Probabilistic Perspectives on Sex Ratio at Birth Dynamics

Abstract

Predicting trends in Sex Ratio at Birth (SRB) is crucial in demographic research, shedding light on evolving population dynamics. This study conducts a thorough investigation into the selection and evaluation of optimal forecasting models for SRB data. Utilizing historical SRB records from selected countries, we meticulously assess various models, including Autoregressive Integrated Moving Average (ARIMA), Autoregressive (AR), and White Noise models. Our empirical analysis reveals the prominence of the AR(2) model in capturing intricate SRB dynamics. Additionally, we explore the White Noise model's role in understanding and predicting SRB fluctuations. Our findings emphasize the AR(2) model's efficacy, attributed to its parsimonious complexity, empirical validation, theoretical alignment, and superior statistical performance. Extending projections to 2070 for Germany, our study not only offers foresight into future SRB trends but also contributes a robust methodology to the broader field of time series analysis.