Lampiran. Lampiran 1 Data Penelitian TAHUN Y X1 X2 X3 X4_AS X4_JPG X4_INDH 1995

Similar documents
ANEXO 1 MODELO CON LM1 NOMINAL DESESTACIONALIZADO. Statistic

The Evolution of Snp Petrom Stock List - Study Through Autoregressive Models

Brief Sketch of Solutions: Tutorial 3. 3) unit root tests

Lampiran 1. Data Keuangan Perusahaan Yang Menjadi Sampel Penelitian

Lecture 8. Using the CLR Model

CHAPTER 6: SPECIFICATION VARIABLES

Exercise Sheet 6: Solutions

Outline. 2. Logarithmic Functional Form and Units of Measurement. Functional Form. I. Functional Form: log II. Units of Measurement

Economics 471: Econometrics Department of Economics, Finance and Legal Studies University of Alabama

About the seasonal effects on the potential liquid consumption

Descriptive Statistics

Romanian Economic and Business Review Vol. 3, No. 3 THE EVOLUTION OF SNP PETROM STOCK LIST - STUDY THROUGH AUTOREGRESSIVE MODELS

Econ 427, Spring Problem Set 3 suggested answers (with minor corrections) Ch 6. Problems and Complements:

November 9th-12th 2010, Port of Spain, Trinidad

Statistical Inference. Part IV. Statistical Inference

Eastern Mediterranean University Department of Economics ECON 503: ECONOMETRICS I. M. Balcilar. Midterm Exam Fall 2007, 11 December 2007.

Model Specification and Data Problems. Part VIII

Final Exam Financial Data Analysis at the University of Freiburg (Winter Semester 2008/2009) Friday, November 14, 2008,

The Simple Regression Model. Part II. The Simple Regression Model

Exercise Sheet 5: Solutions

Tjalling C. Koopmans Research Institute

Lecture 8. Using the CLR Model. Relation between patent applications and R&D spending. Variables

Econometrics Lab Hour Session 6

The GARCH Analysis of YU EBAO Annual Yields Weiwei Guo1,a

2. Linear regression with multiple regressors

Heteroskedasticity. Part VII. Heteroskedasticity

APPLIED MACROECONOMETRICS Licenciatura Universidade Nova de Lisboa Faculdade de Economia. FINAL EXAM JUNE 3, 2004 Starts at 14:00 Ends at 16:30

6. Assessing studies based on multiple regression

Bristol Business School

OLS Assumptions Violation and Its Treatment: An Empirical Test of Gross Domestic Product Relationship with Exchange Rate, Inflation and Interest Rate

5. Erroneous Selection of Exogenous Variables (Violation of Assumption #A1)

2) For a normal distribution, the skewness and kurtosis measures are as follows: A) 1.96 and 4 B) 1 and 2 C) 0 and 3 D) 0 and 0

10. Time series regression and forecasting

Heteroscedasticity 1

The general linear regression with k explanatory variables is just an extension of the simple regression as follows

ECON 366: ECONOMETRICS II. SPRING TERM 2005: LAB EXERCISE #10 Nonspherical Errors Continued. Brief Suggested Solutions

Frequency Forecasting using Time Series ARIMA model

Univariate linear models

Using the Autoregressive Model for the Economic Forecast during the Period

13. Time Series Analysis: Asymptotics Weakly Dependent and Random Walk Process. Strict Exogeneity

Practical Econometrics. for. Finance and Economics. (Econometrics 2)

ARDL Cointegration Tests for Beginner

Bristol Business School

Solutions: Monday, October 22

7. Prediction. Outline: Read Section 6.4. Mean Prediction

x = 1 n (x = 1 (x n 1 ι(ι ι) 1 ι x) (x ι(ι ι) 1 ι x) = 1

Problem Set 2: Box-Jenkins methodology

Outline. 11. Time Series Analysis. Basic Regression. Differences between Time Series and Cross Section

Research Center for Science Technology and Society of Fuzhou University, International Studies and Trade, Changle Fuzhou , China

Statistical and Econometric Methods for Transportation Data Analysis

Applied Econometrics. Applied Econometrics Second edition. Dimitrios Asteriou and Stephen G. Hall

unadjusted model for baseline cholesterol 22:31 Monday, April 19,

Problem set 1: answers. April 6, 2018

9. AUTOCORRELATION. [1] Definition of Autocorrelation (AUTO) 1) Model: y t = x t β + ε t. We say that AUTO exists if cov(ε t,ε s ) 0, t s.

