Do Macroeconomic Predictors Improve Bangladesh Inflation Forecasts? A Leakage-Safe Econometric and Machine-Learning Evaluation, 2010-2025 Abstract- This study evaluates whether lagged macroeconomic indicators improve

This study evaluates whether lagged macroeconomic indicators improve one-month-ahead forecasts of Bangladesh headline inflation beyond strong persistence-based benchmarks. A monthly data set covering January 2010 to December 2025 is assembled from public sources and contains year-on-year inflation, BDT/USD exchange-rate changes, the policy rate, Brent oil-price changes, and annual real GDP growth. To prevent information leakage, annual GDP is introduced only as the previous year’s realized value, and forecasts are evaluated through an expanding-window design on an untouched January 2023-December 2025 holdout. The analysis combines augmented Dickey-Fuller and KPSS stationarity tests, Ljung-Box diagnostics, a distributed-lag regression with heteroskedasticity-and-autocorrelation-consistent standard errors, Granger predictive-causality tests, SARIMAX, autoregression, Ridge regression, Random Forest, Extra Trees, Gradient Boosting, and XGBoost. Inflation is highly persistent, with a lag-one autocorrelation of 0.964. Exchange-rate lags are jointly significant in the explanatory model (Wald p < 0.001), whereas oil-price lags are not and policy-rate changes are only borderline significant. SARIMAX records the lowest RMSE (0.543 percentage points), but its improvement over the last-month benchmark is not statistically significant. Complex machine-learning models do not outperform the parsimonious benchmarks. The results indicate that exchange-rate information matters for inflation dynamics, but most short-horizon forecastability originates from inflation persistence. The paper demonstrates why chronological validation, strong naive baselines, and careful publication-timing controls are essential for credible macroeconomic machine learning.