31:T533,

The Consumer Price Index (CPI) serves as a critical economic indicator reflecting the overall prices of goods and services. While surveys of household consumption behaviors have been reliable to date, the survey-based method of compiling the CPI has recently been under strain. Price measurement has become more complex, thereby increasing demands on the data needed to attain accuracy, coverage, and timeliness.¹ This study introduces an integrated data pipeline incorporating a time-sensitive composite similarity model tailored for multivariate time-series correlation analysis to generate an alternative CPI utilizing price data from an e-commerce portal. The pipeline also offers an implementation of the preferred CPI forecasting model derived from an automated machine learning tool. The composite e-commerce index is 90.75% correlated with the official national food and non-alcoholic beverages CPI. Furthermore, ridge regression resulted in an R-squared value of 0.74 and an MAE of 1.269, hence proving that this novel approach can offer a robust complement to traditional methods for deriving a composite index and forecasting the CPI.

32:T4f6,

Survival analysis offers a sophisticated framework for examining infant and child mortality, facilitating time-to-event analysis and the identification of critical risk factors. This study leverages data from the 2018 Nigerian Demographic and Health Survey (NDHS) to evaluate the appropriateness of various modeling approaches. It uncovers substantial violations of the proportional hazards assumption in the Cox model, underscoring the need for alternative strategies when this assumption fails. To address these issues, regularization techniques such as Lasso, Ridge, and Elastic Net are employed to refine model fit. The Lasso model, in particular, enhances interpretability by selectively eliminating less significant covariates, while Ridge and Elastic Net contribute marginally to model improvement. Among parametric survival models, the Lognormal model proves most effective for analyzing infant mortality, whereas the Weibull model surpasses both the Exponential and Lognormal models in fitting child mortality data, as evidenced by lower AIC, BIC, and superior log-likelihood values. These results highlight the efficacy of Lasso in variable selection and emphasize the importance of choosing appropriate parametric models for precise mortality analysis.

33:T53f,

The cohort-component method is the standard model for producing population projections in official statistics. It is straightforward to compute, requires minimal input data, and is widely recognised by demographers. However, cohort-component projection models are limited in their ability to capture complex demographic processes and provide detailed output for individual-level outcomes. To address these shortcomings, Statistics Austria has developed the dynamic microsimulation model STATSIM for its official national population projection, which was previously computed by the cohort-component method. We have opted for a gradual transition, starting by replicating the results of past cohort-component projections using microsimulation. As a first extension, we have implemented a model of international migration that takes into account the relationship between emigration risk and length of stay, as well as country of birth. By comparing the results of STATSIM's retrospective projections with counterfactual cohort-component projections, we show that STATSIM's projections are more consistent with observed emigration patterns. In the future, STATSIM can be further developed by adding modules for education, employment, health and other socio-economic characteristics.

34:T549,

Luxembourg, known for its immigration history, attracts immigrants to work. This study analyses different immigrant g