Time Series Guide

A skill for applying time series econometric methods including ARIMA modeling, VAR systems, cointegration analysis, and unit root tests. Covers stationarity concepts, model selection, forecasting, and diagnostic checking for economic and financial data.

Stationarity and Unit Root Tests

Why Stationarity Matters

A time series is stationary when its statistical properties (mean, variance, autocorrelation) do not change over time. Most econometric methods require stationarity. Non-stationary series can produce spurious regressions.

Testing for Stationarity

from statsmodels.tsa.stattools import adfuller, kpss
import pandas as pd


def test_stationarity(series: pd.Series, name: str = "Series") -> dict:
    """