A Brief History of Time Series Econometrics

This chapter surveys a century of time series econometrics, from its statistical foundations to modern, data-rich practice. It traces the field’s major turning points: univariate ARIMA modeling, the rise of vector autoregressions and the identification debates that followed, and the treatment of nonstationarity through unit roots, cointegration, and error correction. Later sections take up volatility and nonlinear modeling, state-space signal extraction, and Bayesian estimation. The chapter then follows the shift to high-dimensional forecasting and real-time nowcasting, and the spread of machine learning into time series workflows. It compares the major approaches on forecasting performance, structural interpretability, and the measurement of uncertainty. The methods that endured answered a concrete empirical need, whether for better out-of-sample forecasts, credible policy analysis, or an honest measure of risk. The chapter closes with lessons from the field’s evolution and an agenda for future research as data and computation expand.