ASSIGNMENT Econometrics

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ASSIGNMENT

MID-TERM EXAMINATION ECONOMETRICS

LECTURER:

ATIKA RUKMINASTITI MASRIFAH, M.E.SY.

xxx NIM. xx

ISLAMIC ECONOMICS DEPARTMENT FACULTY OF ECONOMICS & MANAGEMENT

UNIVERSITY OF DARUSSALAM GONTOR

1447/2025

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Part 1. Data

Periode LRCR Aset NPL MM

Jan-11 6.22 14.91 1.75 6.03

Feb-11 6.16 14.91 1.75 6.10

Mar-11 6.18 14.94 1.64 6.14

Apr-11 6.22 14.94 1.62 6.31

May-11 6.26 14.96 1.62 6.24

Jun-11 6.30 14.98 1.62 6.17

Jul-11 6.30 14.98 1.65 5.82

Aug-11 6.33 14.99 1.65 5.88

Sep-11 6.36 15.03 1.65 5.31

Oct-11 6.40 15.04 1.63 5.05

Nov-11 6.44 15.06 1.61 4.53

Dec-11 6.39 15.11 1.24 4.55

Jan-12 6.39 15.10 1.37 4.02

Feb-12 6.42 15.10 1.38 3.76

Mar-12 6.46 15.13 1.32 3.77

Apr-12 6.44 15.14 1.42 3.76

May-12 6.61 15.16 1.46 3.94

Jun-12 6.65 15.17 1.41 4.06

Jul-12 6.66 15.18 1.44 4.06

Aug-12 6.65 15.18 1.46 4.09

Sep-12 6.67 15.20 1.45 4.11

Oct-12 6.68 15.21 1.43 4.19

Nov-12 6.71 15.23 1.43 4.15

Dec-12 6.72 15.27 1.35 4.45

Jan-13 6.72 15.25 1.44 4.18

Feb-13 6.72 15.26 1.46 4.20

Mar-13 6.73 15.28 1.47 4.25

Apr-13 6.75 15.29 1.44 4.17

May-13 6.77 15.30 1.40 4.17

Jun-13 6.80 15.31 1.37 4.60

Jul-13 6.79 15.32 1.35 4.89

Aug-13 6.79 15.34 1.41 5.42

Sep-13 6.81 15.37 1.36 5.70

Oct-13 6.81 15.37 1.37 5.70

Nov-13 6.82 15.39 1.33 5.96

Dec-13 6.82 15.42 1.31 6.23

Jan-14 6.82 15.40 1.40 5.89

Feb-14 6.82 15.40 1.44 5.86

Mar-14 6.82 15.41 1.43 5.89

Apr-14 6.82 15.43 1.45 5.85

May-14 6.82 15.44 1.56 5.85

Jun-14 6.82 15.46 1.55 5.87

Jul-14 6.82 15.45 1.63 6.55

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Part 2. Running the Data with Linear Regression using Ordinary Least Squares (OLS)

Dependent Variable: LRCR Method: Least Squares Date: 06/15/25 Time: 09:06 Sample: 1 44

Included observations: 44

Variable Coefficient Std. Error t-Statistic Prob.

ASET 1.262482 0.056580 22.31326 0.0000

NPL 0.005172 0.086992 0.059456 0.9529

MM -0.037267 0.010744 -3.468580 0.0013

C -12.41953 0.919489 -13.50699 0.0000

R-squared 0.950396 Mean dependent var 6.602273 Adjusted R-squared 0.946676 S.D. dependent var 0.223074 S.E. of regression 0.051512 Akaike info criterion -3.007484 Sum squared resid 0.106141 Schwarz criterion -2.845285 Log likelihood 70.16465 Hannan-Quinn criter. -2.947333 F-statistic 255.4637 Durbin-Watson stat 0.537592 Prob(F-statistic) 0.000000

Classical OLS Assumptions 1. Normality Test

2. Heteroskedasticity Test

3. Autocorrelation Test

4. Multicollinearity Test

LRCR ASET NPL MM

LRCR 1.000000 0.963024 -0.539603 -0.092457 ASET 0.963024 1.000000 -0.478452 0.061100 NPL -0.539603 -0.478452 1.000000 0.499584 MM -0.092457 0.061100 0.499584 1.000000 Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 3.237180 Prob. F(3,40) 0.0321

Obs*R-squared 8.595745 Prob. Chi-Square(3) 0.0352 Scaled explained SS 7.367278 Prob. Chi-Square(3) 0.0611

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 20.92952 Prob. F(2,38) 0.0000

Obs*R-squared 23.06311 Prob. Chi-Square(2) 0.0000

0 1 2 3 4 5 6 7

-0.10 -0.05 -0.00 0.05

Series: Residuals Sample 1 44 Observations 44 Mean -1.78e-15 Median 0.015028 Maximum 0.061694 Minimum -0.121663 Std. Dev. 0.049683 Skewness -1.066934 Kurtosis 3.074144 Jarque-Bera 8.357958 Probability 0.015314

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Part 3. Interpreting the results of Linear Regression using OLS 1. The regression equation: example

2. Constant & Coefficients Term:

Intercept (-12.41953) suggests that … Asset (1.262482) indicates that … NPL (0.005172) suggests that … MM (-0.037267) indicates that … 3. T-Statistic and Prob (T-Statistic):

Prob (T-statistic) of Asset (0.0000) shows that the Asset is … Prob (T-statistic) of NPL (0.9529) shows that the NPL is … Prob (T-statistic) of MM (0.0013) shows that the MM is … 4. F-Statistic and Prob (F-Statistic):