Multiple Regression Analysis. Part III. Multiple Regression Analysis

1 Quantitative Techniques in Practice

4. Nonlinear regression functions

Answers to Problem Set #4

Practice Questions for the Final Exam. Theoretical Part

Regression with Qualitative Information. Part VI. Regression with Qualitative Information

7. Integrated Processes

Exercises (in progress) Applied Econometrics Part 1

4. Examples. Results: Example 4.1 Implementation of the Example 3.1 in SAS. In SAS we can use the Proc Model procedure.

11. Simultaneous-Equation Models

ECON 366: ECONOMETRICS II SPRING TERM 2005: LAB EXERCISE #12 VAR Brief suggested solution

Wednesday, October 17 Handout: Hypothesis Testing and the Wald Test

LAMPIRAN. NO Kode Perusahaan Nama Perusahaan. 1 ADRO Adaro Energy Tbk. 2 BSSR Baramulti Suksessarana Tbk. 3 GEMS Golden Energy Mines Tbk

Review Session: Econometrics - CLEFIN (20192)

in the time series. The relation between y and x is contemporaneous.

Lampiran 1. Hasil Determinasi Tanaman Umbi Singkong

Lecture#17. Time series III

Goce Delcev University-Stip, Goce Delcev University-Stip

13.2 Example: W, LM and LR Tests

CORRELATION, ASSOCIATION, CAUSATION, AND GRANGER CAUSATION IN ACCOUNTING RESEARCH

BUSINESS FORECASTING

Brief Suggested Solutions

PREDICTIONS AGGREGATION BY COUNTRY TO IMPROVE THE ACCURACY OF EUROPEAN UNION GDP RATE FORECASTS? Mihaela Simionescu *

3. Linear Regression With a Single Regressor

Item-Total Statistics. Corrected Item- Cronbach's Item Deleted. Total

Statistical and Econometric Methods for Transportation Data Analysis

7. Integrated Processes

THE INFLUENCE OF FOREIGN DIRECT INVESTMENTS ON MONTENEGRO PAYMENT BALANCE

STAT763: Applied Regression Analysis. Multiple linear regression. 4.4 Hypothesis testing

R Output for Linear Models using functions lm(), gls() & glm()

Econometric Analysis of Panel Data. Assignment 3

Financial Time Series Analysis: Part II

Failure Time of System due to the Hot Electron Effect

Rainfall Drought Simulating Using Stochastic SARIMA Models for Gadaref Region, Sudan

ECONOMETRIA II. CURSO 2009/2010 LAB # 3

TIME SERIES DATA ANALYSIS USING EVIEWS

Testing methodology. It often the case that we try to determine the form of the model on the basis of data

Time Series. Chapter Time Series Data

Thresholds and Regime Change in the Market for Renewable Identification Numbers.

End-Semester Examination MA 373 : Statistical Analysis on Financial Data

Introduction to Linear regression analysis. Part 2. Model comparisons

Model Building Chap 5 p251

Forecasting Foreign Direct Investment Inflows into India Using ARIMA Model

LATVIAN GDP: TIME SERIES FORECASTING USING VECTOR AUTO REGRESSION

Forecasting Seasonal Time Series 1. Introduction. Philip Hans Franses Econometric Institute Erasmus University Rotterdam

CAN SOLAR ACTIVITY INFLUENCE THE OCCURRENCE OF ECONOMIC RECESSIONS? Mikhail Gorbanev January 2016

Transcription:

