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Econometrics: Beginner to Expert

Instructor led best course for Econometrics students to learn statistical modeling. Get hands on experience in Linear regression, Panel data modeling, time series forecasting and so.

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4.7

Rating

30 Hrs

Duration

Beginner

Level

25

Assignments

What you'll learn

Learn how to apply analytics and statistical modeling in economics.
Will get real case studies on demand forecasting and financial modeling.
Also, basic overview on R programming and its use in data analysis, visualization.

Who should take this course

This course is targeted at economics related students.
Also, anyone working in Marketing, sales or any business operation.
Anyone who wants to make career in econometrics.

Skills you'll gain

Data analysis
Data visualization
Descriptive statistics
Inferential statistics
Statistical modeling
Time series forecasting
Linear regression
Instrument variable
Simultaneous equation model
Panel data modeling
Basic R programming

Course contents

Cross-sectional, time-series, panel
Assignments

Sampling types
Central tendency
Measure of spread and position
Skewness
Probability
Case study: KPI reports

Simple to advance data visualization techniques
Case study on data story telling

Sampling
Confidence Interval
Hypothesis testing
Comparison test
Correlation test
Case study: Does a Job-Training Program Increase Wages

Model specification & assumptions
Ordinary Least Squares (OLS) estimation
Goodness of fit (R², adjusted R²)
Hypothesis tests (t-test, F-test)
Prediction & interpretation of coefficients
Case study: Analyzing relationship between product sales and marketing cost

Extending to multiple regressors
Multicollinearity: detection & remedies
Dummy variables & interaction terms
Model specification errors (omitted variables, wrong functional form)
Case study: Impact of house size, location, amenities on price

Case study: Effect of alcohol on car crashes using local tax changes as instrument

Endogenous vs. Exogenous Variables
Structural vs. Reduced Form
Identification Problem
Estimation Methods
Test Validity of Instruments
Interpret Results & Check Residuals
Case study: Estimate supply & demand for a commodity

Stationarity & unit root tests (ADF, KPSS)
AR, MA, ARMA, ARIMA models
Cointegration & error correction models
Granger causality
Forecasting method
Case study: Stock market prediction

Why multivariate time series
Introduction to VAR models and its variants
Model Estimation
Diagnostics
Impulse Response & Forecast Error Variance Decomposition
Case study: Analyzing forex rate

Fixed effects vs random effects models
Difference-in-differences
Dynamic panel models (Arellano–Bond)
Applications in micro & macroeconomic
Case study: Impact of minimum wage hikes on restaurant employment

Basic overview and syntax
Data structure, Conditional statement and Loops
Libraries for performing statistical analysis

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