Using standard deviation values as independent variables

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SUMMARY

Standard deviation values can be effectively used as independent variables in linear regression analysis (OLS), provided that researchers account for overlapping time periods to avoid issues such as autocorrelation and spurious t-values. The discussion highlights the necessity of calculating standard deviation from time series data in advance. To mitigate potential pitfalls, employing robust methods like Generalized Method of Moments (GMM) is essential for accurate results.

PREREQUISITES
  • Understanding of linear regression analysis (OLS)
  • Familiarity with time series data analysis
  • Knowledge of standard deviation calculations
  • Experience with Generalized Method of Moments (GMM)
NEXT STEPS
  • Research the implications of autocorrelation in regression analysis
  • Learn about robust statistical methods, specifically Generalized Method of Moments (GMM)
  • Explore time series analysis techniques for calculating standard deviation
  • Investigate best practices for handling overlapping time periods in regression models
USEFUL FOR

Data analysts, statisticians, and researchers involved in predictive modeling and regression analysis, particularly those focusing on risk assessment and time series data.

monsmatglad
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Hey. I am planning on doing some research, where I predict a change based on different types of risk.
The question is simple. Can I use values of standard deviation as independent variables in a linear regression analysis (OLS)? The standard deviation values over time will be calculated in advance from time series, and then used as independent variables in the regression analysis.

Mons
 
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You have to be careful of overlapping time periods, ie the rh var is sd from period t:t+n and lh variable is data at time t, with each increment of t having n overlap - this will lead to autocorrelation and spurious t-values unless you use a robust method like GMM to correct for these
 

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