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Time Series & Panel Data Models Using STATA Software
Highest Rated
Rating: 4.8 out of 5(22 ratings)
72 students

Time Series & Panel Data Models Using STATA Software

provided in Arabic Language
Highest Rated
Created byMahmoud Touny
Last updated 2/2023
Arabic
Arabic [Auto],

What you'll learn

  • Identify and conduct Diagnostic tests of the regression model
  • Detect Structural Breaks in Time Series
  • Check Stationary of Time Series Models (Unit root test)
  • Estimate Vector Autoregressive (VAR) Models & Granger causality test
  • Estimate Cointegration & Vector Error Correction Models
  • Estimate Panel Data Models (Fixed effects & Random-effects & Dynamic Panel data models)

Course content

6 sections • 18 lectures • 6h 36m total length

  • Introduction to the Course8:27

    • Introduction of the course

  • Introduction to STATA software14:22
  • Diagnostic tests of the regression model using STATA (Part 1)31:06

    in this lecture you will recognize the main assumptions of OLS regression and check the first three assumptions using STATA software

    • Assumptions of OLS regression.

    • Checking Linearity.

    • The error term has a population mean of zero.

    • Model Specification: omitted variables.


  • Diagnostic tests of the regression model using STATA (Part 2)37:42

    Check the rest of OLS assumptions using STATA

    • Serial correlation.

    • Heteroskedasticity.

    • Multicollinearity.

    • Normality of the error term

Requirements

  • Basic Econometrics

Description

This training course includes these training sessions:

Session (1):Introduction & Diagnostic tests of the regression model

This session includes:

  • introduction to STATA software

  • import data from excel file

  • descriptive statistics·

  • Assumptions of OLS regression.

  • Checking Linearity.

  • Model Specification: omitted variables.

  • Serial correlation.

  • Heteroskedasticity.

  • Multicollinearity.

  • Normality of the error term.

Session (2): Structural Breaks in Time Series

This session includes:

  • What are structural break models?

  • Types of a structural break.

  • How to detect structural breaks?

  • Known Breakpoints

  • Unknown Structural Breaks.

Session (3): Stationary of Time Series Models

This session includes:

  • Stationary & Non-Stationary time series.

  • Types of Non-Stationary time series.

  • Methods to check the stationarity of time series.

  • Autocorrelation Function (ACF) plot.

  • Unit root tests: Augmented Dickey-Fuller (ADF) Test & Phillips-perron test.

  • Unit root test for Panel Data.

Session (4): Vector Autoregressive (VAR) Models & Granger causality test

This session includes:

  • Vector autoregressive (VAR) model.

  • Choosing optimal lag length in the VAR model.

  • Stability of the VAR model.

  • Testing for Residual Autocorrelation.

  • The Granger causality test.

  • Impulse response functions (IRFs).

Session (5): Cointegration test and Error Correction Model

This session includes:

  • The concept of co-integration.

  • Engle-Granger co-integration.

  • Error Correction Model (ECT).

  • Johansen- Juselius cointegration analysis.

  • Vector Error Correction Model (VECM).

  • Autoregressive Distributed Lag (ARDL) model

  • Diagnostics tests (Goodness of fits).

Session (6): Panel Data Models

This session includes:

  • The concept of Panel Data.

  • Descriptive statistics of Panel Data.

  • Panel Unit Root Test.

  • Fixed effects & Random effects models.

  • Dynamic Panel data models:

    • Arellano and Bond (1991) estimator (difference GMM estimator)

    • Arellano and Bover (1995) (System GMM estimator)

  • Panel ARDL Model.

Who this course is for:

  • Researchers
  • Economists
  • Academics

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