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Item Restricted Exploring the predictive potential of Google Trends on nowcasting unemployment rate in the Philippines(2016-12) Aclan, Christianne Mari G. ; Marfori, Marie Joyce C. ; Mendoza, Maria Nimfa F.Timely information on the current situation of the economy is very crucial in the decision making process of an economist, policy maker or even an investor. Technological developments have paved the way for such real-time information to be gathered and processed. The challenge now is how one utilizes this sort of information to make meaningful analysis and predictions. For instance, forecasting the current situation of the labor market through economic variables like unemployment rates can significantly contribute to helping government agencies and firms plan effectively particularly in determining the shortage of workers or if more workers will need to be trained in the future. Typically, unemployment rate forecasts are based on the historical time-series properties of the unemployment rate and near-term indicators of the labor market. In this study, we developed a new approach that incorporates and utilizes real-time information, specifically from Google Trends, to make a more accurate and convenient forecast of unemployment rates in the Philippines. Through an in-sample and out-of-sample comparison of forecast accuracy, we found that an Autoregressive Distributed Lag (ARDL) model which uses lagged values of the Google Index (GI) as independent variables, can improve the nowcasting performance of unemployment rates in the Philippines relative to competing benchmark models.Item Restricted Assessing the predictive capability of google search based negative sentiment indices on Philippine inflation(2025-01) Amazona, Oliver Wendell C.; Maronilla, Yohanan Francesco S.; Domingo, Gabriel Angelo B.This study evaluates whether internet search-based sentiment indicators can improve monthly inflation forecasts in the Philippines. We construct four Negative Sentiment Indices (NSIs) using Google Trends data on inflation-related search terms, namely, “price increase and price decrease”, “price hike” and “price rollback”, “pagtaas ng presyo” and “pagbaba ng presyo”, and “inflation and deflation”. These NSIs are designed to capture public concern over rising prices. Additionally, we generate a composite index (PC1) through principal component analysis to summarize the common trend across the four NSIs. Using Autoregressive Distributed Lag (ARDL) models, we assess the forecasting power of these sentiment measures. Cointegration tests show that NSI_2 is cointegrated with monthly inflation at the 10% significance level. However, over a 12-month rolling forecast horizon for 2021, ARDL models that include these variables generally fail to outperform a simple AR(1) benchmark. In terms of RMSE and Diebold-Mariano test results, most NSI-based models yield weaker predictive performance. These findings suggest that while sentiment data may contain useful information, their forecasting value in the Philippine context remains limited. Future research could explore mixed-frequency models using weekly Google Trends data or incorporate richer sentiment sources such as social media and news analytics to improve inflation forecasting in emerging markets.Item Restricted Impact of electric vehicle adoption on fuel consumption and electricity demand in the Philippines(2026-05-01) Chua, Francesca Yddet; Madriaga, Karylle Anne; Chan, Justin Rainer S."This study investigates how electric vehicle (EV) adoption affects fuel demand for internal combustion engine vehicles (ICEVs) and overall fossil fuel use in the Philippines. Annual data from 1990–2024 are analyzed using Autoregressive Distributed Lag (ARDL) models for gasoline and diesel consumption, while annual data from 2003–2024 are analyzed using first-differenced Ordinary Least Squares (OLS) regressions to examine the relationship between EV adoption, electricity consumption, and fossil-based electricity generation. Results show a stable long-run relationship in the gasoline model, where gasoline prices significantly reduce fuel demand while GDP positively affects consumption. However, EV adoption does not exhibit a statistically significant long-run effect on gasoline demand, with only weak short-run substitution effects observed. In contrast, the diesel model shows weak evidence of cointegration, suggesting that diesel demand is influenced mainly by short-run dynamics. The findings also indicate that EV adoption does not yet significantly affect electricity consumption although increases in electricity demand significantly raise fossil-based electricity generation. Overall, current EV adoption remains too limited to generate measurable fossil fuel offsets in the Philippines."