Econometrics: SLR and MLR

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Questions and Answers

Which of the following is NOT a required assumption for Ordinary Least Squares (OLS) estimators to be unbiased?

  • The error term has a zero population mean i.e. $E(u) = 0$
  • The error term is uncorrelated with the independent variables i.e. $E(u|x) = 0$
  • The error term is normally distributed (correct)
  • There is homoskedasticity i.e. constant variance of the error terms

In the context of the Gauss-Markov Theorem, what does the acronym BLUE stand for regarding OLS estimators?

  • Best Linear Unique Estimators
  • Basic Linear Unbiased Estimators
  • Best Log-likelihood Unbiased Estimators
  • Best Linear Unbiased Estimators (correct)

What is the key conceptual difference between the t-test and the F-test in the context of multiple linear regression?

  • The t-test is only applicable to simple linear regression, whereas the F-test is used in multiple linear regression.
  • The t-test assesses the individual significance of regression coefficients, while the F-test assesses the joint significance of a set of coefficients. (correct)
  • The t-test requires normally distributed errors, while the F-test does not.
  • The t-test is used for small samples whereas the F-test is used for large samples.

What does the property of 'homoskedasticity' imply about the error term in a regression model?

<p>The variance of the error term is constant across all levels of the independent variables. (D)</p> Signup and view all the answers

In the context of regression analysis, what does it mean to 'standardize' the relationship between two variables, $x$ and $y$, using $\frac{Cov(x, y)}{Var(x)}$?

<p>It isolates the impact of changes in x on y, independent of the scale or spread of x. (C)</p> Signup and view all the answers

Why is 'no perfect multicollinearity' a crucial assumption in multiple linear regression (MLR)?

<p>Perfect multicollinearity makes it impossible to estimate the individual effects of collinear variables. (B)</p> Signup and view all the answers

What is the interpretation of the error term, $u$, in the simple linear regression model, $Y = \beta_0 + \beta_1X + u$?

<p>Both A and B. (C)</p> Signup and view all the answers

In the context of the F-test, what hypothesis is being tested when comparing two regression models, one with restricted variables (SSRR) and one with unrestricted variables (SSRU)?

<p>The null hypothesis that the restricted variables have no effect on the dependent variable. (D)</p> Signup and view all the answers

Assume you have two independent variables, $X_1$ and $X_2$, where $X_{i1}$ is regressed on $X_{i2}$ resulting in $X_{i1} = \alpha_0 + \alpha_1X_{i2} + r_{i1}$. What does $r_{i1}$ (the residual) represent in the context of partialling-out method?

<p>The part of $X_{i1}$ that is uncorrelated with $X_{i2}$. (C)</p> Signup and view all the answers

If the p-value of a hypothesis test is 0.02, which of the following interpretations is most accurate?

<p>There is a 2% chance of observing the data, or data more extreme, if the null hypothesis is true. (B)</p> Signup and view all the answers

Consider a simple linear regression model: $y_i = \beta_0 + \beta_1x_i + u_i$. Given $\hat{\beta_1} = \frac{\sum_{i=1}^{n}(x_i - \bar{x})(y_i - \bar{y})}{\sum_{i=1}^{n}(x_i - \bar{x})^2}$, what is $E(\hat{\beta_1})$ if the OLS assumptions hold?

<p>$E(\hat{\beta_1}) = \beta_1$ (C)</p> Signup and view all the answers

In multiple linear regression, what does a higher $R^2$ value indicate?

<p>A higher $R^2$ suggests a better fit between the model and the data. (B)</p> Signup and view all the answers

In the context of estimating $\beta_0$ (the intercept) in a simple linear regression, given $\hat{\beta_0} = \bar{y} - \hat{\beta_1}\bar{x}$, what is being estimated and what does it represent?

<p>The predicted value of y when x is zero, representing the starting point of the regression line. (B)</p> Signup and view all the answers

Why is understanding the distribution of Ordinary Least Squares (OLS) estimators important in statistical inference?

<p>It is necessary for constructing confidence intervals and conducting hypothesis tests. (B)</p> Signup and view all the answers

Which of the following best illustrates the concept of 'zero conditional mean' assumption in the context of a regression model?

<p>The expected value of the error term is zero, conditional on any value of the independent variable. (B)</p> Signup and view all the answers

Consider two independent variables $x_1$ and $x_2$ in a multiple regression. If we find that the correlation between $x_1$ and $x_2$ is very high but not perfect, what is the most likely consequence?

