Unlock the power of econometrics with insights on key models and tools that help explain and forecast economic trends. Perfect for both beginners and advanced researchers.
Machine Learning in Econometrics enhances data analysis by addressing high-dimensional data, modeling nonlinear relationships, and improving predictions, while maintaining causal inference and theoretical rigor.
Quantile regression goes beyond mean-based analysis to uncover how relationships between variables vary across a distribution. Ideal for addressing heterogeneity, outliers, and skewed data, it
Structural breaks, caused by events like policy shifts or economic crises, disrupt time series analysis by altering relationships between variables. Addressing these breaks with methods
Bootstrap methods in econometrics provide robust, assumption-free statistical inference through resampling techniques. These methods excel in bias correction, confidence interval construction, and hypothesis testing, especially
Nonparametric and semiparametric methods provide flexible tools for analyzing complex economic data, capturing nonlinear patterns in income distribution, consumer behavior, and market trends.
Simultaneous Equations Models (SEMs) analyze interdependent relationships, capturing dynamics like supply-demand and macroeconomic feedback. Techniques like 2SLS and 3SLS ensure accurate estimates, supporting policy evaluation
Structural Equation Models (SEM) combine latent and observed variables to study complex relationships and causal pathways. By integrating regression, factor, and path analysis, SEM offers
Multivariate time series models analyze interdependencies among economic variables, capturing short-term effects and long-term relationships. Tools like VAR and VECM are essential for forecasting and
Discover how logit and probit models analyze binary outcomes like voting behavior or purchase decisions. These econometric tools estimate probabilities effectively, offering critical insights across
