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This study aims to evaluate linear mixed data sampling (MIDAS), nonlinear artificial neural networks (ANNs) and a hybrid approach for exploiting high-frequency information to improve low-frequency gross domestic product (GDP) forecasting. Their capabilities are assessed through direct forecasting comparisons.

This study compares quarterly GDP forecasts from unrestricted MIDAS (UMIDAS), standalone ANN and ANN-enhanced MIDAS models using five monthly predictors. Rigorous empirical analysis of recent US data is supplemented by Monte Carlo simulations to validate findings.

The empirical results and simulations demonstrate that the hybrid ANN-MIDAS performs best for short-term predictions, whereas UMIDAS is more robust for long-term forecasts. The integration of ANNs into MIDAS provides modeling flexibility and accuracy gains for near-term forecasts.

The model comparisons are limited to five selected monthly indicators. Expanding the variables and alternative data processing techniques may reveal further insights. Longer analysis horizons could identify structural breaks in relationships.

The findings guide researchers and policymakers in leveraging mixed frequencies amidst data complexity. Appropriate modeling choices based on context and forecast horizon can maximize accuracy.

Enhanced GDP forecasting supports improved policy and business decisions, benefiting economic performance and societal welfare. More accurate predictions build stakeholder confidence and trust in statistics underlying critical choices.

This direct forecasting comparison offers unique large-scale simulation evidence on harnessing mixed frequencies with leading statistical and machine learning techniques. The results elucidate their complementarity for short-term versus long-term modeling.

This study aimed to investigate the impact of leader STARA competence (LSC) – managing and implementing smart technologies, artificial intelligence, robotics and algorithms– on green competitiveness (GC) in the tourism and hospitality sectors. It also investigated the role of employee green creativity (EGC) as a mediator between LSC and GC and the mediating role of green human capital (GHC) in the relationship between EGC and GC.

The study utilized PLS-SEM to analyze 320 responses obtained from middle-level management at five-star hotels and travel agencies in Egypt, using WarpPLS statistical software 7.0.

Leader STARA competence positively affects employee green creativity and green competitiveness. Employee green creativity positively affects green competitiveness and green human capital. Green human capital positively affects green competitiveness. In addition, the study demonstrated significant mediation roles of green human capital in the employee green creativity and green competitiveness relationship and employee green creativity in the leader STARA competence and green competitiveness relationship.

The study offers several practical implications for tourism and hospitality enterprises. It underscores the significance of leader STARA’s competence in advancing green competitiveness.

The study provides new insights into how emerging concepts like leader STARA competence, green human capital and employee green creativity simultaneously predict green competitiveness within tourism and hospitality enterprises. It also contributes significantly to enriching the social exchange theory.