Economics

1. Introduction to Computational Macroeconometrics Overview of computational challenges in macroeconometrics The role of software in econometrics Introduction to Julia and MATLAB 2. Data Handling and Visualization Data sources and formats for macroeconomic data Data importing and preprocessing in Julia and MATLAB Data visualization techniques 3. Basic Time Series Analysis Univariate time series models: AR, MA, ARMA Implementing and forecasting using Julia and MATLAB Model selection and diagnostics 4. Advanced Time Series Techniques Vector autoregressions (VAR) and structural VAR (SVAR) 5. Simulation and Bootstrapping Techniques Monte Carlo simulations Bootstrapping for time series Practical examples in Julia and MATLAB 6. Nonlinear Optimization and Estimation Generalized Method of Moments (GMM) Bayesian methods and Markov chain Monte Carlo (MCMC) techniques 7. An introduction to machine learning for macroeconomic data Time series forecasting using machine learning techniques Variable selection and dimensionality reduction 8. High-Performance Computing in Econometrics Parallel computing and distributed computing basics Implementing parallel solutions in Julia and MATLAB Techniques for large-scale macroeconometric problems

- Basic understanding of macroeconomic concepts such as GDP, unemployment, inflation, and fiscal and monetary policies. - Familiarity with linear regression, identification, and endogeneity issues. Knowledge of the classical linear regression model's assumptions and diagnostic tests. - While the course itself delves deeper into time series econometrics, a basic understanding of ARMA/VAR model is beneficial - Basic knowledge of optimization problems and techniques, particularly constrained and unconstrained optimization. - Prior exposure to Julia or MATLAB would be advantageous. If not, then experience with similar computational software or languages can be beneficial.

Julia and Python documentation, tutorials, and online forums. Instructor notes.

It is mandatory.

Evaluating a hands-on, coding-intensive course like "COMPUTATIONAL TOOLS FOR MACROECONOMETRICS" requires a multifaceted approach that assesses both the technical proficiency of students and their ability to apply computational tools to address real-world macroeconometric challenges. Here's a suggested evaluation framework: 1. Weekly Coding Assignments (40%): Purpose: Assess students' ability to understand and implement the coding techniques introduced in each session. Format: Short coding exercises that ask students to preprocess data, implement models, run simulations, or interpret results. Evaluation Criteria: Correctness of code, efficiency of the solution, and the quality of the output or interpretation. 3. Final Project (60%): Purpose: Evaluate students' mastery of the course content and ability to apply computational techniques to a complex, open-ended problem. Format: A more extensive project where students might be asked to design a macroeconometric study, from data acquisition to policy recommendations. This could involve advanced modeling, simulations, and forecasting. Evaluation Criteria: Depth of analysis, quality of coding, innovation in approach, and the relevance and clarity of conclusions or policy recommendations.

1. Introduction to Computational Macroeconometrics Overview of computational challenges in macroeconometrics The role of software in econometrics Introduction to Julia and MATLAB 2. Data Handling and Visualization Data sources and formats for macroeconomic data Data importing and preprocessing in Julia and MATLAB Data visualization techniques 3. Basic Time Series Analysis Univariate time series models: AR, MA, ARMA Implementing and forecasting using Julia and MATLAB Model selection and diagnostics 4. Advanced Time Series Techniques Vector autoregressions (VAR) and structural VAR (SVAR) 5. Simulation and Bootstrapping Techniques Monte Carlo simulations Bootstrapping for time series Practical examples in Julia and MATLAB 6. Nonlinear Optimization and Estimation Generalized Method of Moments (GMM) Bayesian methods and Markov chain Monte Carlo (MCMC) techniques 7. An introduction to machine learning for macroeconomic data Time series forecasting using machine learning techniques Variable selection and dimensionality reduction 8. High-Performance Computing in Econometrics Parallel computing and distributed computing basics Implementing parallel solutions in Julia and MATLAB Techniques for large-scale macroeconometric problems

- Basic understanding of macroeconomic concepts such as GDP, unemployment, inflation, and fiscal and monetary policies. - Familiarity with linear regression, identification, and endogeneity issues. Knowledge of the classical linear regression model's assumptions and diagnostic tests. - While the course itself delves deeper into time series econometrics, a basic understanding of ARMA/VAR model is beneficial - Basic knowledge of optimization problems and techniques, particularly constrained and unconstrained optimization. - Prior exposure to Julia or MATLAB would be advantageous. If not, then experience with similar computational software or languages can be beneficial.

Julia and Python documentation, tutorials, and online forums. Instructor notes.

It is mandatory.

Evaluating a hands-on, coding-intensive course like "COMPUTATIONAL TOOLS FOR MACROECONOMETRICS" requires a multifaceted approach that assesses both the technical proficiency of students and their ability to apply computational tools to address real-world macroeconometric challenges. Here's a suggested evaluation framework: 1. Weekly Coding Assignments (40%): Purpose: Assess students' ability to understand and implement the coding techniques introduced in each session. Format: Short coding exercises that ask students to preprocess data, implement models, run simulations, or interpret results. Evaluation Criteria: Correctness of code, efficiency of the solution, and the quality of the output or interpretation. 3. Final Project (60%): Purpose: Evaluate students' mastery of the course content and ability to apply computational techniques to a complex, open-ended problem. Format: A more extensive project where students might be asked to design a macroeconometric study, from data acquisition to policy recommendations. This could involve advanced modeling, simulations, and forecasting. Evaluation Criteria: Depth of analysis, quality of coding, innovation in approach, and the relevance and clarity of conclusions or policy recommendations.