Excel, Power BI & Tableau, STATA, R & RStudio and Positron and Python & Google Colab

Uploads in progress

Module 1: Introduction to Excel (Beginner)

• Understanding the Excel interface: Ribbon, workbook, worksheets, and cells.

• Basic navigation: Selecting, copying, pasting, and formatting cells.

• Saving, opening, and managing files in various formats (XLSX, CSV, etc.).

• Hands-on activity: Creating a simple spreadsheet.

Module 2: Data Entry and Formatting (Beginner)

• Entering and editing text, numbers, and dates.

• Formatting cells: Font styles, colors, borders, and alignment.

• Number formats: Currency, percentage, date, and custom formats.

• Hands-on activity: Creating a formatted table for monthly expenses.

Module 3: Basic Formulas and Functions (Beginner)

• Introduction to formulas: Operators and cell references (relative and absolute).

• Basic functions: SUM, AVERAGE, MIN, MAX, and COUNT.

• Error handling in formulas: Common errors and fixes.

• Hands-on activity: Calculating totals and averages for a dataset.

Module 4: Data Management and Organization (Intermediate)

• Sorting and filtering data for better analysis.

• Data validation: Creating dropdown lists and restricting inputs.

• Freezing panes and splitting windows for easier navigation.

• Hands-on activity: Organizing a sales dataset with filters and validation.

Module 5: Data Visualization (Intermediate)

• Creating charts: Bar, line, pie, and scatter charts.

• Customizing charts: Titles, labels, legends, and colors.

• Introduction to conditional formatting: Highlighting trends and patterns.

• Hands-on activity: Visualizing trends in a financial dataset.

Module 6: Advanced Formulas and Functions (Intermediate)

• Logical functions: IF, AND, OR, IFERROR.

• Lookup functions: VLOOKUP, HLOOKUP, INDEX-MATCH.

• Text functions: CONCATENATE, LEFT, RIGHT, TRIM, LEN.

• Date and time functions: TODAY, NOW, DATEDIF, WORKDAY.

• Hands-on activity: Analyzing employee attendance and performance data.

Module 7: PivotTables and PivotCharts (Intermediate)

• Creating and customizing PivotTables for data summarization.

• Using slicers and filters in PivotTables.

• Creating PivotCharts for dynamic data visualization.

• Hands-on activity: Analyzing a sales dataset using PivotTables and PivotCharts.

Module 8: Data Analysis Tools (Advanced)

• Using Excel’s Data Analysis ToolPak:

  • Descriptive statistics.

  • Regression analysis.

• Introduction to Solver for optimization problems.

• Hands-on activity: Running a regression analysis on economic data.

Module 9: Advanced Data Visualization and Dashboards (Advanced)

• Creating interactive dashboards with charts, slicers, and PivotTables.

• Using sparklines for compact data visualization.

• Best practices for dashboard design.

• Hands-on activity: Building a sales performance dashboard.

Module 10: Automation with Macros and VBA (Advanced)

• Recording and running macros for repetitive tasks.

• Advance to VBA: Writing basic scripts.

• Automating data cleaning and report generation.

• Hands-on activity: Automating a monthly financial report.

Module 11: Collaboration and Data Security (Advanced)

• Sharing workbooks and tracking changes.

• Protecting sheets and workbooks with passwords.

• Using Excel online for real-time collaboration.

• Hands-on activity: Collaborating on a shared project.

Uploads in progress

Module 1: Introduction to Data Visualization (Beginner)

• Importance of data visualization in decision-making.

• Overview of Power BI and Tableau: Key features and differences.

• Installing and setting up Power BI and Tableau.

• Understanding data types and sources.

Module 2: Getting Started with Power BI and Tableau (Beginner)

• Navigating the interfaces: Power BI Desktop and Tableau Workspace.

• Connecting to data sources (Excel, CSV, databases, cloud services).

• Basic data preparation: Cleaning and transforming data.

• Creating simple visualizations (bar charts, line graphs, pie charts).

• Saving and sharing reports.

Module 3: Data Modeling and Transformation (Intermediate)

• Power BI: Introduction to Power Query Editor.

• Tableau: Data blending and joins.

• Using calculated fields, measures, and parameters.

• Creating hierarchies and relationships in data.

• Handling missing and inconsistent data.

