Inflation is a sustained rise in the general price level that reduces money’s purchasing power. In retirement planning, it determines how spending requirements grow and how long assets may last. section 5.6 section 5.7
A simple simulation increases total and essential spending by a fixed annual rate. A richer model treats inflation as persistent: current inflation depends on its previous value, a long-run level, and a random shock. section 5.6 section 5.7
A practical inflation-risk workflow is:
- Download CPI-U data, resample monthly observations to annual values, and calculate year-over-year percentage changes. section 5.7
- Regress inflation on its lagged value to estimate persistence, the long-run component, and residual volatility. section 5.7
- Generate inflation paths and apply each path to spending inside a Monte Carlo wealth simulation. section 5.7
- Compare bankruptcy probabilities and remaining assets under fixed inflation and inflation-risk assumptions. section 5.6 section 5.7
Inflation-adjusted spending guardrails
A retirement simulation starts with $60,000 annual spending and $48,000 essential spending. Both amounts grow with inflation, while guardrails adjust discretionary spending as wealth changes. A 30-year, 10,000-path run reports approximately 28% bankruptcy probability for the stated dynamic strategy. section 5.6
Listing 5.6 Downloading inflation data
import pandas_datareader.data as web
start = '01-01-1947'
end = '12-30-2023'
data = web.DataReader(name='CPIAUCNS', data_source='fred', start=start,
end=end) #1
data = data.resample('Y').last() #2
data['yoy'] = data['CPIAUCNS'].pct_change() #3
data.tail()
Use the CPI-U data workflow to obtain annual historical inflation rates before estimating persistence or simulating future paths. section 5.7
