Financial Risk Manager Part 1
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When dealing with time series data that exhibits seasonality, one common approach is to use dummy variables. A dummy variable is a numerical variable used in regression analysis to represent subgroups of the sample in your study. In this case, the subgroups are the different seasons in the data. The number of dummy variables used should be one fewer than the number of seasons. This is because one season can be represented as the absence of all other seasons. Therefore, if there are four seasons in the data, three dummy variables should be used. This method allows for the effects of the different seasons to be accounted for in the analysis, providing a more accurate representation of the data.
Choice B is incorrect. Using two dummy variables for each season would not be appropriate for a time series data set that shows quarterly seasonality. This is because there are four quarters in a year, and therefore, four seasons to account for in the data. Using only two dummy variables would not capture the full extent of the seasonal variation present in the data.
Choice C is incorrect. Deseasonalizing the data and then applying non-seasonal methods may not be an appropriate method for handling this type of time series data either. While deseasonalizing can help to remove seasonal patterns from the data, it does not necessarily mean that non-seasonal methods will be effective at analyzing this type of time series data. The underlying structure of the time series may still exhibit other forms of dependence or patterns that are better handled by other methods.
Explanation:
The correct answer is A because when dealing with time series data that exhibits quarterly seasonality (4 seasons), the appropriate number of dummy variables to use is three. This follows the general rule that the number of dummy variables should be one less than the number of categories (seasons) to avoid perfect multicollinearity.
Key Points:
- For quarterly data with 4 seasons, we need 3 dummy variables
- One season serves as the reference category (when all dummy variables = 0)
- This approach properly captures seasonal effects in regression analysis
- Using fewer dummy variables (B or D) would under-specify the model
- Deseasonalizing (C) may remove important seasonal patterns that should be modeled
The dummy variable method allows for direct estimation of seasonal effects while maintaining the interpretability of the time series structure.