Kondratiev Wave Analysis of FMCG Retail Innovation Events, 1780–2024

Supplementary materials for a study analyzing long-term innovation cycles in fast-moving consumer goods retail. The dataset includes a record of 78 innovation events from 1780 to 2024, supporting spectral and statistical analyses. It was authored by Vitaliy Kolomiets and last updated in May 2026.

Use Cases

• Conduct spectral analysis on decadal time series data to identify cyclical patterns in retail innovation.
• Test parametric models of innovation cycles based on the provided AR(2) stochastic cycle comparison.
• Perform bias sensitivity analyses using the included date perturbation and inter-rater reliability protocols.
• Compare retail wave drivers between different regions, such as Russia and the USA, using the event study data.

Strengths

• Includes a timeline of 78 specific innovation events spanning 244 years from 1780 to 2024.
• Provides full replication code in Python for spectral, surrogate, and regression analyses.
• Contains multiple sensitivity and robustness checks, including alternative detrending and aggregation window tests.

Limitations

• The dataset is small, at 110.0 KB, suggesting limited raw data volume.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale modeling.

Provenance

Source

figshare

Collection Method

Likely compiled from historical and economic sources for event analysis.

Time Range

1780–2024

Freshness

Last updated 2026-05-10 18:43:57