Econometrics and Data Analysis

1. Challenges in Econometrics
   1. Multicollinearity
      1. Definition and Causes
         1. Perfect Multicollinearity
         2. Near Multicollinearity
         3. High Correlation Among Predictors
      2. Detection Methods
         1. Variance Inflation Factor (VIF)
         2. Condition Index
         3. Correlation Matrix Analysis
      3. Consequences
         1. Inflated Standard Errors
         2. Unstable Coefficient Estimates
         3. Reduced Predictive Power
      4. Remedies
         1. Dropping Variables
         2. Combining Variables
         3. Principal Component Analysis (PCA)
         4. Ridge Regression
   2. Heteroscedasticity
      1. Definition and Causes
         1. Non-Constant Variance of Error Terms
         2. Differences in Scale Among Variables
         3. Outliers and Data Clustering
      2. Detection Methods
         1. Breusch-Pagan Test
         2. White Test
         3. Residual Plots
      3. Consequences
         1. Inefficient Estimators
         2. Invalid Hypothesis Tests
         3. Biased Confidence Intervals
      4. Remedies
         1. Weighted Least Squares (WLS)
         2. Generalized Least Squares (GLS)
         3. Robust Standard Errors
   3. Autocorrelation
      1. Definition and Causes
         1. Correlation Between Error Terms
         2. Time Series and Spatial Data Issues
         3. Model Misspecification
      2. Detection Methods
         1. Durbin-Watson Statistic
         2. Ljung-Box Test
         3. Residual Diagnostics
      3. Consequences
         1. Inefficient Estimators
         2. Misleading Inference
         3. Unsuitable Forecasting
      4. Remedies
         1. Cochrane-Orcutt Procedure
         2. Using Lagged Variables
         3. ARIMA Model Adjustment
   4. Model Specification Errors
      1. Incorrect Model Formulation
         1. Omitted Variable Bias
         2. Inclusion of Irrelevant Variables
         3. Functional Form Mis-specification
      2. Detection Methods
         1. Ramsey RESET Test
         2. Examination of Residuals
         3. Comparison of Model Fit Statistics
      3. Consequences
         1. Biased and Inconsistent Estimates
         2. Invalid Predictions
         3. Poor Policy Recommendations
      4. Remedies
         1. Thorough Exploratory Data Analysis
         2. Robust Model Building Strategies
         3. Cross-Validation Techniques
   5. Sample Selection Bias
      1. Definition and Causes
         1. Non-Random Sample Selection
         2. Self-Selection Models
         3. Non-Response in Surveys
      2. Detection Methods
         1. Heckman Correction Model
         2. Probit Model for Selection
      3. Consequences
         1. Misleading Coefficient Estimates
         2. Biased Inference
      4. Remedies
         1. Use of Instrumental Variables
         2. Corrective Modeling Techniques
   6. Endogeneity
      1. Definition and Causes
         1. Correlation Between Predictor and Error Term
         2. Measurement Error
         3. Simultaneity
      2. Detection Methods
         1. Hausman Test
         2. Tests for Overidentifying Restrictions
      3. Consequences
         1. Biased and Inconsistent Estimators
         2. Misleading Causal Inferences
      4. Remedies
         1. Instrumental Variables (IV) Technique
         2. Use of Structural Equation Models (SEM)
   7. Data Limitations
      1. Incomplete Data
         1. Missing Data Imputation
         2. Data Augmentation Techniques
      2. Poor Data Quality
         1. Data Cleaning and Validation
         2. Addressing Measurement Errors
      3. Small Sample Sizes
         1. Bootstrapping Methods
         2. Use of Bayesian Techniques
   8. Non-Stationarity
      1. Definition and Causes
         1. Trends and Unit Roots in Data
         2. Cyclical Patterns
      2. Detection Methods
         1. Augmented Dickey-Fuller Test
         2. Phillips-Perron Test
      3. Consequences
         1. Spurious Regression Results
         2. Unreliable Statistical Tests
      4. Remedies
         1. Differencing and Detrending Data
         2. Cointegration Analysis for Long-Term Relationships