COVID-19 demonstrated distance learning as a feasible alternative to in-person classes during emergencies. To maximize its benefits, it is essential to ensure all participants have access to the necessary resources. The first step is identifying school divisions that are prepared versus unprepared. This study develops an index to measure the readiness of school divisions for distance learning. Through Bayesian Exploratory Factor Analysis (BEFA), eight readiness dimensions with 29 indicators were identified. A distance-based approach was then used to calculate dimension indices and the overall distance learning readiness index, allowing for comparisons of school divisions' relative performance.

Systemic risk became a key issue during the Global Financial Crisis (GFC) of 2008 and remains a concern today. The GFC highlighted how interconnected financial systems can fail, with one institution's collapse leading to widespread economic instability. This study used a specific measure called SRISK to evaluate how much the banking sector contributes to systemic risk (SRISKBanks) during prolonged market declines. It focused on 16 Philippine banks (14 universal banks and two thrift banks) listed on the Philippine Stock Exchange. The study also examined how changes in Gross Domestic Product (GDP), unemployment rates, and the Philippine Peso exchange rate against the US Dollar (USDPHP) influence the banking sector's systemic risk. Results showed that the highest SRISKBanks values occurred in early 2009 and between 2021 and 2022, mainly due to the GFC and the COVID-19 pandemic. There was no long-term relationship between SRISKBanks and GDP growth, USDPHP, unemployment rate—only short-term effects were found. Using vector autoregression model, the study found that changes in GDP growth negatively impact the banking system's systemic risk contribution, while USDPHP and unemployment rate positively impact SRISKBanks. The variance decomposition indicates that variations in the banking sector's systemic risk is largely attributed to shocks to itself, followed by the unemployment rate, GDP growth, and USDPHP exchange rate.

Drought remains one of the most significant climate-related hazards in the Philippines, particularly in Central Luzon, where agriculture, water resources, and local livelihoods are highly sensitive to rainfall variability. This study characterized the spatiotempo-ral behavior of meteorological drought in Central Luzon and evaluated forecasting ap-proaches for short- and long-range drought prediction using the Standardized Precipi-tation Index (SPI).
Monthly Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) rainfall data from 1981–2025 were quality-controlled and validated against Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) sta-tion observations. Following validation, SPI was computed to assess drought variability, identify spatial drought hotspots, evaluate long-term trends, and examine spatial clus-tering patterns. Two forecasting frameworks were then developed for SPI-3 prediction: a Conditional Long Short-Term Memory (LSTM) model and a Bayesian spatiotempo-ral model incorporating Besag–York–Mollie´ 2 (BYM2) spatial random effects, AR(1) temporal dependence, climatic forcing, and seasonal predictors.
Results revealed pronounced spatial heterogeneity in drought occurrence across Central Luzon, with persistent drought hotspots identified in portions of Nueva Ecija, Bulacan, Tarlac, and Zambales. These areas consistently exhibited higher drought fre-quency and greater vulnerability than the rest of the region.
Overall, the findings indicate that both the Conditional LSTM and Bayesian spa-tiotemporal models are suitable for short-term drought forecasting, with the Conditional LSTM providing slightly higher predictive accuracy at shorter lead times. For medium-and long-range forecasting, the Bayesian framework exhibited more stable predictive
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performance while offering the additional advantages of model interpretability and un-certainty quantification, making it well suited for drought outlooks and decision-support applications.

This study constructs a daily Philippine financial system sentiment index using a hierarchical dynamic factor model (HDFM) estimated via the Kalman expectation-maximization algorithm. The index synthesizes 22 indicators spanning the banking, equities, fixed income, and external segments of the Philippine financial system into a composite system-wide sentiment measure and four sectoral sub-indices, covering the period January 2016 to March 2026. The modeling framework accommodates the mixed-frequency and ragged-edge structure of the indicator panel — with observation frequencies ranging from daily to quarterly. The extracted composite index captures the two most significant stress episodes within the sample — the COVID-19 onset in March 2020 and the 2022 global monetary tightening cycle. The sentiment index represents the first application of the Moench–Ng–Potter hierarchical factor framework to an emerging market financial system and constitutes a novel daily-frequency macroprudential surveillance tool for the Bangko Sentral ng Pilipinas.

The field of systematic review and meta-analysis allows researchers to combine multiple studies to obtain a more precise estimate of clinical phenomena. This research focuses on determining reference intervals, which provides a range of val-ues used by clinicians to classify subjects as healthy or not. Existing methods for estimating reference intervals in meta-analysis rely on strict parametric assump-tions, which lead to severe variance overestimation and artificially inflated interval lengths when study populations are skewed or heterogeneous. To resolve these limitations, this study proposes a nonparametric methodology based on Kernel Density Estimation integrated into a Mixture Distribution framework (KDE-MD) under a fixed-effects model, the preferred approach when only a few studies are available. By superimposing individual density curves rather than pooling their variances, the KDE-MD method successfully adapts to complex, mixed data struc-tures without requiring pre-specified distribution families. Furthermore, because non-parametric density estimation inherently risks finite-sample over-smoothing, a bootstrap coverage calibration procedure is introduced to dynamically adjust the nominal confidence level. Extensive simulation results demonstrate that the Bootstrap-Calibrated KDE-MD (BootKDE-MD) methodology effectively detects and corrects these finite-sample biases. Ultimately, the proposed method consis-tently guarantees the accurate nominal target coverage while yielding narrower interval lengths than traditional parametric alternatives.