<p>The standard errors of the coefficients for $x_1$ and $x_2$ will be inflated. (D)</p> Signup and view all the answers

In regression analysis, what could be the implication of not including relevant variables in your model?

<p>It can lead to biased coefficient estimates if the omitted variable is correlated with included variables. (C)</p> Signup and view all the answers

What condition must be satisfied to validate the use of t-tests when making inferences about population means?

<p>The underlying population or the sample means must be normally distributed. (C)</p> Signup and view all the answers

How does 'sampling variation' affect OLS assumptions and the reliability of regression results?

<p>It means that different samples yield different estimates, affecting the precision of OLS estimators. (C)</p> Signup and view all the answers

Why is random sampling important for Ordinary Least Squares (OLS) regression?

<p>It helps to ensure that the error term is uncorrelated with the independent variables, reducing the likelihood of selection bias. (A)</p> Signup and view all the answers

Flashcards

OLS Assumption 1: E(u)

The error term is expected to be zero across the population.

OLS Assumption 4: E(u|x)

Error does not depend on x. Zero Conditional Mean.

OLS Assumption 5: Homoskedasticity

Constant Variance.

Gauss-Markov Theorem Implies?

The best linear unbiased estimators of the regression coefficients.

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MLR Assumption 1: Linearity

Relationship between dependent and independent variables are linear

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MLR Assumption 5: Homoskedasticity

Variance of error term is constant across all observations.

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MLR Assumption 6: Normality

Error terms are normally distributed to validate tests

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P-Value

Provides a measure of evidence against the null hypothesis (H₀).

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R² Value

Measures proportion of the variance in the dependent variable that is predictable from the independent variable.

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F-Test

Primarily used to compare variances between two or more groups and is essential in assessing multiple linear restrictions in regression analysis.

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T-Test

Hypothesis testing concerning the means of one or two groups. Helps determine if there is a significant difference between the group means under the assumptions of normal distribution.

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MLR assumption: No Perfect Multicollinearity

Independent variables are not perfectly correlated

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MLR assumption: Zero Conditional Mean

Expected value of error terms are zero

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Study Notes

  • The notes cover topics from an Econometrics course
  • The topics include Simple Linear Regression (SLR) and Multiple Linear Regression (MLR)

SLR Proof

  • The SLR model is defined as y = β₀ + β₁x + u, where y is the dependent variable, x is the independent variable, β₀ and β₁ are the intercept and slope coefficients respectively, and u is the error term.
  • ŷ = β₀ + β₁xᵢ + ûᵢ , where ŷ is the predicted dependent variable
  • The Least Squares Proof aims to minimize the sum of squared errors (Q = Σûᵢ²)
  • The First Order Conditions (FOCs) are derived by taking the partial derivatives of Q with respect to β₀ and β₁ and setting them equal to 0.
  • ∂Q/∂β₀ = -2Σ(yᵢ - β₀ - β₁xᵢ) = 0
  • ∂Q/∂β₁ = -2Σxᵢ(yᵢ - β₀ - β₁xᵢ) = 0
  • Σ(yᵢ - β₀ - β₁xᵢ) = 0 and Σxᵢ(yᵢ - β₀ - β₁xᵢ) = 0
  • β₀ = ȳ - β₁x̄, where ȳ and x̄ are the sample means of y and x, respectively.
  • cov(x, y) = Σ((xᵢ - x̄)(yᵢ - ȳ))/n
  • var(x) = Σ(xᵢ - x̄)²/n
  • β₁ = cov(x, y) / var(x)
  • The reason is to assess how y changes per change in x without the influence of other variables via standardizing the relationship.

OLS Assumptions

  • E(u) = 0: The error term is expected to be zero across the population.
  • Random Sampling: The data is obtained through random sampling.
  • Sampling Variation: There is variation in the sample data.
  • E(u|x) = 0: The error term is uncorrelated with the independent variable. Zero Conditional Mean
  • Homoskedasticity: The error term has constant variance.

OLS Unbiasedness

  • The proof starts with the slope estimator β₁
  • β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) = Σ((xᵢ - x̄)(yᵢ - ȳ)) / SSTx, where SSTx is the total sum of squares of x.
  • β₁ = Σ((xᵢ - x̄)(β₀ + β₁xᵢ + uᵢ)) / SSTx
  • β₁ = (β₁Σ((xᵢ - x̄)xᵢ) + Σ((xᵢ - x̄)uᵢ)) / SSTx
  • E(β₁) = β₁ + (1/SSTx) ΣdᵢE(uᵢ), where dᵢ = xᵢ - x̄.
  • E(β₁) = β₁, which demonstrates that β₁ is an unbiased estimator of β₁.
  • Proof for β₀: β₀ = (β₀ + β₁x̄ + ū) - β₁x̄
  • E(β₀) = E(β₀) + x̄E(β₁) + E(ū) - x̄E(β₁)
  • E(β₀) = β₀, which proves that β₀ is an unbiased estimator of β₀.

