Assuming the model is re-estimated with additional new variable (lagged of LGDP), based on the new estimated results, what is your conclusion about the autocorrelation diagnostic test? If there is diagnostic problem, show how to solve the problem. Table 2C: Estimated Regression ResultDependent Variable: LSDRMethod: Least SquaresSample (adjusted): 2000Q2 2012Q4Included observations: 51 after adjustmentsVariableLM2LREERLGDPLTBRLSDR(-1)сR-squaredAdjusted R-squaredS.E. of regressionSum squared residLog likelihoodF-statisticProb(F-statistic)Coefficient Std. Error t-StatisticProb.0.85930.02240.029879 0.167618 0.178259-0.199495 0.084326 -2.3657540.001576 0.207831 0.0075850.006450 0.0117330.99400.5497070.58520.717507 0.096076 7.468094 0.00001.100717 0.811970 1.3556130.1820Mean dependent var0.8224580.802731 S.D. dependent var0.068386Akaike info criterion0.210447Schwarz criterion67.63794 Hannan-Quinn criter.41.69215Durbin-Watson stat0.0000002.0904980.153970-2.417174-2.189900-2.3303261.003093

Assuming the model is re-estimated with additional new variable (lagged of LGDP), based on the new estimated results, what is your conclusion about the autocorrelation diagnostic test? If there is diagnostic problem, show how to solve the problem.

Assuming the model is re-estimated with additional new variable (lagged of LGDP), based on the new estimated results, what is your conclusion about the autocorrelation diagnostic test? If there is diagnostic problem, show how to solve the problem.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 5PPS

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Question

Assuming the model is re-estimated with additional new variable (lagged of
LGDP), based on the new estimated results, what is your conclusion about the
autocorrelation diagnostic test? If there is diagnostic problem, show how to
solve the problem.

Table 2C: Estimated Regression Result
Dependent Variable: LSDR
Method: Least Squares
Sample (adjusted): 2000Q2 2012Q4
Included observations: 51 after adjustments
Variable
LM2
LREER
LGDP
LTBR
LSDR(-1)
с
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
Coefficient Std. Error t-Statistic
Prob.
0.8593
0.0224
0.029879 0.167618 0.178259
-0.199495 0.084326 -2.365754
0.001576 0.207831 0.007585
0.006450 0.011733
0.9940
0.549707
0.5852
0.717507 0.096076 7.468094 0.0000
1.100717 0.811970 1.355613
0.1820
Mean dependent var
0.822458
0.802731 S.D. dependent var
0.068386
Akaike info criterion
0.210447
Schwarz criterion
67.63794 Hannan-Quinn criter.
41.69215
Durbin-Watson stat
0.000000
2.090498
0.153970
-2.417174
-2.189900
-2.330326
1.003093

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