Applied Economic Modelling: Econometrics, Forecasting, Machine Learning and Decision Methods for Management Research

Applied Economic Modelling brings together contemporary econometric, statistical, and data-driven methods for analysing economic and management problems. Moving from traditional economic modelling to modern artificial intelligence and machine learning approaches, the book demonstrates how quantitative techniques can support forecasting, research, and practical decision-making. The volume covers a broad range of methods, including Uncovered Interest Parity and financial hedging, time-series modelling and forecasting, artificial intelligence and machine learning, association rules and Market Basket Analysis, Two-Stage Least Squares, multinomial probit models, decision trees, multivariate analysis, and regression trees. Particular attention is given to translating quantitative models into management practice and business decisions, including applications involving large datasets, classification, forecasting, profitability, consumer behaviour, and risk assessment. Combining econometric reasoning with emerging computational methods, the book illustrates how modelling techniques can be applied to real economic, financial, and managerial questions. It also explores the use of decision trees and multivariate methods in areas such as consumer and firm classification and the principal–agent problem. A useful resource for researchers, students, academics, and professionals interested in economics, econometrics, management science, forecasting, business analytics, and data-driven decision-making.