SearcharxivSearch

arXiv · 2604.16186

Path-Explosive Behaviour in Economic Time Series: A Realization-Centred Exploratory Framework

Abstract

We propose a descriptive, realization-centred framework for detecting and characterising explosive and co-explosive behaviour in economic time series, which we term path-explosive behaviour. Departing from the data-generating-process (DGP) perspective that underlies recursive unit root testing, the approach operates directly on observable path properties of the realised series. Four diagnostic layers -- level geometry, growth rate dynamics, normalised curvature, and log-space behaviour -- yield statistics that discriminate between genuine self-reinforcing multiplicative growth and I(2) dynamics without distributional assumptions or asymptotic critical values. Two theoretically motivated absolute gate thresholds screen detected episodes before a composite intensity score is assigned. Co-explosive behaviour between pairs of series is assessed at the episode level through a Jaccard co-occurrence index and non-parametric intensity concordance measures. The theoretical motivation draws on the path dependence and planning irreversibility literatures to argue that, in settings where discrete institutional decisions shape growth trajectories, a realization-centred characterisation is epistemically more appropriate than a DGP-based test. A simulation study across four DGP regimes validates the framework's discriminating power and conservatism. An empirical application to real house prices, commodity prices, public debt, and Spanish tourism destinations illustrates the empirical content of the path-explosive concept and distinguishes it from speculative bubble detection.

Explore related subjects

Keep this discovery

BibTeXRIS

José Francisco Perles-Ribes. 2026-04-17. Path-Explosive Behaviour in Economic Time Series: A Realization-Centred Exploratory Framework. https://arxiv.org/abs/2604.16186

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Identification in Linear Quantile Panel Models

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

econ.EM

Experimental Design for Policy Choice

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design--policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

econ.EM

Designing Spatial Treatments

Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a unit's response diminishes with distance to a treated site. Our estimand of interest is an ``uncontaminated'' effect equal to the average impact of a single intervention site over all hypothetical sites. We propose a novel design based on a Mat\'{e}rn point process which separates treatments by a distance of at least $r$. A larger choice of $r$ reduces bias by separating interventions but increases variance by reducing their numerosity. We choose $r$ to maximize the rate of convergence of a Horvitz-Thompson estimator and prove that this is minimax rate-optimal. We provide weak conditions under which the estimator is asymptotically normal and propose a variance estimator.

econ.EM