Which one of the following statements is true for a linear regression model with non-spherical disturbances (i.e. E(uu') = Ω): The GLS estimator covariance matrix is unreasonable. The OLS estimator is not consistent. The standard formula for the OLS estimator covariance matrix is incorrect. The GLS estimator is not consistent. All of the above. None of the above.

Linear regression is maybe one in all the foremost documented and well understood algorithms in…

Answered: Which one of the following statements…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

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Problem 1QTC

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You estimated a regression with the following output. Source | SS df MS Number of obs = 289 -------------+---------------------------------- F(1, 287) = 41986.64 Model | 664544048 1 664544048 Prob > F = 0.0000 Residual | 4542496.25 287 15827.5131 R-squared = 0.9932 -------------+---------------------------------- Adj R-squared = 0.9932 Total | 669086544 288 2323217.17 Root MSE = 125.81 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 43.81013 .2138056 204.91 0.000 43.38931 44.23096 _cons | 49.31707 16.96222 2.91 0.004 15.93094 82.70319…
In an OLS regression, which value represents the "best" R2 in terms of explained variance in the dependent variable? A. 2.53 B. 16.22 C. .001 D. 0.53
Assignment-log linear model with dummy variable regressor A least squares regression model explaining the sample variation of LOGEARN,, the logarithm of reported weekly earnings for individual i, yields the estimated Bivariate Regression Model LOGEARN = 5.99+ .383 gender, + U₁ where gender, is a dummy variable which is, in this case, one for females and zero for males. The issue of how to evaluate the precision of the parameter estimates in a model such as this will be discussed in Chapters 6 and 7: you may ignore these issues in answering this question. a. What interpretation can be given to this estimated coefficient on the variable gender,? In particular, what can one say about how the expected value of the logarithm of weekly earnings depends on gender? b. What can one say about how the expected value of household earnings itself depends on gender? c. Suppose that it becomes apparent that females are, on average, better educated than males. Presuming that well-educated individuals…
The estimated regression models having a different number of explanatory variables are compared on the basis of _____. Select one: a. Chi squared -statistic b. Adjusted R squared-statistic c. R squared-statistic d. None of the above
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A website that rents movies online recorded the age and the number of movies rented during the past month for some of their customers. The data are shown below for a random sample of 25 of their customers.The regression line for the data, with number of movie rentals as the response variable, provides an intercept = 18.87, and slope = -0.228. The standard error of the slope SE(b1) = 0.0827. Margin of error ME for a 99% Confidence Interval for the slope of the Population regression line is: 0.1161 0.2322 0.4644 0.3483
The index of industrial production (IP) is a monthly time series that measures the quantity of industrial commodities produced in a given month. This problem uses data on this index for the United States. All regressions are estimated over the sample period 1986:M1 to 2017:M12 (that is, January 1986 through December 2017). Let Ye=1200 x In(IP/IP-1). Suppose that a forecaster estimates the following AR(4) model for Ye Ŷ=0.749 +0.071Y1 + 0.170Y 2 + 0.216Y 3 + 0.167Y(-4 (0.488) (0.088) (0.053) (0.078) (0.064) The forecaster wants to use this AR(4) to forecast the value of Ye in January 2018, using the following values of IP for July 2017 through December 2017: Date IP 2017:M7 2017:M8 2017:M9 2017:M10 2017:M11 2017:M12 105.01 104.56 104.82 106.58 106.86 107.30 The forecast value is a. The forecast value can not be computed with the given values. O b. 4.104 6.485 Od. 0.787
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You estimated a regression with the following output. Source | SS df MS Number of obs = 411 -------------+---------------------------------- F(1, 409) = 4098.54 Model | 22574040.7 1 22574040.7 Prob > F = 0.0000 Residual | 2252702.97 409 5507.83122 R-squared = 0.9093 -------------+---------------------------------- Adj R-squared = 0.9090 Total | 24826743.7 410 60553.0334 Root MSE = 74.215 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.727341 .1050822 64.02 0.000 6.520772 6.933909 _cons | -.7552724 9.26027 -0.08 0.935 -18.95894 17.44839…
None
Suppose we estimated our multiple linear regression, all of the variables have p values below 0.05 (so they are statistically different from zero) but the intercept has p-value equal to p=0.343. What does it mean?

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		The GLS estimator covariance matrix is unreasonable.
		The OLS estimator is not consistent.
		The standard formula for the OLS estimator covariance matrix is incorrect.
		The GLS estimator is not consistent.
		All of the above.
		None of the above.