Choosing Between End of Month and Start of Month Values in Daily Time Series Regression

When conducting a time series regression with daily values, one of the key decisions is whether to use the end of the month value or the start of the month value. This choice can impact the accuracy and consistency of your regression analysis. Let's break down the considerations and potential implications.

End of Month Values

End of month (EOM) values can offer several advantages:

Data Alignment: Most monthly data use EOM conventions. This ensures that your analysis is in sync with other variables, making it easier to compare and combine data sets. Accuracy: Financial variables, in particular, are often calculated more accurately at the EOM. This can provide a more reliable basis for your regression analysis.

Given these advantages, many analysts choose to use the EOM value for their time series regression. However, it is crucial to ensure that your data aligns consistently with this choice.

Start of Month Values

Alternatively, you might consider using the start of the month value. While this approach has some potential drawbacks, it is still a valid option:

EOD Bias: If you include the start of the month value, you might be introducing a lag into your data, depending on the specific day of the week. This potential lag should be carefully considered and possibly adjusted for in your analysis. Consistency: As long as you are consistent in using the same approach throughout your analysis, your results will still be meaningful, albeit with a slight time delay.

It is important to note that using the start of the month value does not necessarily mean you are lagging your data. The key is to remain consistent and transparent about your methodology.

Average Daily Values

Another option is to use the average daily value for the month. This approach offers a middle ground:

Smoothing: By averaging daily values, you smooth out short-term fluctuations, providing a more stable and reliable dataset for your regression analysis. Flexibility: Averaging daily values can be particularly useful if you are dealing with highly volatile data or if you are aiming for a more representative monthly value.

However, using the average daily value approach requires careful consideration of the distribution of daily values within the month. If there are significant outliers or systematic biases, these may affect your results.

Conclusion

The choice between using the end of month, start of month, or average daily values in a time series regression for daily data is largely dependent on your specific needs and the nature of the variables involved. Here are some key takeaways:

End of Month: Use when data alignment and financial accuracy are key. Start of Month: Consider for potential lag but ensure consistency. Average Daily: Opt for when you need to smooth out daily volatility.

No matter which approach you choose, the most important aspect is consistency and clear communication of your methodology. This ensures that your results are neither biased nor misleading, and that they contribute meaningfully to your analysis or project.