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Autoregressive model

As explained in Build a Robo-Advisor with Python (From Scratch)

A time-series model in which the current value depends linearly on one or more previous values and a random error term.

An autoregressive model represents a persistent time series whose current value depends on a previous value and a random disturbance. For inflation, an AR(1) process combines lagged inflation with a long-term average, allowing high inflation to persist while gradually reverting toward typical levels. section 5.7

How Build a Robo-Advisor with Python (From Scratch) covers it

How deep it goes
Estimates one-lag parameters with regression and implements simulated paths, stopping at the AR(1) case.
Examples
Uses CPI-U data, lagged-inflation regression, residual volatility, and simulated thirty-year wealth paths.
What you can do after
Build an AR(1) inflation component and insert it into a retirement Monte Carlo simulation.
How it connects
Connects inflation risk with mean reversion, linear regression, random shocks, and Monte Carlo simulation.

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This explainer was generated by Manning's knowledge graph from the text of Build a Robo-Advisor with Python (From Scratch). The section links point to the parts of the book it draws on.