Module 4: Advanced Visualizations and Interactivity (Intermediate)

• Creating dashboards and storyboards.

• Adding interactivity: Filters, slicers, and drill-through actions.

• Power BI: Using bookmarks and dynamic visuals.

• Tableau: Creating actions for interactivity (filters, URL actions).

• Using maps for geospatial data visualization.

Module 5: Data Analysis and Calculations (Intermediate)

• Power BI: Introduction to DAX (Data Analysis Expressions).

  • Calculated columns and measures.

  • Time intelligence functions.

• Tableau: Advanced calculations using Tableau's calculated fields.

  • Table calculations (running totals, percent change).

  • LOD (Level of Detail) expressions.

Module 6: Connecting to Advanced Data Sources (Advanced)

• Connecting to live databases (SQL Server, Azure, Google BigQuery).

• Tableau: Using Tableau Prep for advanced data cleaning.

• Power BI: Working with dataflows and the Power BI Service.

• Handling real-time data with Power BI and Tableau.

Module 7: Advanced Dashboards and Reports (Advanced)

• Designing professional dashboards: Best practices.

• Power BI: Using themes and custom visuals.

• Tableau: Advanced dashboard actions and storytelling.

• Performance optimization for large datasets.

Mdule 8: Integration and Automation (Advanced)

• Integrating Power BI and Tableau with other tools (Excel, Python, R).

• Tableau: Using Tableau Public and Tableau Server.

• Power BI: Publishing to the Power BI Service and using Power BI Mobile.

• Automating data refresh and report delivery.

Module 9: Collaboration and Sharing (Advanced)

• Power BI: Role-level security and sharing reports securely.

• Tableau: Permissions and publishing on Tableau Server.

• Collaborating with teams using Power BI Workspaces and Tableau Projects.

• Embedding dashboards into websites or applications.

