Structural Equation Modeling (SEM) is a key framework for analyzing complex economic relationships involving latent variables, mediation effects, and endogeneity, yet the choice between frequentist and Bayesian estimation remains theoretically and practically contested, especially in settings with non-stationary data and small samples. This study provides a formal comparison of the two approaches by formulating SEM as a probabilistic graphical model and deriving the corresponding estimation procedures, identifiability conditions, and uncertainty measures. We examine asymptotic properties of frequentist estimators and posterior consistency in Bayesian SEM, with particular attention to integrated time-series SEM applications such as shadow economy estimation. The analysis shows that while both approaches converge under large-sample conditions, important differences arise in finite samples. Bayesian methods exhibit more stable inference through coherent uncertainty quantification and greater robustness to model misspecification, especially when prior theoretical information is available. In contrast, frequentist estimators rely more heavily on asymptotic assumptions that may be violated in typical economic datasets. These findings suggest that Bayesian SEM offers practical advantages for empirical economic modeling under realistic data constraints, without rejecting the theoretical validity of frequentist methods in large-sample settings.

Structural Equation Modeling (SEM) is a key framework for analyzing complex economic relationships involving latent variables, mediation effects, and endogeneity, yet the choice between frequentist and Bayesian estimation remains theoretically and practically contested, especially in settings with non-stationary data and small samples. This study provides a formal comparison of the two approaches by formulating SEM as a probabilistic graphical model and deriving the corresponding estimation procedures, identifiability conditions, and uncertainty measures. We examine asymptotic properties of frequentist estimators and posterior consistency in Bayesian SEM, with particular attention to integrated time-series SEM applications such as shadow economy estimation. The analysis shows that while both approaches converge under large-sample conditions, important differences arise in finite samples. Bayesian methods exhibit more stable point estimates through coherent uncertainty quantification, particularly when prior information regularizes an otherwise ill-conditioned likelihood. Under model misspecification, Bayesian posteriors concentrate around the pseudo-true parameter defined by the Kullback-Leibler projection, providing a probabilistic representation of misspecification uncertainty through posterior spread—an advantage over frequentist inference, which typically conditions on the maintained model as exact. These findings carry direct implications for empirical economic modeling under realistic data constraints. In settings where sample sizes are small, identification is weak, and model uncertainty is substantial, conditions that routinely characterize macroeconomic research, the choice of inferential framework is not a matter of philosophical preference but a determinant of whether policy-relevant conclusions can be credibly defended. Bayesian SEM offers a principled and transparent path forward in precisely these conditions.

Does access to transaction-level records affect which forecasting method public-finance analysts should choose? To answer this question, we compare four forecasting strategies applied to a short, highly seasonal time series of child-benefit payments: top-down, bottom-up, data-enrichment, and minimum-trace reconciliation. Our application concerns monthly child-benefit expenditure in Republika Srpska (Bosnia and Herzegovina), where we have approximately a hundred observations of total payments, but more than a million observations of individual payments. We also consider four different ways to deal with the recurring near-zero observations typical of administrative payment data. Under an expanding-window design, where each model can only use information available at the time of each forecast origin, exponential smoothing performs best across every treatment, with minimum-trace reconciliation yielding statistically equivalent accuracy. Enrichment with features from microdata offers no advantage when those features must be known in advance, while machine-learning techniques fail to outperform the benchmarks. The choice of treatment for near-zero observations proves as important as the forecasting method itself. Overall, in the presence of strong seasonality and short time-series, widely-used forecasting techniques remain hard to improve upon, and more data only helps if it provides genuinely forward-looking information.

Insurance plays a crucial role in economic development by providing financial security, encouraging investment, and reducing risks in the economy. In particular, the growth of life and non-life insurance premiums can contribute to economic stability and GDP growth by mobilizing capital and improving market efficiency. The aim of this analysis is to examine whether and to what extent the growth rates of gross life and non-life insurance premiums contribute to GDP growth in the European Union countries, both in the long and short run. The research is based on quarterly panel data for European Union countries, covering the period from the fourth quarter of 2017 to the fourth quarter of 2024. To analyze these interdependencies, the panel ARDL (Autoregressive Distributed Lag) model is employed, allowing for a consistent and efficient estimation of both long- and short-run effects of insurance on economic growth. This model accommodates variables of the same or different levels of integration, provided they are below I(2), ensuring more precise results. Given the limitations of the software environment, parameter estimation is conducted using the Pooled Mean Group (PMG) method, which allows for a reliable interpretation of the long-term relationship between insurance and economic growth while permitting heterogeneity in short-term effects across observational units.