We explore advanced and emerging applied econometric, statistical, and machine learning techniques tailored for early career researchers. Our platform offers guidance for graduates, lectures, and experts on the latest trends in data analysis, data analytics, and machine learning models using Python and Google colab. Join us in advancing your knowledge and skills in this dynamic field!
We explore advanced and emerging applied econometric, statistical, and machine learning techniques tailored for early career researchers. Our platform offers guidance for graduates, lectures, and experts on the latest trends in data analysis, data analytics, and machine learning models using Python and Google colab. Join us in advancing your knowledge and skills in this dynamic field!
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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)
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
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
Impact Evaluation Models
4.1 Propensity Score Matching (PSM)
4.2 Difference in Difference (DID)
Specialized Econometric Models or Frameworks
SEM
Quantile Regressions
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