Econometrics part 1 and 2 key terms

Chapter 1 econometric basics

Causal Effect Experimental Data
Ceteris Paribus Nonexperimental Data
Cross-Sectional Data Set Observational Data
Data Frequency Panel Data
Econometric Model Pooled Cross Section
Economic Model Random Sampling
Empirical Analysis Time Series Data

chapter 2 simple regression model

Coefficient of Determination
Constant Elasticity Model
Control Variable
Covariate
Degrees of Freedom
Dependent Variable
Elasticity
Error Term (Disturbance)
Error Variance
Explained Sum of Squares (SSE)
Explained Variable
Explanatory Variable
First Order Conditions
Fitted Value
Heteroskedasticity
Homoskedasticity
Independent Variable
Intercept Parameter
Ordinary Least Squares (OLS)
OLS Regression Line
Population Regression Function (PRF)
Predicted Variable
Predictor Variable
Regressand
Regression Through the Origin
Regressor
Residual
Residual Sum of Squares (SSR)
Response Variable
R-squared
Sample Regression Function (SRF)
Semi-elasticity
Simple Linear Regression Model
Slope Parameter
Standard Error of ˆ
1
Standard Error of the Regression (SER)
Sum of Squared Residuals
Total Sum of Squares (SST)
Zero Conditional Mean Assumption

Chapter 3 Multiple regression estimation

Best Linear Unbiased Estimator (BLUE)
Biased Towards Zero
Ceteris Paribus
Degrees of Freedom (df )
Disturbance
Downward Bias
Endogenous Explanatory Variable
Error Term
Excluding a Relevant Variable
Exogenous Explanatory Variables
Explained Sum of Squares (SSE)
First Order Conditions
Gauss-Markov Assumptions
Gauss-Markov Theorem
Inclusion of an Irrelevant Variable
Intercept
Micronumerosity
Misspecification Analysis
Multicollinearity
Multiple Linear Regression Model
Multiple Regression Analysis
Omitted Variable Bias
OLS Intercept Estimate
OLS Regression Line
OLS Slope Estimate
Ordinary Least Squares
Overspecifying the Model
Partial Effect
Perfect Collinearity
Population Model
Residual
Residual Sum of Squares
Sample Regression Function (SRF)
Slope Parameters
Standard Deviation of beta j
Standard Error of beta j
Standard Error of the Regression (SER)
Sum of Squared Residuals (SSR)
Total Sum of Squares (SST)
True Model
Underspecifying the Model
Upward Bias

Chapter 4 Multiple regression analysis and inference

Alternative Hypothesis
Classical Linear Model
Classical Linear Model (CLM)
Assumptions
Confidence Interval (CI)
Critical Value
Denominator Degrees of Freedom
Economic Significance
Exclusion Restrictions
F Statistic
Joint Hypotheses Test
Jointly Insignificant
Jointly Statistically Significant
Minimum Variance Unbiased Estimators
Multiple Hypotheses Test
Multiple Restrictions
Normality Assumption
Null Hypothesis
Numerator Degrees of Freedom
One-Sided Alternative
One-Tailed Test
Overall Significance of the Regression
p-Value
Practical Significance
R-squared Form of the F Statistic
Rejection Rule
Restricted Model
Significance Level
Statistically Insignificant
Statistically Significant
t Ratio
t Statistic
Two-Sided Alternative
Two-Tailed Test
Unrestricted Model

Chapter 5 Asymptotes and large numbers

Asymptotic Bias
Asymptotic Confidence Interval
Asymptotic Normality
Asymptotic Properties
Asymptotic Standard Error
Asymptotic t Statistics
Asymptotic Variance
Asymptotically Efficient
Auxiliary Regression
Consistency
Inconsistency
Lagrange Multiplier LM Statistic
Large Sample Properties
n-R-squared Statistic
Score Statistic


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