Uploads in progress

1. CROSS SECTIONAL MODELS

1.1 Ordinary Least Square (OLS)

1.2 Discrite Choice Models

1.2.1 Binary Choice Models

2.1.1 LPM

2.1.2 Logit model

2.1.3 Probit model

2.1.4 Complementary log–log regression

1.2.2 Multi-Response Models

1.2.2.1 Bivariateprobit

1.2.2.1.1 Simple Statndard Bivariate probit regression

1.2.2.1.2 Multiple Statndard Bivariate probit regression

1.2.2.1.3 Constrain Bivariate probit regression

1.2.2.1.4 Seemingly unrelated bivariate probit regression

1.2.2.1.5 Partial observability bivariate probit regression

1.2.2.2 Ordered Choice Models (ologit and oprobit)

1.2.2.2.1 Ordered ologit model

1.2.2.2.2 Ordered oprobit model

1.2.2.3 Multinomial

1.2.2.3.1 Multinomial logit model

1.2.2.3.2 Multinomial probit model

1.2.2.4 Conditional Logit

1.2.2.4.1 Nested Logit

1.2.2.4.2 Multivariate Probit

1.3 Limited Dependent Variable Models

1.3.1 Censored Models 

1.3.1.1 Type I Tobit or Standard Tobit (Dependent variable is censored from below or above)

1.3.1.2 Two limit Tobit

1.3.1.3 Type II Tobit 

1.3.1.4 Type III Tobit 

1.3.1.5 Panel Tobit 

1.3.1.6 Hurdle Tobit

1.3.2 Truncated Models 

1.3.2.1 Tobit with Truncation

1.3.3 Multivariate Tobit

1.3.4 Sample Selection Models (Heckman selection model)

2. TIME SERIES DATA MODELS

2.1 Univariate Time Series Models

2.1.1 Autoregressive (AR) Model

2.1.2 Moving Average (MA) Model

2.1.3 Autoregressive Moving Average (ARMA) Model

2.1.4 Autoregressive Integrated Moving Average (ARIMA) Model

2.1.5 Seasonal ARIMA (SARIMA) Model

2.1.6 Exponential Smoothing (ETS) Model

2.2 Multivariate Time Series Models

2.2.1 Vector Autoregression (VAR)

2.2.2 Vector Error Correction Model (VECM)

2.2.3 Structural Vector Autoregression (SVAR)

2.3 State-Space Models

2.3.1 Kalman Filter

2.3.2 Dynamic Linear Model (DLM)

2.3.3 Hidden Markov Model (HMM)

2.4 Non-Stationary and Trend Models

2.4.1 Unit Root Models

2.4.2 Difference Stationary Model

2.4.3 Trend Stationary Model

2.5 Long Memory Models

2.5.1 Autoregressive Fractionally Integrated Moving Average (ARFIMA)

2.6 Volatility Models

2.6.1 Autoregressive Conditional Heteroskedasticity (ARCH)

2.6.2 Generalized ARCH (GARCH)

2.6.3 Exponential GARCH (EGARCH)

2.6.4 Threshold GARCH (TGARCH)

2.7 Nonlinear Time Series Models

2.7.1 Threshold Autoregressive (TAR) Model

2.7.2 Smooth Transition Autoregressive (STAR) Model

2.7.3 Nonlinear Autoregressive Neural Network (NAR)

2.8 Machine Learning Models

2.8.1 Recurrent Neural Networks (RNN)

2.8.2 Long Short-Term Memory (LSTM)

2.8.3 Gated Recurrent Unit (GRU)

2.9 Frequency Domain Models

2.9.1 Spectral Analysis

2.9.2 Fourier Transform Models

2.10 Bayesian Time Series Models

2.10.1 Bayesian Structural Time Series (BSTS)

2.10.2 Bayesian VAR

