Economists conventionally attribute observed volatility in economic time series data to exogenous random shocks that agitate otherwise stable real-world markets and, consequently, model volatility with a variety of linear–stochastic and probabilistic methods. However, some economists have recognized another possible explanation for volatility: markets may be intrinsically unstable, and we might be able to model attending volatility parsimoniously with low-dimensional, nonlinear, deterministic dynamic models without resorting to stochastic inputs. Whether observed volatility is generated by inherently stable or unstable markets has serious policy implications. Will laissez-faire policies suffice to dampen volatility because markets are self-correcting, or are interventionist policies required? This chapter introduces nonlinear time series analysis (NLTS) – a collection of methods developed in mathematical physics to diagnose the source of real-world volatility from observed time series data. Depending on data quality, economists can potentially use NLTS to reconstruct phase-space market dynamics and extract equations of motion from a single price series.
JEL classification: C8