Lampiran Lampiran 1 Data Penelitian TAHUN Y X1 X2 X3 X4_AS X4_JPG X4_INDH 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 3,107,163 3,102,431 3,443,555 2,713,611 2,606,216 2,437,764 2,294,796 2,891,996 5,022,203 4,563,075 5,523,901 7,820,899 9,261,977 6,460,117 6,327,254 9,147,778 11,838,269 10,393,936 9,598,008 9,361,110 2308 7.24 13.34 79.4755 254.468 356.871 2383 8.7 14.26 79.433 256.749 203.326 4650 13.1 17.38 79.518 252.187 428.574 8025 83.56 37.93 79.348 261.311 378.125 7100 1.37 12.64 79.688 243.063 361.026 9595 5.73 14.31 79.008 279.559 413.304 10400 14.79 17.63 288.321 515.421 870.356 8940 9.59 13.12 235.951 556.473 752.043 8465 4.23 8.34 251.049 520.399 1095.313 9270 6.8 7.29 251.049 520.399 1095.313 9830 22.41 12.83 255.233 498.32 851.483 9020 6.11 9.8 235.423 543.002 959.107 9419 6.6 8 215.69 471.667 1290.556 10950 10.72 10.8 200.114 441.977 1780.186 9400 2.61 6.5 223.549 395.822 1676.102 9036 8 6.26 205.053 449.535 1719.004 9336 3.67 6.58 201.606 446.45 1312.914 9384 3.86 5.77 230.76 460.308 1414.701 10460 10.18 7 278.648 473.159 968.262 11878 8.17 7.52 382.941 372.942 650.686 67

Lampiran 2 Data penelitian Tahun LOG_Y LOG_X1 LOG_X2 LOG_X3 LOG_X4_AS LOG_X4_JPG LOG_X4_INDH 1995 6.492364 3.363236 0.859739 1.125156 1.900233 2.405633 2.552511 1996 6.491702 3.377124 0.939519 1.15412 1.900001 2.409509 2.308193 1997 6.537007 3.667453 1.117271 1.24005 1.900465 2.401723 2.632026 1998 6.433548 3.904445 1.921998 1.578983 1.899536 2.417158 2.577635 1999 6.41601 3.851258 0.136721 1.101747 1.901393 2.385719 2.557538 2000 6.386992 3.982045 0.758155 1.15564 1.897671 2.446473 2.61627 2001 6.360744 4.017033 1.169968 1.246252 2.459876 2.712162 2.939697 2002 6.461198 3.951338 0.981819 1.117934 2.372822 2.745444 2.876243 2003 6.700894 3.927627 0.62634 0.921166 2.399758 2.716336 3.039538 2004 6.659258 3.96708 0.832509 0.862728 2.399758 2.716336 3.039538 2005 6.742246 3.992554 1.350442 1.108227 2.406937 2.697508 2.930176 2006 6.893257 3.955207 0.786041 0.991226 2.371849 2.734801 2.981867 2007 6.966704 3.974005 0.819544 0.90309 2.33383 2.673635 3.110777 2008 6.81024 4.039414 1.030195 1.033424 2.301277 2.6454 3.250465 2009 6.801215 3.973128 0.416641 0.812913 2.349373 2.5975 3.2243 2010 6.961316 3.955976 0.90309 0.796574 2.311866 2.652764 3.235277 2011 7.073288 3.970161 0.564666 0.818226 2.304503 2.649773 3.118236 2012 7.01678 3.972388 0.586587 0.761176 2.363161 2.663049 3.150665 2013 6.982181 4.019532 1.007748 0.845098 2.445056 2.675007 2.985993 2014 6.971327 4.074743 0.912222 0.876218 2.583132 2.571641 2.813371 Sumber : data diolah 68