2.11 Others

2.11.1 Markov Switching Models

2.11.2 Dynamic Factor Models

2.11.3 Cointegration Models

3. PANEL DATA MODELS

3.1 Static Panel Models or Linear Panel Data Models

3.1.1 Pooled Ordinary Least Squares (Pooled OLS)

3.1.2 Fixed Effects (FE) Model

3.1.3 Random Effects (RE) Model

3.1.4 Between Effects Model

3.1.5 First-Difference (FD) Model

3.2 Dynamic Panel Data Models

3.2.1 Arellano-Bond Generalized Method of Moments (GMM)

3.2.2 Arellano-Bover/Blundell-Bond System GMM

3.2.3 Instrumental Variable (IV) Models for Panel Data

3.3 Nonlinear Panel Data Models

3.3.1 Fixed Effects Logit Model

3.3.2 Fixed Effects Probit Model

3.3.3 Random Effects Logit/Probit Models

3.3.4 Tobit Model for Panel Data

3.4 Panel Data Models with Heterogeneity

3.4.1 Random Coefficients Model

3.4.2 Mixed Effects Model

3.5 Panel Time Series Models

3.5.1 Panel Unit Root Tests

3.5.2 Panel Cointegration Models

3.5.3 Dynamic Panel ARDL (Pooled Mean Group, Mean Group, Dynamic Fixed Effects)

3.6 Spatial Panel Data Models

3.6.1 Spatial Fixed Effects Model

3.6.2 Spatial Random Effects Model

3.6.3 Spatial Durbin Model

3.7 Bayesian Panel Data Models

3.7.1 Bayesian Fixed Effects Model

3.7.2 Bayesian Random Effects Model

3.8 Other Panel Data Models

3.8.1 Threshold Panel Data Models

3.8.2 Semiparametric and Nonparametric Panel Models

3.8.3 Quantile Regression for Panel Data

4. Impact Evaluation Models

4.1 Propensity Score Matching (PSM)

4.2 Difference in Difference (DID)

5. Specialized Econometric Models or Frameworks

SEM

Quantile Regressions

6. Machine Learning Models 

6.1 Supervised Learning Models

6.1.1 Regression Models

6.1.1.1. Linear Regression

6.1.1.2. Ridge Regression

6.1.1.3. Lasso Regression

6.1.1.4. Elastic Net

6.1.1.5. Polynomial Regression

6.1.1.6. Support Vector Regression (SVR)

6.1.1.7. Decision Tree Regression

6.1.1.8. Random Forest Regression

6.1.1.9. Gradient Boosting Regression (e.g., XGBoost, LightGBM, CatBoost)

6.1.1.10. K-Nearest Neighbors (KNN) Regression

6.1.2 Classification Models

6.1.2.1 Logistic Regression

6.1.2.2 Naive Bayes

6.1.2.3 Decision Tree Classifier

6.1.2.4 Random Forest Classifier

6.1.2.5 Support Vector Machine (SVM)

6.1.2.6 K-Nearest Neighbors (KNN) Classifier

6.1.2.7 Gradient Boosting Classifiers (e.g., XGBoost, LightGBM, CatBoost)

6.1.2.8 Neural Networks (Multi-Layer Perceptron)

6.1.2.9 Ensemble Methods (e.g., Bagging, Stacking)

6.2 Unsupervised Learning Models

6.2.1. K-Means Clustering

6.2.2. Hierarchical Clustering

6.2.3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

6.2.4. Gaussian Mixture Models (GMM)

6.2.5. Principal Component Analysis (PCA)

6.2.6. Independent Component Analysis (ICA)

6.2.7. t-SNE (t-Distributed Stochastic Neighbor Embedding)

