Econometrics: Eviews and Risk Simulator Training Course

Introduction

General Econometrics Concepts for Government

• Understanding the foundational principles of econometrics
• Identifying variables and measurement techniques
• Overview of probability and confidence levels
• Statistical inference and percentile analysis
• Theoretical probability distributions for government applications
• Significance testing and confidence interval methods
• Addressing asymmetry in data
• Analyzing kurtosis
• Conducting ANOVA for government datasets

Regression Analysis for Government

• Core concepts of regression analysis
• Understanding linear regression models
• Techniques for regression estimation
• Methods for regression inference
• Evaluating statistical assumptions in government data
• Addressing assumption violations and their implications
• Identifying spurious regression in public sector datasets
• Exploring various regression models for government use
• Transforming variables to improve model fit
• Interpreting regression coefficients
• Applying linear and non-linear regression models in public sector analysis

Time Series Analysis for Government

• Key components of time series data
• Various decomposition methods for government datasets
• Analyzing trends, cycles, and seasonality in public sector data
• Conducting stationarity tests for government applications
• Interpreting graphs and correlograms for time series analysis
• Performing unit root tests on public sector data
• Transforming non-stationary time series data for government use
• Understanding stationary processes in government datasets
• Implementing complex transformations in time series models for government analysis
• Economic and time series forecasting techniques for government planning

Neural Networks for Government

• Understanding neural network concepts and methodologies for government applications
• Composition of neural networks in public sector models
• Overview of machine learning techniques for government use
• Differentiating between supervised and unsupervised learning in government datasets
• Comparing machine learning and econometrics approaches in the public sector

Financial Risk Modeling for Government

• Techniques for measuring financial risks in government projects
• Assessing the probability of risk occurrences
• Understanding the coefficient of variation in government financial data
• Adjusting capital for risk in public sector investments

Markov Chain and Monte Carlo Simulation for Government

• Understanding simulation concepts and modeling techniques for government use
• Fitting distributions and probability models to government data
• Creating profiles for public sector analysis
• Analyzing random and outcome variables in government datasets

Evaluating a Project for Government

• Establishing project selection criteria for government initiatives
• Understanding the elasticity of demand in public sector projects
• Assessing the economic feasibility of government projects
• Conducting risk breakeven analysis for government programs
• Evaluating net flow in government project assessments
• Utilizing analytical tools for public sector project evaluation
• Performing stress analysis on government projects

Summary and Next Steps