what are the problems that autocorrelation created when using OLS regression in time series data.

Question

Question

Answer 1

Answer #1

Please give me a thumbs-up if this helps you out. Thank you! :)

Answer 2

Similar Homework Help Questions

[3] TRUE or FALSE: In the presence of autocorrelation, the OLS estimators remain unbiased but are...

[3] TRUE or FALSE: In the presence of autocorrelation, the OLS estimators remain unbiased but are no longer efficient. [4] TRUE or FALSE: In the presence of autocorrelation, the estimated OLS variances will be unbiased estimators of the correct OLS variances. [5] TRUE or FALSE: The core time series models are the regression or static model, the autoregressive model, the distributed lag model, and the autoregressive distributed lag model.
When using autoregressive regression analysis to find a best-fitting line to a set of time series...

When using autoregressive regression analysis to find a best-fitting line to a set of time series data with trend, we should use time period as the independent variable. true or false
(e) Suppose that we reject the null hypothesis, what does that imply about OLS estimatron of the ...

(e) Suppose that we reject the null hypothesis, what does that imply about OLS estimatron of the regression equation of ve? (Hint: does this problem affect unbiasedness or c ciency of OLS estimators?) (d) (10 pts bonus) Solve the problem by completely specifying the regression model. 630 pts) Suppose & is the residual of the following regression (a) If we are also running the regression what OLS assumption of time series data we suspect is violated (what time series prob-...

OLS regression is done when your independment varaible is what level of measurement

OLS regression is done when your independment varaible is what level of measurement
Distinguish between the following: Heteroskedasticity and autocorrelation specified regression model vs estimated regression equation data type...

Distinguish between the following: Heteroskedasticity and autocorrelation specified regression model vs estimated regression equation data type vs level of measurement ANOVA and Multiple Regression Outliers vs Influencers Distinguish between the following: Heteroskedasticity and autocorrelation specified regression model vs estimated regression equation data type vs level of measurement ANOVA and Multiple Regression Outliers vs Influencers
explain when you want to use an IV regression instead of the OLS regression.

explain when you want to use an IV regression instead of the OLS regression.

3. (a) Explain what you understand by the concept autocorrelation in the context of regression analysis mention the...

3. (a) Explain what you understand by the concept autocorrelation in the context of regression analysis mention the possible causes. and (b) Describe using standard notations, a simple linear regression model in which it is known that a first order autocorrelation is present. (c) For the model in (b) above, obtain a general term for the model error and comment on the (i) first moment (ii) second moment and (iii) autocovariance 3. (a) Explain what you understand by the concept...
Q3. [10 points [Serial Correlation Consider a simple linear regression model with time series dat...

Q3. [10 points [Serial Correlation Consider a simple linear regression model with time series data: Suppose the error ut is strictly exogenous. That is Moreover, the error term follows an AR(1) serial correlation model. That where et are uncorrelated, and have a zero mean and constant variance a. 2 points Will the OLS estimator of P be unbiased? Why or why not? b. [3 points Will the conventional estimator of the variance of the OLS estimator be unbiased? Why or...
Using the matrix formula for the OLS estimator of a linear regression, solve for the scalar...

Using the matrix formula for the OLS estimator of a linear regression, solve for the scalar formula for the coefficient estimates of the following regression:

Answer each question by writing TRUE or FALSE 1. For OLS estimators to be linear the...

Answer each question by writing TRUE or FALSE 1. For OLS estimators to be linear the explanatory variables must be variable, non- stochastic and fixed in repeated samples. Under the conditions of perfect multicollinearity, the OLS estimators are not unique. The presence of heteroskedasticity causes the OLS method to overestimate the variances 2. 3. of the parameters. The Breusch-Godfrey LM test is applicable when a lagged dependent variable is used. If we include a non-influential variable in an equation the...