020

问题如下：

Suppose an hourly time series has a calendar effect where the hour of the day matters. How would the dummy variable approach be implemented to capture this calendar effect? How could differencing be used instead to remove the seasonality?

选项：

解释：

Let s = 24 represent the hour of the day in military time (e.g. 13 = 1 p.m.). Then Yt=g(t)+γ1I1t+...+γ23I23t+ϵtY_t = g(t) + \gamma_1I_{1t} + ... + \gamma_{23}I_{23t} + \epsilon_t.

Differencing this series can be achieved by looking at observation 24 periods (hours) apart from each other (the following presumes that the error terms are iid and normal):

Yt+24−Yt=g(t+24)−g(t)+ϵt+24−ϵtY_{t + 24} - Y_t = g(t + 24) - g(t) + \epsilon_{t + 24} - \epsilon_t

Once the deterministic time trend is removed the remaining is a covariance-stationary MA(1) process.

这道题不是非平稳时间序列么，应该是下一章的练习题把

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李坏_品职助教 · 2024年06月03日

嗨，努力学习的PZer你好：


是的，这个是非平稳时间序列，通过差分变成平稳序列。题库里确实有部分章节的题目超前了。

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NO.PZ2020011101000020 问题如下 Suppose hourly time series ha calenr effewhere the hour of the y matters. How woulthe mmy variable approaimplementeto capture this calenr effect? How coulfferencing useinsteto remove the seasonality? Let s = 24 represent the hour of the y in military time (e.g. 13 = 1 p.m.). Then Yt=g(t)+γ1I1t+...+γ23I23t+ϵtY_t = g(t) + \gamma_1I_{1t} + ... + \gamma_{23}I_{23t} + \epsilon_tYt​=g(t)+γ1​I1t​+...+γ23​I23t​+ϵt​.fferencing this series cachievelooking observation 24 perio (hours) apart from eaother (the following presumes ththe error terms are iiannormal):Yt+24−Yt=g(t+24)−g(t)+ϵt+24−ϵtY_{t + 24} - Y_t = g(t + 24) - g(t) + \epsilon_{t + 24} - \epsilon_tYt+24​−Yt​=g(t+24)−g(t)+ϵt+24​−ϵt​Onthe terministic time trenis removethe remaining is a covariance-stationary MA(1) process. g(t)是什么东西？讲义里哪里提到过？

2023-06-11 11:38 1 · 回答

NO.PZ2020011101000020 问题如下 Suppose hourly time series ha calenr effewhere the hour of the y matters. How woulthe mmy variable approaimplementeto capture this calenr effect? How coulfferencing useinsteto remove the seasonality? Let s = 24 represent the hour of the y in military time (e.g. 13 = 1 p.m.). Then Yt=g(t)+γ1I1t+...+γ23I23t+ϵtY_t = g(t) + \gamma_1I_{1t} + ... + \gamma_{23}I_{23t} + \epsilon_tYt​=g(t)+γ1​I1t​+...+γ23​I23t​+ϵt​.fferencing this series cachievelooking observation 24 perio (hours) apart from eaother (the following presumes ththe error terms are iiannormal):Yt+24−Yt=g(t+24)−g(t)+ϵt+24−ϵtY_{t + 24} - Y_t = g(t + 24) - g(t) + \epsilon_{t + 24} - \epsilon_tYt+24​−Yt​=g(t+24)−g(t)+ϵt+24​−ϵt​Onthe terministic time trenis removethe remaining is a covariance-stationary MA(1) process.

2022-07-28 21:34 1 · 回答

NO.PZ202001110100002024小时对应23个哑变量，第24个Yt是前23个哑变量为0。那么是不是要从第25小时开始下一个循环，对应第1小时呢？

2022-01-28 17:18 1 · 回答

Yt+24那条式子哪来的, 为什么remove后等于M？谢谢

2020-05-31 06:35 1 · 回答