• Time-series dynamics and cross-equation methods emerged as a central paradigm, unifying causality testing, cross-spectral analysis, and multivariate estimation for dynamic economic relationships [4], [11], [18], [20].

• Econometric inference increasingly addressed autocorrelated errors and residual validity, giving rise to specialized tests, residual analysis, and corrections to LS methods [7], [2], [15], [8].

• A shift toward improved estimators, Bayesian approaches, and regularization expanded the toolbox for linear models and production function specifications, emphasizing robustness and flexibility [13], [12], [5], [14].

• Production function specification and frontier analysis, alongside macro-econometric modeling, mobilized structural assumptions and estimation strategies to analyze production, time allocation, and economic behavior [14], [19], [17], [18].