Wavelet Analysis and Forecasting using Open-Access Lumber Market Indices for Assessing the Impact of Hurricanes on Southern US Stumpage Prices

Abstract

Abstract In the aftermath of events such as hurricanes, the economic impact of downed timber can reach billions of dollars. Accurate forecasting of stumpage prices after such events is crucial for maximizing recovery value while minimizing salvage costs. However, this poses challenges because of the inherent nature of the data. This study addresses these challenges by exploring the application of wavelet analysis, a novel approach in the field of forestry economic analysis. Wavelet analysis offers a unique framework for studying periodic phenomena in time series data, particularly when frequency changes over time are present. By leveraging wavelet analysis, this study uncovers relationships between timber market indices and hurricane seasons. The combination of traditional correlation analysis and wavelet coherence analysis enhances the statistical analysis in this study, offering a comprehensive examination of the relationship between the Timber Market Survey data and market indices. This innovative analytical approach enables a deeper understanding of the dynamics of the timber market and the potential effects of hurricanes on timber prices. Furthermore, this research highlights the recent advancements in wavelet methodology and the availability of open-source packages in the programming language R, such as WaveletComp and WaveletArima, that facilitate wavelet analysis and time series forecasting. The Wavelet-ARIMA model used in this study demonstrates its effectiveness in reducing noise and improving prediction accuracy. The study incorporates an extensive data set consisting of 10 Consumer Price Indices, 7 Producer Price Indices, 30 state-wide Timber Market Survey indices, 54 TMS subregions, and 6 open market series.