📊 Econometrics

Master statistical methods for analyzing economic data and testing economic theories

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Econometrics Curriculum

12
Core Units
~110
Statistical Methods
20+
Estimation Techniques
70+
Applied Examples
1

Introduction to Econometrics

Learn the foundations of econometric analysis and its role in economic research.

  • What is econometrics?
  • Types of data
  • Economic models vs econometric models
  • Causality vs correlation
  • Steps in econometric analysis
  • Software introduction
  • Data sources
  • Research methodology
2

Probability and Statistics Review

Master essential probability and statistics concepts needed for econometric analysis.

  • Probability distributions
  • Expected value and variance
  • Joint and conditional distributions
  • Central limit theorem
  • Sampling distributions
  • Hypothesis testing
  • Confidence intervals
  • Maximum likelihood
3

Simple Linear Regression

Understand the basics of regression analysis with one explanatory variable.

  • Regression model assumptions
  • Ordinary least squares
  • Properties of OLS estimators
  • Gauss-Markov theorem
  • Goodness of fit
  • Hypothesis testing
  • Confidence intervals
  • Prediction intervals
4

Multiple Linear Regression

Extend regression analysis to models with multiple explanatory variables.

  • Multiple regression model
  • OLS estimation
  • Interpretation of coefficients
  • Omitted variable bias
  • Multicollinearity
  • F-tests
  • Dummy variables
  • Interaction terms
5

Regression Diagnostics

Learn to test and validate regression model assumptions and identify problems.

  • Residual analysis
  • Normality tests
  • Heteroskedasticity
  • Autocorrelation
  • Outliers and influence
  • Specification tests
  • Model selection criteria
  • Remedial measures
6

Violations of Classical Assumptions

Address problems when regression assumptions are violated and learn corrective methods.

  • Heteroskedasticity consequences
  • White's test
  • Weighted least squares
  • Robust standard errors
  • Autocorrelation detection
  • Durbin-Watson test
  • Generalized least squares
  • Newey-West estimator
7

Instrumental Variables

Learn to handle endogeneity problems using instrumental variables methods.

  • Endogeneity problem
  • Instrumental variables concept
  • Two-stage least squares
  • Instrument validity
  • Overidentification tests
  • Weak instruments
  • Applications in economics
  • Alternative methods
8

Panel Data Methods

Analyze data that varies across individuals and time using panel data techniques.

  • Panel data structure
  • Fixed effects model
  • Random effects model
  • Hausman test
  • First differences
  • Dynamic panel models
  • Unbalanced panels
  • Panel unit roots
9

Limited Dependent Variables

Study models for binary, categorical, and censored dependent variables.

  • Binary choice models
  • Linear probability model
  • Logit and probit models
  • Maximum likelihood estimation
  • Marginal effects
  • Multinomial models
  • Ordered models
  • Censored regression
10

Time Series Analysis

Analyze time-dependent data and understand temporal relationships in economics.

  • Time series concepts
  • Stationarity
  • Unit root tests
  • ARIMA models
  • Vector autoregressions
  • Cointegration
  • Error correction models
  • Forecasting
11

Advanced Topics

Explore specialized econometric methods for specific research problems.

  • Simultaneous equations
  • Treatment effects
  • Regression discontinuity
  • Difference-in-differences
  • Matching methods
  • Quantile regression
  • Spatial econometrics
  • Machine learning methods
12

Applied Econometrics

Apply econometric methods to real-world economic problems and policy evaluation.

  • Research design
  • Data collection and cleaning
  • Model specification
  • Robustness checks
  • Policy evaluation
  • Presenting results
  • Reproducible research
  • Case studies

Unit 1: Introduction to Econometrics

Learn the foundations of econometric analysis and its role in economic research.

What is Econometrics?

Understand the definition, scope, and purpose of econometric analysis in economics.

Statistical Analysis Economic Theory Data Driven
Econometrics is the application of statistical methods to economic data in order to give empirical content to economic relationships. It combines economic theory, mathematics, and statistical inference to quantify economic phenomena and test economic theories.
# Econometrics Definition Framework
econometrics = {
  "definition": "Statistical analysis of economic data",
  "components": {
    "economic_theory": "Provides hypotheses to test",
    "mathematics": "Formal model specification",
    "statistics": "Tools for estimation and inference",
    "data": "Empirical information for analysis"
  },
  "purposes": {
    "description": "Summarize economic relationships",
    "estimation": "Quantify economic parameters",
    "testing": "Validate economic theories",
    "forecasting": "Predict future economic outcomes",
    "policy_analysis": "Evaluate policy effectiveness"
  },
  "methodology": {
    "specification": "Choose appropriate model",
    "estimation": "Use data to estimate parameters",
    "evaluation": "Assess model adequacy",
    "interpretation": "Draw economic conclusions"
  }
}

Types of Data

Learn about different types of data used in econometric analysis and their characteristics.

Main Data Types:
• Cross-sectional: Different units at one point in time
• Time series: One unit over multiple time periods
• Panel data: Multiple units over multiple time periods
• Experimental vs observational data
Data Characteristics Matter:
• Sample size affects precision of estimates
• Data frequency impacts analysis possibilities
• Data quality determines reliability of conclusions
• Missing data creates analytical challenges
# Data Types Classification
data_types = {
  "cross_sectional": {
    "definition": "Different observations at single time point",
    "examples": ["Household survey", "Firm data", "Country comparison"],
    "advantages": ["Large sample sizes", "Variation across units"],
    "limitations": ["No time dimension", "Omitted variable bias"]
  },
  "time_series": {
    "definition": "Single unit observed over time",
    "examples": ["GDP over years", "Stock prices", "Unemployment rate"],
    "advantages": ["Dynamic relationships", "Trend analysis"],
    "limitations": ["Small samples", "Non-stationarity issues"]
  },
  "panel_data": {
    "definition": "Multiple units over multiple time periods",
    "examples": ["Household panel", "Firm panel", "Country panel"],
    "advantages": ["Control for unobserved heterogeneity", "Large samples"],
    "limitations": ["Complex structure", "Attrition problems"]
  },
  "data_quality": {
    "measurement_error": "Inaccurate data collection",
    "missing_data": "Incomplete observations",
    "selection_bias": "Non-random sampling",
    "data_mining": "Over-searching for results"
  }
}

Causality vs Correlation

Understand the crucial distinction between correlation and causation in econometric analysis.

Key Principle:
Correlation does not imply causation. Just because two variables move together doesn't mean one causes the other. Econometrics seeks to identify causal relationships, not just correlations.
Challenges in Establishing Causality:
• Omitted variable bias: Missing important variables
• Reverse causality: Y might cause X instead of X causing Y
• Simultaneity: X and Y might cause each other
• Selection bias: Non-random assignment to treatment
# Causality vs Correlation Framework
causality_analysis = {
  "correlation": {
    "definition": "Statistical association between variables",
    "measurement": "Correlation coefficient (-1 to +1)",
    "interpretation": "How variables move together",
    "limitations": "Doesn't imply causation"
  },
  "causation": {
    "definition": "One variable directly affects another",
    "requirements": ["Temporal precedence", "Covariation", "No confounders"],