Lampirn 3 Hasil Regresi Linier Berganda Dependent Variable: LOG_Y Date: 03/29/16 Time: 11:22 Variable Coefficient Std. Error tstatistic Prob. C 6.789744 1.826192 3.717980 0.0026 LOG_X1 0.290472 0.555409 0.522987 0.6098 LOG_X2 0.336042 0.132245 2.541065 0.0246 LOG_X3 1.393089 0.304050 4.581779 0.0005 LOG_X4_AS 0.176705 0.364914 0.484237 0.6363 LOG_X4_INDH 0.013624 0.073976 0.184167 0.8567 LOG_X4_JPG 0.098738 0.293833 0.336035 0.7422 Rsquared 0.793152 Mean dependent var 6.707914 Adjusted Rsquared 0.697683 S.D. dependent var 0.243776 S.E. of regression 0.134036 Akaike info criterion 0.912200 Sum squared resid 0.233553 Schwarz criterion 0.563694 Log likelihood 16.12200 HannanQuinn criter. 0.844168 Fstatistic 8.307990 DurbinWatson stat 1.363155 Prob(Fstatistic) 0.000766 69

LOG_X1 1 LOG_X2 LOG_X3 LOG_X4_AS LOG_X4_INDH LOG_X4_JPG Lampiran 4 Uji Multikolinieritas LOG_X1 LOG_X2 LOG_X3 LOG_X4_AS LOG_X4_INDH LOG_X4_JPG 0.01119489673 0.33529590490 0.94149890892 0.78421792628 0.93435794632 88842 60508 27854 18867 90792 0.01119489673 88842 1 0.33529590490 0.65621175247 60508 0.94149890892 27854 0.78421792628 18867 0.93435794632 90792 0.65621175247 72331 72331 1 0.10253733669 26729 0.06846994813 648065 0.09829535209 098124 0.42783293109 0.10253733669 26729 0.42783293109 87289 87289 1 0.36395391792 79159 0.06846994813 648065 0.36395391792 79159 0.71290779568 5014 0.71290779568 5014 1 0.09829535209 098124 0.36276715832 17215 0.99003655247 08315 0.70699188324 91924 0.36276715832 0.99003655247 0.70699188324 17215 08315 91924 1 Dependent Variable: LOG_Y Date: 03/29/16 Time: 11:44 Variable Coefficient Std. Error tstatistic Prob. C 4.655014 1.022297 4.553486 0.0002 LOG_X1 0.526818 0.262022 2.010590 0.0596 Rsquared 0.183395 Mean dependent var 6.707914 Adjusted Rsquared 0.138028 S.D. dependent var 0.243776 S.E. of regression 0.226327 Akaike info criterion 0.039031 Sum squared resid 0.922032 Schwarz criterion 0.060543 Log likelihood 2.390307 HannanQuinn criter. 0.019593 Fstatistic 4.042471 DurbinWatson stat 0.308367 Prob(Fstatistic) 0.059594 70

Dependent Variable: LOG_Y Date: 03/29/16 Time: 11:44 Variable Coefficient Std. Error tstatistic Prob. C 6.847635 0.145613 47.02633 0.0000 LOG_X2 0.157688 0.152434 1.034470 0.3146 Rsquared 0.056115 Mean dependent var 6.707914 Adjusted Rsquared 0.003677 S.D. dependent var 0.243776 S.E. of regression 0.243327 Akaike info criterion 0.105817 Sum squared resid 1.065743 Schwarz criterion 0.205391 Log likelihood 0.941827 HannanQuinn criter. 0.125255 Fstatistic 1.070128 DurbinWatson stat 0.251581 Prob(Fstatistic) 0.314616 Dependent Variable: LOG_Y Date: 03/29/16 Time: 11:45 Variable Coefficient Std. Error tstatistic Prob. C 7.686908 0.177485 43.31020 0.0000 LOG_X3 0.957454 0.170413 5.618435 0.0000 Rsquared 0.636854 Mean dependent var 6.707914 Adjusted Rsquared 0.616679 S.D. dependent var 0.243776 S.E. of regression 0.150929 Akaike info criterion 0.849381 Sum squared resid 0.410030 Schwarz criterion 0.749808 Log likelihood 10.49381 HannanQuinn criter. 0.829943 Fstatistic 31.56681 DurbinWatson stat 1.252018 Prob(Fstatistic) 0.000025 71

