Econometrics

The references of most of the observations that econometricians have are ill defined. To use such data in an empirical analysis, the econometrician in charge must find a way to give them economic meaning. In this paper, I have data and an econometric model, and I set out to show how economic theory can be used to interpret the variables and parameters of my econometric model. According to Ragnar Frisch, that is a difficult task. Economic theories reside in a Model World and the econometrician’s data reside in the Real World; the rational laws in the model world are fundamentally different from the empirical laws in the real world; and between the two worlds there is a gap that can never be bridged To accomplish my task, I build a bridge between Frisch’s two worlds with applied formal-econometric arguments, invent a pertinent model-world economic theory, walk the bridge with the invented theory, and use it to give economic meaning to the variables and parameters of my econometric model. At the end I demonstrate that the invented theory and the bridge I use in my analysis are empirically relevant in the empirical context of my econometric model.

This study enhances macroeconometric modelling by utilising an I(2) cointegration framework to analyse the dynamic link between tourism prices and inflation in Slovenia and the Eurozone. Using monthly data from 2000 to 2017, we estimate cointegrated VAR models that capture long-run equilibria, short-run adjustments, and persistent deviations inherent in I(2) processes. The results reveal strong spillover effects from Slovenian tourism and input prices to Eurozone inflation and hospitality prices in the short run, while Eurozone-wide shocks dominate the long-run dynamics. By explicitly accounting for nonstationarity, structural breaks, and seasonal patterns, the I(2) model provides a more reliable framework than traditional I(1)-based approaches, which are often prone to misspecification when higher-order integration and persistent deviations are ignored. The findings contribute to macroeconometric theory by demonstrating the value of I(2) cointegration in modelling complex price systems and offer policy insights into inflation management and competitiveness in tourism-dependent economies.

This study examines the impact of international trade shocks from South Africa and recent U.S. federal tax reforms on state-level municipal bond returns within the United States. Employing a unique transaction-level dataset comprising more than 50 million municipal bond trades from 2020 to 2024, the empirical approach integrates machine learning estimators with econometric volatility models to examine daily nonlinear spillovers and structural complexity across twenty U.S. states. The study introduces and extends the application of a vector radial basis function neural network framework, leveraging its universal approximation capacity to jointly model multiple state-level outcomes and uncover complex response patterns The empirical results reveal substantial cross-state heterogeneity in bond-return resilience, influenced by variation in state tax regimes, economic complexity, and differential exposure to external financial forces. States exhibiting higher economic adaptability demonstrate faster recovery and weaker shock amplification, whereas structurally rigid states experience persistent volatility and slower mean reversion. These findings demonstrate that complexity-aware predictive modeling, when combined with granular fiscal and trade-linkage data, provides valuable insight into the pathways through which global and domestic shocks propagate into U.S. municipal bond markets and shape subnational financial stability.