Time Series Analysis Skill

This skill provides guidance for univariate and multivariate time series analysis in empirical economics. It covers stationarity testing, ARIMA modeling, VAR/VECM systems, cointegration, and Granger causality.

Analysis Workflow

Step 1: Exploratory Inspection

• Plot the series (level, first difference, log)
• Examine ACF and PACF plots to identify dependence structure
• Check for obvious trends, seasonality, structural breaks

Step 2: Stationarity Testing

Always test for unit roots before modeling. Preferred approach:

1. ADF test (H₀: unit root present)
2. KPSS test (H₀: series is stationary)
3. If ADF rejects AND KPSS fails to reject → stationary (I(0))
4. If both suggest non-stationary → take first difference, retest

ADF vs KPSS Conflict Resolution: When ADF fails to reject (suggests unit root) but KPSS also fails to reject (suggests stationary), the tests disagree. Recommended approach: (a) check for structural breaks — a break can make a stationary series look like it has a unit root; (b) use Zivot-Andrews test to allow for one structural break; (c) examine the series visually and consider economic theory. When in doubt, err on the side of differencing to avoid spurious regressions.