Autoregression Models
Autoregression models are part of a more classical statistical modeling technique that is used on time series data (that is, any dataset that changes with time) and extends upon the linear regression techniques covered in this chapter. Autoregression models are commonly used in the economics and finance industry as they are particularly powerful in time series datasets with a sizeable number of measurements. To reflect this, we will change our dataset to the S&P daily closing prices from 1986 to 2018, which is available in the accompanying source code.
Figure 3.59: S&P 500 Daily Closing Price
The main principle behind autoregression models is that, given enough previous observations, a reasonable prediction for the future can be made; that is, we are essentially constructing a model using the dataset as a regression against itself, hence autoregression. This relationship can be modeled mathematically as a linear equation:
Figure 3.60: First-order autoregression...
