Assignment writing. The data set provided below is 1,000 observations for the model…

Assignment 2:The data set provided below is 1,000 observations for the modelY=alpha+beta X + epsilon.Population parameters are alpha=1 and beta =3. Epsilon is distributed normal with zero mean and unitary variance.1- Split the data into sizes of 5 observations (first 5 observations of (Y,X), next 5 observations of (Y,X), etc.) for a total of 200 different data sets. Calculate the beta hat of these data sets and plot their sampling distribution. Is your estimator biased/unbiased? Calculate the degree of bias by bias = average (beta hats) – beta. What is the mean squared error (MSE)? Calculate the MSE by MSE = var(beta hat) + bias squared (beta hat).2- Repeat Question (1) by splitting the data into sizes of 20 observations for a total of 50 different data sets.3- Repeat Question (1) by splitting the data into sizes of 40 observations for a total of 25 different data sets.4- Repeat Question (1) by splitting the data into sizes of 50 observations for a total of 20 different data sets.5- Illustrate consistency of beta hat by using your results from (1)-(4) graphically.You can use Eviews, Stata, Excel or R as software.

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Assignment 2:

The data set provided below is 1,000 observations for the model

Y=alpha+beta X + epsilon.

Population parameters are alpha=1 and beta =3. Epsilon is distributed normal with zero mean and unitary variance.

1- Split the data into sizes of 5 observations (first 5 observations of (Y,X), next 5 observations of (Y,X), etc.) for a total of 200 different data sets. Calculate the beta hat of these data sets and plot their sampling distribution. Is your estimator biased/unbiased? Calculate the degree of bias by bias = average (beta hats) – beta. What is the mean squared error (MSE)? Calculate the MSE by MSE = var(beta hat) + bias squared (beta hat).

2- Repeat Question (1) by splitting the data into sizes of 20 observations for a total of 50 different data sets.

3- Repeat Question (1) by splitting the data into sizes of 40 observations for a total of 25 different data sets.

4- Repeat Question (1) by splitting the data into sizes of 50 observations for a total of 20 different data sets.

5- Illustrate consistency of beta hat by using your results from (1)-(4) graphically.

You can use Eviews, Stata, Excel or R as software.

The data set is:

AS2data.csv

ata, Excel or R as software.

The data set is:

AS2data.csv

del

Y=alpha+beta X + epsilon.

Population parameters are alpha=1 and beta =3. Epsilon is distributed normal with zero mean and unitary variance.

1- Split the data into sizes of 5 observations (first 5 observations of (Y,X), next 5 observations of (Y,X), etc.) for a total of 200 different data sets. Calculate the beta hat of these data sets and plot their sampling distribution. Is your estimator biased/unbiased? Calculate the degree of bias by bias = average (beta hats) – beta. What is the mean squared error (MSE)? Calculate the MSE by MSE = var(beta hat) + bias squared (beta hat).

2- Repeat Question (1) by splitting the data into sizes of 20 observations…

Attachments:

Assignment2.doc