Statistical and Econometric Modelling Training Course

Overview

The objective of the course is to enable participants to gain a mastery of the fundamentals of statistical and econometric modelling.

Course Outline

The Nature of Econometrics and Economic Data

• Econometrics and models
• Steps in econometric modelling
• Types of economic data, time series, cross-sectional, panel
• Causality in econometric analysis

Specification and Data Issues

• Functional form
• Proxy variables
• Measurement error in variables
• Missing data, outliers, influential observations

Regression Analysis

• Estimation
  1. Ordinary least squares (OLS) estimators
  2. Classical OLS assumptions,
  3. Gauss Markov-Theorem
  4. Best Linear Unbiased Estimators
• Inference
  1. Testing statistical significance of parameters t-test(single, group)
  2. Confidence intervals
  3. Testing multiple linear restrictions, F-test
  4. Goodness of fit
  5. Testing functional form
  6. Missing variables
  7. Binary variables
• Testing for violation of assumptions and their implications:
  1. Heteroscedasticity
  2. Autocorrelation
  3. Multicolinearity
  4. Endogeneity
• Other Estimation techniques
  1. Instrumental Variables Estimation
  2. Generalised Least Squares
  3. Maximum Likelihood
  4. Generalised Method of Moments

Models for Binary Response Variables

• Linear Probability Model
• Probit Model
• Logit Model
• Estimation
• Interpretation of parameters, Marginal Effects
• Goodness of Fit

Limited Dependent Variables

• Tobit Model
• Truncated Normal Distribution
• Interpretation of Tobit Model
• Specification and Estimation Issues

Time Series Models

• Characteristics of Time Series
• Decomposition of Time Series
• Exponential Smoothing
• Stationarity
• ARIMA models
• Co-Integration
• ECM model

Predictive Analysis

• Forecasting, Planning and Goals
• Steps in Forecasting
• Evaluating Forecast Accuracy
• Redisual Diagnostics
• Prediction Intervals