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Principles of Econometric and Its Applications Using Statistical Analysis SPSS

Overview:

Introduction:

This training program provides an in-depth understanding of econometric principles and their applications, focusing on the use of SPSS for statistical analysis. It empowers participants to apply econometric techniques to real-world data, perform robust statistical analyses, and derive actionable insights.

Program Objectives:

By the end of this program, participants will be able to:

  • Understand the fundamental principles of econometrics.

  • Use SPSS for data management and statistical analysis.

  • Apply econometric models to analyze economic data.

  • Interpret econometric results for decision-making.

  • Perform diagnostic tests and ensure model validity.

Target Audience:

  • Economists.

  • Data analysts.

  • Researchers.

  • Statisticians.

  • Professionals using statistical tools for economic data analysis.

Program Outline:

Unit 1:

Introduction to Econometrics:

  • Overview of econometrics and its importance.

  • Types of econometric models.

  • Basic concepts: dependent and independent variables.

  • Understanding the assumptions of econometric models.

  • Introduction to SPSS for econometric analysis.

Unit 2:

Data Management in SPSS:

  • Importing and managing datasets in SPSS.

  • Data cleaning and preparation for analysis.

  • Handling missing data and outliers.

  • Creating and transforming variables in SPSS.

  • Data visualization techniques in SPSS.

Unit 3:

Simple and Multiple Regression Analysis:

  • Basics of simple linear regression.

  • Extending to multiple regression analysis.

  • Estimating and interpreting regression coefficients.

  • Hypothesis testing and significance levels.

  • Using SPSS for regression analysis.

Unit 4:

Econometric Applications in Time Series Analysis:

  • Understanding time series data and its characteristics.

  • Autocorrelation and stationarity in time series.

  • ARIMA models for time series forecasting.

  • Model selection and diagnostics for time series.

  • Applying time series analysis in SPSS.

Unit 5:

Panel Data Econometrics:

  • Introduction to panel data and its advantages.

  • Fixed effects vs. random effects models.

  • Estimating panel data models using SPSS.

  • Diagnostic testing for panel data models.

  • Applications of panel data in economic research.

Unit 6:

Econometric Models for Categorical Data:

  • Introduction to logistic regression and probit models.

  • Estimating binary outcome models in SPSS.

  • Interpreting results from categorical data models.

  • Diagnostic tests for categorical models.

  • Applications of categorical econometric models.

Unit 7:

Simultaneous Equation Models:

  • Understanding systems of simultaneous equations.

  • Identification problems in simultaneous equations.

  • Estimating simultaneous equations with SPSS.

  • Structural vs. reduced-form models.

  • Real-world applications of simultaneous equations.

Unit 8:

Diagnostic Testing in Econometrics:

  • Testing for multicollinearity, heteroskedasticity, and autocorrelation.

  • Performing model diagnostic tests in SPSS.

  • Ensuring the validity and reliability of econometric models.

  • Remedies for common econometric problems.

  • Practical examples of diagnostic testing.

Unit 9:

Forecasting with Econometric Models:

  • Introduction to forecasting techniques in econometrics.

  • Building forecasting models in SPSS.

  • Evaluating the accuracy of forecasts.

  • Application of econometric forecasting to economic data.

  • Best practices for developing reliable forecasts.

Unit 10:

Advanced Econometric Techniques:

  • Introduction to advanced topics: GMM, VAR, and VEC models.

  • Handling endogeneity in econometric models.

  • Structural equation modeling (SEM) in SPSS.

  • Practical applications of advanced econometric techniques.

  • Integrating advanced techniques into economic research.

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