6.2.8. Autoencoders

6.3 Semi-Supervised Learning Models

6.3.1. Self-Training Models

6.3.2. Co-Training Models

6.3.3. Graph-Based Models

6.4 Reinforcement Learning Models

6.4.1. Q-Learning

6.4.2. Deep Q-Learning (DQN)

6.4.3. Policy Gradient Methods

6.4.4. Actor-Critic Models

6.4.5. Proximal Policy Optimization (PPO)

6.4.6. Monte Carlo Methods

6.5 Deep Learning Models

6.5.1. Feedforward Neural Networks (FNN)

6.5.2. Convolutional Neural Networks (CNN)

6.5.3. Recurrent Neural Networks (RNN)

6.5.4. Long Short-Term Memory (LSTM)

6.5.5. Gated Recurrent Unit (GRU)

6.5.6. Transformer Models (e.g., BERT, GPT)

6.5.7. Variational Autoencoders (VAEs)

6.5.8. Generative Adversarial Networks (GANs)

6.6 Probabilistic Models

6.6.1. Bayesian Networks

6.6.2. Hidden Markov Models (HMM)

6.6.3. Gaussian Processes

6.7 Ensemble Models

6.7.1. Bagging (e.g., Random Forest)

6.7.2. Boosting (e.g., AdaBoost, Gradient Boosting, XGBoost, LightGBM)

6.7.3. Stacking

6.7.4. Blending

6.8 Time Series Models (Machine Learning-Based)

6.8.1. ARIMA with Machine Learning

6.8.2. Recurrent Neural Networks (RNN)

6.8.3. Long Short-Term Memory (LSTM)

6.8.4. Temporal Convolutional Networks (TCN)

6.8.5. Prophet Model

6.8.6. Gradient Boosting for Time Series

6.9 Specialized Models

6.9.1. Graph Neural Networks (GNN)

6.9.2. Sequence-to-Sequence Models

6.9.3. Attention Mechanisms (e.g., Transformers)

6.9.4. Reinforcement Learning with Deep Neural Networks

Uploads in progress

1. CROSS SECTIONAL MODELS

1.1 Ordinary Least Square (OLS)

1.2 Discrite Choice Models

1.2.1 Binary Choice Models

2.1.1 LPM

2.1.2 Logit model

2.1.3 Probit model

2.1.4 Complementary log–log regression

1.2.2 Multi-Response Models

1.2.2.1 Bivariateprobit

1.2.2.1.1 Simple Statndard Bivariate probit regression

1.2.2.1.2 Multiple Statndard Bivariate probit regression

1.2.2.1.3 Constrain Bivariate probit regression

1.2.2.1.4 Seemingly unrelated bivariate probit regression

1.2.2.1.5 Partial observability bivariate probit regression

1.2.2.2 Ordered Choice Models (ologit and oprobit)

1.2.2.2.1 Ordered ologit model

1.2.2.2.2 Ordered oprobit model

1.2.2.3 Multinomial

1.2.2.3.1 Multinomial logit model

1.2.2.3.2 Multinomial probit model

1.2.2.4 Conditional Logit

1.2.2.4.1 Nested Logit

1.2.2.4.2 Multivariate Probit

1.3 Limited Dependent Variable Models

1.3.1 Censored Models 

1.3.1.1 Type I Tobit or Standard Tobit (Dependent variable is censored from below or above)

1.3.1.2 Two limit Tobit

1.3.1.3 Type II Tobit 

1.3.1.4 Type III Tobit 

1.3.1.5 Panel Tobit 

1.3.1.6 Hurdle Tobit

1.3.2 Truncated Models 

1.3.2.1 Tobit with Truncation

1.3.3 Multivariate Tobit

1.3.4 Sample Selection Models (Heckman selection model)

2. TIME SERIES DATA MODELS

2.1 Univariate Time Series Models

2.1.1 Autoregressive (AR) Model

2.1.2 Moving Average (MA) Model

2.1.3 Autoregressive Moving Average (ARMA) Model

2.1.4 Autoregressive Integrated Moving Average (ARIMA) Model

2.1.5 Seasonal ARIMA (SARIMA) Model

2.1.6 Exponential Smoothing (ETS) Model

2.2 Multivariate Time Series Models

2.2.1 Vector Autoregression (VAR)

2.2.2 Vector Error Correction Model (VECM)

2.2.3 Structural Vector Autoregression (SVAR)

2.3 State-Space Models

2.3.1 Kalman Filter

2.3.2 Dynamic Linear Model (DLM)

2.3.3 Hidden Markov Model (HMM)

2.4 Non-Stationary and Trend Models

2.4.1 Unit Root Models

2.4.2 Difference Stationary Model

2.4.3 Trend Stationary Model

2.5 Long Memory Models

