## Questions and Answers

What is the primary purpose of the disturbance term in the simple linear regression model?

To model the unobserved influences on the dependent variable

What is the notation for the true coefficients in the simple linear regression model?

(β0, β1)

What is the predicted value of the dependent variable in the simple linear regression model?

y^ = β^0 + β^1x

What is the formula for the ordinary least squares (OLS) estimator in the simple linear regression model?

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What is the purpose of the residuals in the simple linear regression model?

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What is the notation for the matrix of explanatory variables in the linear regression model in matrix notation?

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What is the assumption made about the distribution of the disturbance term in the simple linear regression model?

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What is the purpose of the OLS estimator in the simple linear regression model?

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What is the OLS estimator in the context of linear regression?

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What is the purpose of conducting a simulation study in econometrics?

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What is the difference between the OLS estimator and the OLS estimate?

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What is the formula for the OLS estimator in simple linear regression?

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What is the effect of increasing the sample size on the distribution of the OLS estimator?

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What is the standard error of the OLS estimator in simple linear regression?

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What is the purpose of the simulation study in R?

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What is the relationship between the OLS estimator and the true parameters?

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## Study Notes

### Simple Linear Regression Model

- A simple linear regression model satisfies the relationship yt = β0 + β1xt + εt for all observations, where yt is the dependent variable, xt is the explanatory variable, and εt is a random variable that describes unobserved influences.
- β0 and β1 are the true coefficients, and εt is the disturbance (or error term).
- We will typically make some assumptions on the distribution of εt.

### Estimate, Predicted Value, and Residuum

- β^ is an estimate of the true parameter vector β.
- The predicted values (or fitted values) of y are given by y^ = β^0 + β^1x.
- The residuals (estimated values of the disturbance) are given by ε^ = y - y^ = y - β^0 - β^1x.
- The residuals ε^ are close to the true disturbances ε if our estimate β^ is close to the true parameters β.

### Ordinary Least Squares (OLS) Estimation

- An OLS estimate minimizes the sum of squared residuals.
- The OLS estimator β^ is given by the formula β^ = argmin Σ ε^2.
- In simple linear regression, the OLS estimator has the formula β^1 = Cov(xt, yt) / Var(xt).

### Linear Regression Model in Matrix Notation

- The linear regression model can be written in matrix notation as y = Xβ + ε, where X is a matrix of explanatory variables.
- The OLS estimator β^ is given by β^ = (X'X)^-1X'y, where X' is the transpose of X.

### Estimators and Estimates

- The OLS estimator β^ is a linear transformation of the true parameters β and the disturbance ε.
- The OLS estimator β^ is a random variable, and the OLS estimate β^ is a realization of the OLS estimator for particular draws of ε.
- To understand econometrics and statistics, it's essential to distinguish between an estimator (a random variable) and an estimate (a realization of the estimator).

### Distribution of OLS Estimator

- A Monte-Carlo simulation study shows that the OLS estimator β^ has a distribution that depends on the sample size T.
- The distribution of β^ changes if we change the sample size T.

### Standard Error of OLS Estimator

- In a simple linear regression, the standard deviation of the OLS estimator β^ can be estimated by σ / sqrt(Σ (xi - x̄)^2), where σ is the standard deviation of εt.

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## Description

Learn about the simple linear regression model, its components, and the endogeneity problem in market analysis with econometrics and machine learning.