Econometrics: Eviews and Risk Simulator Training Course

Introduction

Fundamentals of Econometrics

• Review of core econometric principles
• Classification and measurement of variables
• Principles of probability and confidence levels
• Statistical inference and percentile analysis
• Theoretical probability distributions
• Hypothesis testing and confidence interval methodologies
• Analysis of distribution asymmetry
• Assessment of kurtosis
• Analysis of variance (ANOVA) techniques

Regression Modeling

• Core regression concepts
• Principles of linear regression
• Estimation methods for regression parameters
• Statistical inference in regression contexts
• Key statistical assumptions underlying models
• Detection of assumption violations and implications testing
• Identification and mitigation of spurious regression
• Overview of regression model types
• Variable transformation techniques
• Interpretation of regression coefficients
• Application of linear and non-linear regression models

Time Series Analysis

• Components of time series data
• Methods for data decomposition
• Analysis of trends, cycles, and seasonality
• Application of stationarity tests
• Interpretation of graphical outputs and correlograms
• Unit root testing procedures
• Transformation of non-stationary time series data
• Characteristics of stationary processes
• Application of complex transformations within models
• Economic and time series forecasting methods

Neural Network Applications

• Principles and methodology of neural networks
• Structure and composition of neural networks
• Overview of machine learning frameworks
• Distinction between supervised and unsupervised learning
• Comparison of machine learning and econometric approaches

Financial Risk Modeling

• Methodologies for risk measurement
• Calculation of occurrence probability
• Application of the coefficient of variation
• Determination of risk-adjusted capital requirements

Markov Chains and Monte Carlo Simulation

• Principles of simulation modeling
• Distribution fitting and probability analysis
• Development of analytical profiles
• Differentiation between random and outcome variables

Project Evaluation Frameworks

• Establishment of project selection criteria
• Analysis of demand elasticity
• Assessment of economic feasibility
• Risk-based break-even analysis
• Evaluation of net cash flows
• Utilization of analytical tools for decision support
• Conducting stress analysis scenarios

Summary and Next Steps