Online Econometrics Tutor SAMUEL

Since June 2024, I have been serving as a Professor of Macroeconometrics and Microeconometrics at Universidad Privada San Francisco de Asís (USFA), teaching at the Master’s level. Previously, I worked as a Teaching Assistant at Universidad Carlos III de Madrid from January 2022 to July 2023, where I supported courses in Econometrics and Game Theory for Bachelor’s programs. Earlier in my career, I also held a teaching role at Universidad Alberto Hurtado in July 2018, providing valuable academic support during that period.

✅Econometrics: Panel Data Analysis, Cross-Sectional Analysis, Generalized Method of Moments (GMM), Instrumental Variables (IV), Structural Equation Modeling (SEM), Fixed Effects Model, Random Effects Model, Difference-in-Differences (DiD), Bayesian Econometrics, Regression Analysis, Maximum Likelihood Estimation (MLE), Quantile Regression, Endogeneity, Causal Inference

✅Time Series Analysis: ARIMA Modeling, Seasonal Decomposition, Exponential Smoothing, State Space Models, Cointegration, Vector Autoregression (VAR), Autoregressive Moving Average (ARMA), Unit Root Testing, Granger Causality, Kalman Filter, Forecasting Accuracy, High-Frequency Data Analysis, Long Memory Processes, Frequency Domain Analysis

✅Tutoring (Microeconomics and Macroeconomics): Microeconomic Theory, Consumer Behavior, Market Structure Analysis, Game Theory, Perfect Competition, Monopolistic Competition, Macroeconomic Models, IS-LM Model, AD-AS Framework, Economic Growth Models, Monetary Policy, Fiscal Policy, International Trade, Public Economics, Labor Economics, Development Economics, Environmental Economics

✅DSGE Modeling: Dynamic Stochastic General Equilibrium (DSGE), New Keynesian Models, Real Business Cycle (RBC) Theory, Endogenous Growth Models, Log-Linearization, Shock Analysis, Calibration and Estimation, Stochastic Simulation, Impulse Response Functions (IRFs), Bayesian Estimation of DSGE Models, Policy Analysis with DSGE, Dynare, Model Selection, Parameter Estimation, Policy Rules

✅Data Science: Exploratory Data Analysis (EDA), Data Wrangling, Data Preprocessing, Dimensionality Reduction, Feature Selection, Data Cleaning, Hypothesis Testing, A/B Testing, Ensemble Methods, Clustering, Association Rules, Natural Language Processing (NLP), Big Data.

✅Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, Neural Networks, Support Vector Machines (SVM), Decision Trees, Random Forests, Gradient Boosting Machines (GBM), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA).