Topic 1: Econometrics as a validity tool: Power of rejection, correlation V.s. causality, and Benchmarks

Topic 2: Classical Linear Regression Assumptions and Hypothesis testing

Topic 3: Single and Multiple regression

Topic 4: Ordinary Least Squares (OLS): estimators calculation, efficiency, and unbiasedness estimators.

Topic 5: Specification problems: detection and remedy: e.g Autocorrelation, Hetrosk., Collinearity.

Topic 6: Dummy Variable Models - Setting up the models and interpretations

Topic 7: Weighted Least Square (WLS) and Generalised Least square (GLS).

Topic 1:Time Series Econometrics: Introduction, preparing dataset and data description

Topic 2: Stationarity and model selection criteria

Topic 3: Cointegration and causality 

Topic 4: HP filter and cyclical decomposition

Topic 5: HAC estimators 

Topic 6: ARDL models

Topic 7: Volatility models: ARCH and GARCH

Topic 8: Stationary time series models: VAR, VECM, VARX, and SVAR

Topic 9: In and out of sample forecasting

Topic 10: Introduction to Mixed Data Sampling (MIDAS)

Topic 1: Mathematical Concepts used in PCA e.g. Eigenvectors and values, etc

Topic 2: Theoretical foundations of PCA: setting data set 

Topic 3: Transforming the data and Covariance matrix formulation

Topic 4: Calculate Eigenvectors and Eigenvalues using the covariance matrix

Topic 5: Choosing components and forming a feature vector

Topic 6: Step by step codes for PCA on STATA

Topic 7: Explaining the inference of the results

Topic 8: Deriving principal components from the data

Topic 1 Introduction to R-Programming Language and R-studio Interface

Topic 2:  The basic concept of Wavelet and its applications

Topic 3: Decomposition and plotting of the series using the MODWT method.

Topic 4: Wavelet correlation, wavelet covariance, and cross-wavelet correlation

Topic 5: Continuous wavelet transform and cross wavelet transform

Topic 6: Wavelet coherence  and partial wavelet coherence 

Topic 7: Introduction to quantile on quantile and quantile autoregressive distributed lags error correction model to times series

Topic 8: Granger causality in Quantile on Quantile regression

Topic 9: Non-parametric quantile methods 

Topic 1 Introduction and setup

Topic 2:  Probability theory

Topic 3: Model inference

Topic 4: Probalistical programming

Topic 5: Bayesian AB testing

Topic 6: Hierarchical models

Topic 7: Simple linear regression

Topic 8: Hierarchical linear regression

Topic 9: Logistic regression

Topic 10: Bayesian Neural network

Topic 1: Introduction, Data structures, Statistics, and linear algebra

Topic 2: Solving system of equations, Optimization, and automatic differentiation

Topic 3: Gradient Descent, Gradient Descent optimization

Topic 4: Implementing Ordinary Least Squares, and Maximum Likelihood Estimation

Topic 5: Artificial Neural Networks, Mathematics of ANNs, Implementation, and a few examples

Topic 6: Machine Learning Algorithms,

Topic 7: Bootstrap Aggregation

Topic 8: Gradient Boosting

Topic 1: Introduction and Course Agenda

Topic 2: CGE modeling definition, uses, and scope

Topic 3: Difference between CGE, PE and Econometrics

Topic 4: CGE Model Structure

Topic 5: CGE Model steps

Topic 6: Walras Law 

Topic 7: Intro. to GTAP database

Topic 8: Running the first simulation

Topic 9: SAM and IO Tables+Practical Calibration exercises

Topic 1: Introduction and Data in R 

Topic 2: Regression Analysis in R

Topic 3: Univariate and Multivariate Time Series Analysis, Volatility models (ARIMA and GARCH systems) and Multivariate Endogenous Models (VAR Family)

Topic 4: Cross-section and Panel Data Analysis: Fixed, Random effects, and Dynamic models GMM.

Topic 5: Quantile Regression

Topic 6: Parametrics Regressions, linear regression, logistic regression, probit regression, and negative binomial regressions

Topic 7: Nonparametrics Regressions: splines and kernel regressions

Topic 1: Introduction and Data Processing in Python

Topic 2: Linear Regression Analysis in R

Topic 3: Univariate and Multivariate Time Series Analysis, Volatility models (ARIMA and GARCH systems) and Multivariate Endogenous Models (VAR Family)

Topic 4: Cross-section and Panel Data Analysis: Fixed, Random effects, and Dynamic models GMM.

Topic 5: Intro. to Machine Learning

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