Hi all, I have a general question related to logit regression model. I want to know your mind about one point related to logit regression model. I am using logit model for my analyzing. My key independent variable is the lagged; hence, it is predictive regression. So, my dependent variable is 0 and 1. These 0 and 1 is related to the time of the day. To make it more clear, I have minutely time-series data for one day. In one day, we have 24 hours * 60 minutes = 1440 rows as my dependent variable. The dependent variable is "0" from 1st to 600th rows and from 900th to 1440th rows - it is "normal period". However, the dependent variable is "1" from 600th to 900th rows - let's say it is "abnormal period". In its simplest from, my dependent and independent variables are: Number of rows Dependent variable Independent variable 1 0 0.1 2 0 0.15 . . . . . . 600 0 0.17 601 1 0.13 . . . . . . 900 1 0.22 901 0 0.19 . . . . . . 1440 0 0.21 I am interested the examine the predictive power of my key independent variable. Now I use the first lag of my independent variable as my KEY VARIABLE. Actually, I am confusing about the result of my regression. The issue is, I guess that the majority part of the predictive power of lagged variable will come from the "abnormal period" rather than "normal period". What do you think about that?
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