Assume that the data looks as follows:
Y = $\left( \begin{array} { l } Y _ { 1 } \\Y _ { 2 } \\O \\Y _ { n }\end{array}\right)$ , U = $\left( \begin{array} { c } u _ { 1 } \\u _ { 2 } \\\mathrm { O } \\u _ { n }\end{array} \right)$ , X = $\left( \begin{array} { l } X _ { 11 } \\X _ { 12 } \\\mathrm { O } \\X _ { 1 n }\end{array} \right)$ , and β = (β₁)
Using the formula for the OLS estimator $\hat { \beta }$ = ( $X ^ { \prime }$ X)^-¹
$X ^ { \prime }$ ^Y, derive the formula for $\hat { \beta }$ ₁, the only slope in this "regression through the origin."

Question

Assume that the data looks as follows:
Y =

\left( \begin{array} { l } Y _ { 1 } \\Y _ { 2 } \\O \\Y _ { n }\end{array}\right)

, U =

\left( \begin{array} { c } u _ { 1 } \\u _ { 2 } \\\mathrm { O } \\u _ { n }\end{array} \right)

, X =

\left( \begin{array} { l } X _ { 11 } \\X _ { 12 } \\\mathrm { O } \\X _ { 1 n }\end{array} \right)

, and β = (β₁)
Using the formula for the OLS estimator

\hat { \beta }

= (

X ^ { \prime }

X)^-¹

X ^ { \prime }

^Y, derive the formula for

\hat { \beta }

₁, the only slope in this "regression through the origin."

Quizplus · Accepted Answer

The Answer of Assume that the data looks as follows: Y = $ \left( \begin{array} { l } Y _ { 1 } \ Y _ { 2 } \ O \ Y _ { n } \end{array}\right)$ , U = $\left( \begin{array} { c } u _ { 1 } \ u _ { 2 } \ \mathrm { O } \ u _ { n } \end{array} \right)$ , X = $\left( \begin{array} { l } X _ { 11 } \ X _ { 12 } \ \mathrm { O } \ X _ { 1 n } \end{array} \right)$ , and β = (β₁) Using the formula for the OLS estimator $\hat { \beta }$ = ( $X ^ { \prime }$ X)^-¹ $X ^ { \prime }$ ^Y, derive the formula for $\hat { \beta }$ ₁, the only slope in this "regression through the origin."

Assume That the Data Looks as Follows: Y = (Y1Y2OYn) \left( \begin{array} { l } Y _ { 1 } \\Y _ { 2 } \\O \\Y _ { n }\end{array}\right)​Y1​Y2​OYn​​​

Assume That the Data Looks as Follows:
Y = $\left( \begin{array} { l } Y _ { 1 } \\Y _ { 2 } \\O \\Y _ { n }\end{array}\right)$