Econometrics is crucial for fields like data science and finance, involving a variety of models and testing methods. This guide outlines common interview questions, covering topics such as data types, regression models, hypothesis testing, time series analysis, and the distinction between fixed and random effects in panel data.

AI is revolutionizing credit risk management in banking by enhancing predictive accuracy and efficiency through analyzing diverse data types, including non-traditional sources. Applications like dynamic credit scoring, fraud detection, and climate risk integration demonstrate its significance. However, challenges such as bias, explainability, and compliance remain crucial for successful implementation.

Multicollinearity can undermine regression models by causing unstable coefficient estimates and inflated standard errors. Ridge regression addresses this issue by adding a penalty term to minimize coefficients, leading to more reliable predictions. While not suitable for all cases, it is particularly effective when predictors are correlated or outnumber observations.

Bayesian inference and Maximum Likelihood Estimation (MLE) are key statistical methods for learning from data. MLE identifies parameters that maximize observed data likelihood, while Bayesian inference integrates prior beliefs with observed data, providing a distribution over possible parameters. Each method has unique strengths, with MLE being simpler and faster when data is plentiful.

The Expectation-Maximization (EM) algorithm is a two-step iterative method for estimating parameters in models with incomplete data. It addresses issues like missing values through an E-step (estimation) and M-step (maximization) process, ensuring non-decreasing likelihood, and is widely applicable in statistics and machine learning, despite some limitations.