Statistical and Econometric Modelling Training Course

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Course Code



21 hours (usually 3 days including breaks)


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



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