Forecasting the Future of the Lubid Industry: An Econometric Model of Market Trends and Production Capacity in the Barangays of Malilipot, Albay

Abstract

This study aimed to forecast the future of the Lubid industry in Malilipot, Albay, by developing an econometric model to analyze market trends and production capacity across different barangays. It sought to determine the extent to which the model could accurately predict demand, supply, prices, and technological progress, as well as to identify any significant differences in forecasting accuracy among barangays. Additionally, the study examined the key factors influencing the reliability of forecasts, such as data accuracy, stakeholder interpretation, and external variables, and explored the relationship between these factors and forecast precision. Using a mixed-methods approach that combined descriptive analysis with econometric modeling, the research integrated primary data from surveys and interviews with secondary data from reports and industry records, ensuring comprehensive insights into industry conditions. Purposive sampling was employed to select 90 participants—30 officials and 60 artisans—from six barangays, focusing on those with direct industry knowledge.  Findings revealed that the model was highly effective in predicting demand and supply, both with a weighted mean of 3.75 ("Very Much Extent"), and demonstrated consistent results with low standard deviations of 0.45. However, its ability to incorporate external factors scored slightly lower at 3.30 ("Much Extent") and showed more variability. The analysis also uncovered variability in forecast accuracy across barangays, with Barangay D showing the lowest residual error of 0.0077, while Barangay C had the highest at 0.0417, indicating a need for localized adjustments. Critical factors affecting forecast reliability included data accuracy and completeness, which scored a high mean of 3.90, along with stakeholder interpretation and data management practices, each with a mean of 3.50. Regression analysis confirmed these factors significantly impact forecast accuracy, with all p-values below 0.01.  Conclusions: The econometric model demonstrated strong potential in forecasting key market indicators of the Lubid industry, with high accuracy in demand and supply predictions. Nonetheless, external factors and localized differences among barangays highlight the need for continuous refinement. The significance of data quality and stakeholder involvement underscores their vital roles in enhancing forecast reliability.  Recommendations: To improve the accuracy and reliability of future forecasts, it is recommended to standardize data collection procedures across barangays, enhance stakeholder training and coordination, and incorporate advanced econometric techniques such as VAR, ARDL, or ECM to better capture industry dynamics. Additionally, fostering a collaborative environment among industry stakeholders and policymakers can facilitate more accurate data sharing and interpretation. These strategies will support more informed decision-making, promote sustainable growth, and strengthen the resilience of the Lubid industry in Malilipot, Albay.