Econometrics: Eviews and Risk Simulator Training Course

Introduction

General Econometrics Concept

• Comprehending fundamental econometric principles
• Analyzing variables and measurement frameworks
• Review of probability theory and confidence levels
• Statistical inference and percentile analysis
• Examination of theoretical probability distributions
• Application of significance tests and confidence interval methodologies
• Managing data asymmetry
• Evaluation of kurtosis
• Application of ANOVA techniques

Regression Analysis

• Core principles of regression modeling
• In-depth understanding of linear regression
• Methods for regression estimation
• Inference processes in regression analysis
• Review of underlying statistical assumptions
• Assessing assumption violations and their implications
• Identification and analysis of spurious regression
• Interpretation of diverse regression models
• Techniques for transforming variables
• Accurate interpretation of regression coefficients
• Comparison of linear and non-linear regression frameworks

Time Series Analysis

• Identification of time series components
• Application of various decomposition methods
• Analysis of trends, cycles, and seasonal patterns
• Execution of stationarity tests
• Interpretation of graphical representations and correlograms
• Conducting unit root tests
• Transformations applied to non-stationary time series
• Characteristics of stationary processes
• Managing complex transformations within models
• Economic forecasting and time series projection methods

Neural Networks

• Foundational concepts and methodologies of neural networks
• Structural composition of neural networks
• Overview of machine learning principles
• Differentiation between supervised and unsupervised learning
• Comparative analysis of machine learning and econometrics

Financial Risk Modeling

• Quantification of financial risks
• Calculation of occurrence probability
• Understanding the coefficient of variation
• Assessment of risk-adjusted capital

Markov Chain and Monte Carlo Simulation

• Conceptual understanding of simulation and modeling
• Analysis of fit distributions and probability metrics
• Development of scenario profiles
• Handling random inputs and outcome variables

Evaluating a Project

• Establishment of project selection criteria
• Analysis of demand elasticity
• Assessment of economic project feasibility
• Conducting risk-based breakeven analysis
• Evaluation of net cash flow dynamics
• Application of analytical tools
• Implementation of stress testing

Summary and Future Recommendations