2.5.1 Autoregressive Fractionally Integrated Moving Average (ARFIMA)

2.6 Volatility Models

2.6.1 Autoregressive Conditional Heteroskedasticity (ARCH)

2.6.2 Generalized ARCH (GARCH)

2.6.3 Exponential GARCH (EGARCH)

2.6.4 Threshold GARCH (TGARCH)

2.7 Nonlinear Time Series Models

2.7.1 Threshold Autoregressive (TAR) Model

2.7.2 Smooth Transition Autoregressive (STAR) Model

2.7.3 Nonlinear Autoregressive Neural Network (NAR)

2.8 Machine Learning Models

2.8.1 Recurrent Neural Networks (RNN)

2.8.2 Long Short-Term Memory (LSTM)

2.8.3 Gated Recurrent Unit (GRU)

2.9 Frequency Domain Models

2.9.1 Spectral Analysis

2.9.2 Fourier Transform Models

2.10 Bayesian Time Series Models

2.10.1 Bayesian Structural Time Series (BSTS)

2.10.2 Bayesian VAR

2.11 Others

2.11.1 Markov Switching Models

2.11.2 Dynamic Factor Models

2.11.3 Cointegration Models

3. PANEL DATA MODELS

3.1 Static Panel Models or Linear Panel Data Models

3.1.1 Pooled Ordinary Least Squares (Pooled OLS)

3.1.2 Fixed Effects (FE) Model

3.1.3 Random Effects (RE) Model

3.1.4 Between Effects Model

3.1.5 First-Difference (FD) Model

3.2 Dynamic Panel Data Models

3.2.1 Arellano-Bond Generalized Method of Moments (GMM)

3.2.2 Arellano-Bover/Blundell-Bond System GMM

3.2.3 Instrumental Variable (IV) Models for Panel Data

3.3 Nonlinear Panel Data Models

3.3.1 Fixed Effects Logit Model

3.3.2 Fixed Effects Probit Model

3.3.3 Random Effects Logit/Probit Models

3.3.4 Tobit Model for Panel Data

3.4 Panel Data Models with Heterogeneity

3.4.1 Random Coefficients Model

3.4.2 Mixed Effects Model

3.5 Panel Time Series Models

3.5.1 Panel Unit Root Tests

3.5.2 Panel Cointegration Models

3.5.3 Dynamic Panel ARDL (Pooled Mean Group, Mean Group, Dynamic Fixed Effects)

3.6 Spatial Panel Data Models

3.6.1 Spatial Fixed Effects Model

3.6.2 Spatial Random Effects Model

3.6.3 Spatial Durbin Model

3.7 Bayesian Panel Data Models

3.7.1 Bayesian Fixed Effects Model

3.7.2 Bayesian Random Effects Model

3.8 Other Panel Data Models

3.8.1 Threshold Panel Data Models

3.8.2 Semiparametric and Nonparametric Panel Models

3.8.3 Quantile Regression for Panel Data

4. Impact Evaluation Models

4.1 Propensity Score Matching (PSM)

4.2 Difference in Difference (DID)

5. Specialized Econometric Models or Frameworks

SEM

Quantile Regressions

6. Machine Learning Models 

6.1 Supervised Learning Models

6.1.1 Regression Models

6.1.1.1. Linear Regression

6.1.1.2. Ridge Regression

6.1.1.3. Lasso Regression

6.1.1.4. Elastic Net

6.1.1.5. Polynomial Regression

6.1.1.6. Support Vector Regression (SVR)

6.1.1.7. Decision Tree Regression

6.1.1.8. Random Forest Regression

6.1.1.9. Gradient Boosting Regression (e.g., XGBoost, LightGBM, CatBoost)

6.1.1.10. K-Nearest Neighbors (KNN) Regression

6.1.2 Classification Models

6.1.2.1 Logistic Regression

6.1.2.2 Naive Bayes

6.1.2.3 Decision Tree Classifier

6.1.2.4 Random Forest Classifier

6.1.2.5 Support Vector Machine (SVM)

6.1.2.6 K-Nearest Neighbors (KNN) Classifier

6.1.2.7 Gradient Boosting Classifiers (e.g., XGBoost, LightGBM, CatBoost)

6.1.2.8 Neural Networks (Multi-Layer Perceptron)

6.1.2.9 Ensemble Methods (e.g., Bagging, Stacking)

6.2 Unsupervised Learning Models

6.2.1. K-Means Clustering

6.2.2. Hierarchical Clustering

6.2.3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

6.2.4. Gaussian Mixture Models (GMM)

6.2.5. Principal Component Analysis (PCA)

6.2.6. Independent Component Analysis (ICA)

6.2.7. t-SNE (t-Distributed Stochastic Neighbor Embedding)

