Advanced Econometric Methods

Explore advanced econometric methods: discrete choice, time series, panel data, and more. Grasp key models and techniques for analyzing complex economic relationships. Enhance your ability to build robust models and derive meaningful insights for informed decision-making.

Advanced Econometric Methods

1 Discrete Dependent Variable Models

  1. Introduction
  2. Qualitative Choice Analysis
  3. The Regression Approach
  4. The Latent Regression Approach
  5. The Probit Model
  6. The Logit Model
  7. Estimation and Inference

2 Censored and Truncated Regression Models

  1. Characteristics of Qualitative Response Models
  2. Tobit Model
  3. Truncated Regression Model
  4. Sample Selection Model
  5. Models with Multiple Choices

3 Autoregressive (AR) Models

  1. Structure of AR Models
  2. Reasons for Inclusion of Lags in AR Models
  3. Use of Lag Operator in AR Models
  4. Inter-temporal Effect of Shocks in AR Models
  5. Relevance of AR Models to Economic Theory
  6. Yule-Walker Equations in AR Models
  7. Estimation of Parameters of AR Model
  8. Use of AR Models in Financial Economics

4 Distributed Lag Models

  1. Distributed Lag Models
  2. Koyck Model
  3. Autoregressive Models
  4. A More General Dynamic Model
  5. Jorgenson’s Rational Lag Model
  6. Partial Adjustment Model
  7. Adaptive Expectations Model
  8. Interpretation of Coefficients
  9. Estimation and Inference

5 Estimation of System of Equations

  1. Seemingly Unrelated Regression Equations (SURE)
  2. Generalized Least Squares (GLS)
  3. Feasible Generalized Least Squares (FGLS)
  4. Maximum Likelihood Estimates
  5. Hypothesis Testing
  6. Treating Autocorrelation
  7. Interrelated Factor Demand

6 Introduction to Simultaneous Equations Models

  1. Simultaneous Equations Model (SEM)
  2. Structural Form and Reduced Form
  3. Identification Problem
  4. Order Condition
  5. Rank Condition
  6. General Structure of SEM
  7. Simultaneity Bias

7 Estimation of Simultaneous Equations Models

  1. Limited Information Systems
  2. Full Information Systems

8 Specification Issues of Time Series Data Models

  1. Stochastic Process
  2. Detection of Unit Root – Graphical Examination
  3. Detection of Unit Root – Statistical Tests
  4. The KPSS Test
  5. Test for Unit Root in the Presence of Structural Break
  6. Relations among Non-Stationary Series
  7. Limitations of Engle-Granger Test

9 Modelling Univariate Time Series

  1. Autoregressive Models
  2. Moving Average Models
  3. ARMA Models
  4. Integrated Processes and the ARIMA Models
  5. Box-Jenkins Methodology
  6. ARIMA Modelling in Software R

10 Vector Auto-Regression (VAR) Models

  1. Specification and Estimation of VAR
  2. Uses of VAR
  3. Innovation Accounting
  4. Vector Autoregression of Non-Stationary Data

11 Modelling Volatility

  1. The Autoregressive Conditional Heteroscedasticity (ARCH) Model
  2. Properties of the ARCH Model
  3. Test for ARCH Effects
  4. Generalized-ARCH (GARCH) Model
  5. Extensions of the GARCH Model

12 Introduction to Panel Data Models

  1. Introduction
  2. Panel Data Models
  3. Fixed Effects Model
  4. Random Effects Model
  5. Choice between Fixed Effects and Random Effects Models
  6. Hausman Test

13 Dynamic Panel Data Analysis

  1. Static Panel Data Model
  2. Specification of Dynamic Panel Data Model
  3. Estimation Methods of Dynamic Panel data Models
  4. Arellano-Bond Estimator
  5. System-GMM Method of Estimation
  6. Problems with the Arellano-Bond Approach
  7. Maximum Likelihood Estimator

14 Introduction to Generalised Method of Moments Estimation

  1. Need for Generalized Method of Moments
  2. Additional Moments Restrictions and Generalized Method of Moments
  3. Leading Example of GMM: IV Regression in Overidentified Models
  4. Variance Estimation and Optimal GMM
  5. Estimating Optimal GMM – Two-Step GMM Estimator
  6. Test of Overidentifying Restrictions