In the last chapter, we focused on linear models tailored to cross-sectional data where the input data belongs to the same time period as the output they aim to explain or predict. In this chapter, we will focus on time series data where observations differ by period, which also creates a natural ordering. Our goal will be to identify historical patterns in data and leverage these patterns to predict how the time series will behave in the future.
We already encountered panel data with both a cross-sectional and a time series dimension in the last chapter and learned how the Fama-Macbeth regression estimates the value of taking certain factor risks over time and across assets. However, the relationship between returns across time is typically fairly low, so this procedure could largely ignore the time dimension. The models in this chapter focus on time series...