6.2.8. Autoencoders

6.3 Semi-Supervised Learning Models

6.3.1. Self-Training Models

6.3.2. Co-Training Models

6.3.3. Graph-Based Models

6.4 Reinforcement Learning Models

6.4.1. Q-Learning

6.4.2. Deep Q-Learning (DQN)

6.4.3. Policy Gradient Methods

6.4.4. Actor-Critic Models

6.4.5. Proximal Policy Optimization (PPO)

6.4.6. Monte Carlo Methods

6.5 Deep Learning Models

6.5.1. Feedforward Neural Networks (FNN)

6.5.2. Convolutional Neural Networks (CNN)

6.5.3. Recurrent Neural Networks (RNN)

6.5.4. Long Short-Term Memory (LSTM)

6.5.5. Gated Recurrent Unit (GRU)

6.5.6. Transformer Models (e.g., BERT, GPT)

6.5.7. Variational Autoencoders (VAEs)

6.5.8. Generative Adversarial Networks (GANs)

6.6 Probabilistic Models

6.6.1. Bayesian Networks

6.6.2. Hidden Markov Models (HMM)

6.6.3. Gaussian Processes

6.7 Ensemble Models

6.7.1. Bagging (e.g., Random Forest)

6.7.2. Boosting (e.g., AdaBoost, Gradient Boosting, XGBoost, LightGBM)

6.7.3. Stacking

6.7.4. Blending

6.8 Time Series Models (Machine Learning-Based)

6.8.1. ARIMA with Machine Learning

6.8.2. Recurrent Neural Networks (RNN)

6.8.3. Long Short-Term Memory (LSTM)

6.8.4. Temporal Convolutional Networks (TCN)

6.8.5. Prophet Model

6.8.6. Gradient Boosting for Time Series

6.9 Specialized Models

6.9.1. Graph Neural Networks (GNN)

6.9.2. Sequence-to-Sequence Models

6.9.3. Attention Mechanisms (e.g., Transformers)

6.9.4. Reinforcement Learning with Deep Neural Networks

Uploads in progress

Uploads in progress

1. CROSS SECTIONAL MODELS

1.1 Ordinary Least Square (OLS)

1.2 Discrite Choice Models

1.2.1 Binary Choice Models

2.1.1 LPM

2.1.2 Logit model

2.1.3 Probit model

2.1.4 Complementary log–log regression

1.2.2 Multi-Response Models

1.2.2.1 Bivariateprobit

1.2.2.1.1 Simple Statndard Bivariate probit regression

1.2.2.1.2 Multiple Statndard Bivariate probit regression

1.2.2.1.3 Constrain Bivariate probit regression

1.2.2.1.4 Seemingly unrelated bivariate probit regression

1.2.2.1.5 Partial observability bivariate probit regression

1.2.2.2 Ordered Choice Models (ologit and oprobit)

1.2.2.2.1 Ordered ologit model

1.2.2.2.2 Ordered oprobit model

1.2.2.3 Multinomial

1.2.2.3.1 Multinomial logit model

1.2.2.3.2 Multinomial probit model

1.2.2.4 Conditional Logit

1.2.2.4.1 Nested Logit

1.2.2.4.2 Multivariate Probit

1.3 Limited Dependent Variable Models

1.3.1 Censored Models 

1.3.1.1 Type I Tobit or Standard Tobit (Dependent variable is censored from below or above)

1.3.1.2 Two limit Tobit

1.3.1.3 Type II Tobit 

1.3.1.4 Type III Tobit 

1.3.1.5 Panel Tobit 

1.3.1.6 Hurdle Tobit

1.3.2 Truncated Models 

1.3.2.1 Tobit with Truncation

1.3.3 Multivariate Tobit

1.3.4 Sample Selection Models (Heckman selection model)

2. TIME SERIES DATA MODELS

2.1 Univariate Time Series Models

2.1.1 Autoregressive (AR) Model

2.1.2 Moving Average (MA) Model

2.1.3 Autoregressive Moving Average (ARMA) Model

2.1.4 Autoregressive Integrated Moving Average (ARIMA) Model

2.1.5 Seasonal ARIMA (SARIMA) Model

2.1.6 Exponential Smoothing (ETS) Model

2.2 Multivariate Time Series Models

2.2.1 Vector Autoregression (VAR)

2.2.2 Vector Error Correction Model (VECM)

2.2.3 Structural Vector Autoregression (SVAR)

2.3 State-Space Models

2.3.1 Kalman Filter

2.3.2 Dynamic Linear Model (DLM)

2.3.3 Hidden Markov Model (HMM)

2.4 Non-Stationary and Trend Models

2.4.1 Unit Root Models

2.4.2 Difference Stationary Model

2.4.3 Trend Stationary Model

2.5 Long Memory Models

2.5.1 Autoregressive Fractionally Integrated Moving Average (ARFIMA)

2.6 Volatility Models

2.6.1 Autoregressive Conditional Heteroskedasticity (ARCH)

2.6.2 Generalized ARCH (GARCH)

2.6.3 Exponential GARCH (EGARCH)

2.6.4 Threshold GARCH (TGARCH)

2.7 Nonlinear Time Series Models

2.7.1 Threshold Autoregressive (TAR) Model

2.7.2 Smooth Transition Autoregressive (STAR) Model

2.7.3 Nonlinear Autoregressive Neural Network (NAR)

2.8 Machine Learning Models

2.8.1 Recurrent Neural Networks (RNN)

2.8.2 Long Short-Term Memory (LSTM)

2.8.3 Gated Recurrent Unit (GRU)

2.9 Frequency Domain Models

2.9.1 Spectral Analysis

2.9.2 Fourier Transform Models

2.10 Bayesian Time Series Models

2.10.1 Bayesian Structural Time Series (BSTS)

2.10.2 Bayesian VAR