Gauss-Markov Theorem

  • Under certain assumptions, the OLS estimators are the Best Linear Unbiased Estimators (BLUE) of the regression coefficients.

Standard Error

  • se(β₁) = √((s²)/Σ((xᵢ - x̄)²)), where s² = SSR/(n-2) in SLR
  • SSR = Σûᵢ²
  • Degrees of freedom are n-2.

MLR: Linearity

  • Relationship between dependent and independent variables are linear

Random Sampling

  • Observations are drawn randomly

MLR: No Perfect Multicolinearity

  • Independent variables are not perfectly correlated

MLR: Zero Conditional Mean

  • Expected value of error terms are zero

MLR: Homoskedasticity

  • Variance of error term is constant across all observations

MLR: Normality

  • Error terms are normally distributed to validate tests

OLS is BLUE

  • OLS is BLUE because it has the least variance, is linear, and unbiased.

MLR OLS Estimator

  • The MLR model is defined as yᵢ = β₀ + β₁xᵢ₁ + β₂xᵢ₂ + uᵢ
  • The predicted values are ŷᵢ = β₀ + β₁xᵢ₁ + β₂xᵢ₂ + ûᵢ
  • The residuals are defined as ûᵢ = yᵢ - ŷᵢ
  • The objective is to minimize Q = Σ(yᵢ - β₀ - β₁xᵢ₁ - β₂xᵢ₂)²
  • The First Order Conditions (FOCs)
  • ∂Q/∂β₀ = -2Σ(yᵢ - β₀ - β₁xᵢ₁ - β₂xᵢ₂) = 0
  • ∂Q/∂β₁ = -2Σ(yᵢ - β₀ - β₁xᵢ₁ - β₂xᵢ₂)xᵢ₁ = 0
  • ∂Q/∂β₂ = -2Σ(yᵢ - β₀ - β₁xᵢ₁ - β₂xᵢ₂)xᵢ₂ = 0

Partialling-out Method

  • Regress xᵢ₁ on xᵢ₂: xᵢ₁ = α₀ + α₁xᵢ₂ + rᵢ₁
  • r̂ᵢ₁ = xᵢ₁ - α̂₀ - α̂₁xᵢ₂
  • r̂ᵢ₁ is the variation in xᵢ₁ that is left after removing the variation in xᵢ₂.
  • Regress yᵢ on r̂ᵢ₁: yᵢ = β₀ + β₁r̂ᵢ₁ + uᵢ
  • β̂₁ = Σ((r̂ᵢ₁ - r̄)(yᵢ - ȳ)) / Σ((r̂ᵢ₁ - r̄)²) = cov̂(r̂ᵢ₁, yᵢ) / var̂(r̂ᵢ₁)
  • β̂₀ = ȳ - β̂₁x̄ - β̂₂x̄₂

MLR Inference

  • This includes hypothesis tests about population parameters and construction of confidence intervals.
  • Knowing the expected values and variances of the OLS estimators is not sufficient; understanding their distribution is critical.

T-Test

  • Hypothesis testing concerning the means of one or two groups, helps determine if there is a significant difference between the group means under the assumptions of normal distribution.
  • Best Use: Small sample sizes (<30)
  • The t-statistic is calculated as t = β̂₂ / se(β̂₂).
  • Reject the null hypothesis if |t| > t_crit

F-Test

  • Primarily used to compare variances between two or more groups and is essential in assessing multiple linear restrictions in regression analysis.
  • Best Use: Test ANOVA or to validate regression models; larger sample sizes are recommended.
  • The F-statistic is calculated as F = ((SSRR - SSRU) / q) / (SSRU / (N - k - 1)),
  • q is the number of restrictions
  • Reject the null hypothesis if F > F_crit

Results

  • P-Value: Provides a measure of evidence against the null hypothesis (H₀)
  • A low P-value (< 0.05) indicates strong evidence against H₀, suggesting a significant difference from zero
  • R² Value: Measures the proportion of the variance in the dependent variable that is predictable from the independent variable
  • A higher R² value suggests a better fit between the model and the data

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