2.11 Others

2.11.1 Markov Switching Models

2.11.2 Dynamic Factor Models

2.11.3 Cointegration Models

3. PANEL DATA MODELS

3.1 Static Panel Models or Linear Panel Data Models

3.1.1 Pooled Ordinary Least Squares (Pooled OLS)

3.1.2 Fixed Effects (FE) Model

3.1.3 Random Effects (RE) Model

3.1.4 Between Effects Model

3.1.5 First-Difference (FD) Model

3.2 Dynamic Panel Data Models

3.2.1 Arellano-Bond Generalized Method of Moments (GMM)

3.2.2 Arellano-Bover/Blundell-Bond System GMM

3.2.3 Instrumental Variable (IV) Models for Panel Data

3.3 Nonlinear Panel Data Models

3.3.1 Fixed Effects Logit Model

3.3.2 Fixed Effects Probit Model

3.3.3 Random Effects Logit/Probit Models

3.3.4 Tobit Model for Panel Data

3.4 Panel Data Models with Heterogeneity

3.4.1 Random Coefficients Model

3.4.2 Mixed Effects Model

3.5 Panel Time Series Models

3.5.1 Panel Unit Root Tests

3.5.2 Panel Cointegration Models

3.5.3 Dynamic Panel ARDL (Pooled Mean Group, Mean Group, Dynamic Fixed Effects)

3.6 Spatial Panel Data Models

3.6.1 Spatial Fixed Effects Model

3.6.2 Spatial Random Effects Model

3.6.3 Spatial Durbin Model

3.7 Bayesian Panel Data Models

3.7.1 Bayesian Fixed Effects Model

3.7.2 Bayesian Random Effects Model

3.8 Other Panel Data Models

3.8.1 Threshold Panel Data Models

3.8.2 Semiparametric and Nonparametric Panel Models

3.8.3 Quantile Regression for Panel Data

4. Impact Evaluation Models

4.1 Propensity Score Matching (PSM)

4.2 Difference in Difference (DID)

5. Specialized Econometric Models or Frameworks

SEM

Quantile Regressions

6. Machine Learning Models 

6.1 Supervised Learning Models

6.1.1 Regression Models

6.1.1.1. Linear Regression

6.1.1.2. Ridge Regression

6.1.1.3. Lasso Regression

6.1.1.4. Elastic Net

6.1.1.5. Polynomial Regression

6.1.1.6. Support Vector Regression (SVR)

6.1.1.7. Decision Tree Regression

6.1.1.8. Random Forest Regression

6.1.1.9. Gradient Boosting Regression (e.g., XGBoost, LightGBM, CatBoost)

6.1.1.10. K-Nearest Neighbors (KNN) Regression

6.1.2 Classification Models

6.1.2.1 Logistic Regression

6.1.2.2 Naive Bayes

6.1.2.3 Decision Tree Classifier

6.1.2.4 Random Forest Classifier

6.1.2.5 Support Vector Machine (SVM)

6.1.2.6 K-Nearest Neighbors (KNN) Classifier

6.1.2.7 Gradient Boosting Classifiers (e.g., XGBoost, LightGBM, CatBoost)

6.1.2.8 Neural Networks (Multi-Layer Perceptron)

6.1.2.9 Ensemble Methods (e.g., Bagging, Stacking)

6.2 Unsupervised Learning Models

6.2.1. K-Means Clustering

6.2.2. Hierarchical Clustering

6.2.3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

6.2.4. Gaussian Mixture Models (GMM)

6.2.5. Principal Component Analysis (PCA)

6.2.6. Independent Component Analysis (ICA)

6.2.7. t-SNE (t-Distributed Stochastic Neighbor Embedding)

6.2.8. Autoencoders

6.3 Semi-Supervised Learning Models

6.3.1. Self-Training Models

6.3.2. Co-Training Models

6.3.3. Graph-Based Models

6.4 Reinforcement Learning Models

6.4.1. Q-Learning

6.4.2. Deep Q-Learning (DQN)

6.4.3. Policy Gradient Methods

6.4.4. Actor-Critic Models

6.4.5. Proximal Policy Optimization (PPO)

6.4.6. Monte Carlo Methods

6.5 Deep Learning Models

6.5.1. Feedforward Neural Networks (FNN)

6.5.2. Convolutional Neural Networks (CNN)

6.5.3. Recurrent Neural Networks (RNN)

6.5.4. Long Short-Term Memory (LSTM)

6.5.5. Gated Recurrent Unit (GRU)

6.5.6. Transformer Models (e.g., BERT, GPT)

6.5.7. Variational Autoencoders (VAEs)

6.5.8. Generative Adversarial Networks (GANs)

6.6 Probabilistic Models

6.6.1. Bayesian Networks

6.6.2. Hidden Markov Models (HMM)

6.6.3. Gaussian Processes

6.7 Ensemble Models

6.7.1. Bagging (e.g., Random Forest)

6.7.2. Boosting (e.g., AdaBoost, Gradient Boosting, XGBoost, LightGBM)

6.7.3. Stacking

6.7.4. Blending

6.8 Time Series Models (Machine Learning-Based)

6.8.1. ARIMA with Machine Learning

6.8.2. Recurrent Neural Networks (RNN)

6.8.3. Long Short-Term Memory (LSTM)

6.8.4. Temporal Convolutional Networks (TCN)

6.8.5. Prophet Model

6.8.6. Gradient Boosting for Time Series

6.9 Specialized Models

6.9.1. Graph Neural Networks (GNN)

6.9.2. Sequence-to-Sequence Models

6.9.3. Attention Mechanisms (e.g., Transformers)

6.9.4. Reinforcement Learning with Deep Neural Networks