Dependent Variable: LOG_Y Date: 03/29/16 Time: 11:45 Variable Coefficient Std. Error tstatistic Prob. C 6.454830 0.125962 51.24415 0.0000 LOG_X4_AS 0.129449 0.059184 2.187236 0.0422 Rsquared 0.209972 Mean dependent var 6.707914 Adjusted Rsquared 0.166081 S.D. dependent var 0.243776 S.E. of regression 0.222614 Akaike info criterion 0.072118 Sum squared resid 0.892023 Schwarz criterion 0.027455 Log likelihood 2.721181 HannanQuinn criter. 0.052680 Fstatistic 4.784000 DurbinWatson stat 0.350294 Prob(Fstatistic) 0.042167 Dependent Variable: LOG_Y Date: 03/29/16 Time: 11:46 Variable Coefficient Std. Error tstatistic Prob. C 6.280757 0.209350 30.00122 0.0000 LOG_X4_INDH 0.154242 0.073390 2.101668 0.0499 Rsquared 0.197038 Mean dependent var 6.707914 Adjusted Rsquared 0.152429 S.D. dependent var 0.243776 S.E. of regression 0.224429 Akaike info criterion 0.055880 Sum squared resid 0.906627 Schwarz criterion 0.043694 Log likelihood 2.558796 HannanQuinn criter. 0.036442 Fstatistic 4.417010 DurbinWatson stat 0.347184 Prob(Fstatistic) 0.049927 72

Dependent Variable: LOG_Y Date: 03/29/16 Time: 11:46 Variable Coefficient Std. Error tstatistic Prob. C 6.486855 0.132328 49.02087 0.0000 LOG_X4_JPG 0.098906 0.054539 1.813484 0.0865 Rsquared 0.154482 Mean dependent var 6.707914 Adjusted Rsquared 0.107509 S.D. dependent var 0.243776 S.E. of regression 0.230299 Akaike info criterion 0.004237 Sum squared resid 0.954677 Schwarz criterion 0.095336 Log likelihood 2.042372 HannanQuinn criter. 0.015201 Fstatistic 3.288726 DurbinWatson stat 0.307526 Prob(Fstatistic) 0.086466 Lampiran 5 Uji heterokedastisitas Heteroskedasticity Test: White Fstatistic 0.471507 Prob. F(6,13) 0.8177 Obs*Rsquared 3.574493 Prob. ChiSquare(6) 0.7340 Scaled explained SS 1.303403 Prob. ChiSquare(6) 0.9715 Test Equation: Dependent Variable: RESID^2 Date: 03/29/16 Time: 11:40 Variable Coefficient Std. Error tstatistic Prob. C 0.023337 0.098054 0.238000 0.8156 LOG_X1^2 0.001611 0.008905 0.180866 0.8593 LOG_X2^2 0.000556 0.010568 0.052591 0.9589 LOG_X3^2 0.001109 0.023595 0.047008 0.9632 LOG_X4_AS^2 0.006010 0.010315 0.582636 0.5701 LOG_X4_INDH^2 0.000776 0.003227 0.240366 0.8138 LOG_X4_JPG^2 0.008171 0.007398 1.104540 0.2894 Rsquared 0.178725 Mean dependent var 0.011678 Adjusted Rsquared 0.200326 S.D. dependent var 0.015741 S.E. of regression 0.017246 Akaike info criterion 5.013306 Sum squared resid 0.003866 Schwarz criterion 4.664799 Log likelihood 57.13306 HannanQuinn criter. 4.945274 Fstatistic 0.471507 DurbinWatson stat 2.824780 Prob(Fstatistic) 0.817655 73

Lampiran 6 Uji Normalitas 6 5 4 3 2 1 Series: Residuals Sample 1995 2014 Observations 20 Mean 1.13e15 Median 2.24e05 Maximum 0.183535 Minimum 0.246280 Std. Dev. 0.110871 Skewness 0.278753 Kurtosis 2.726107 JarqueBera 0.321525 Probability 0.851494 0 0.25 0.20 0.15 0.10 0.05 0.00 0.05 0.10 0.15 0.20 74

Lampiran 7 Uji Autokorelasi BreuschGodfrey Serial Correlation LM Test: Fstatistic 1.259889 Prob. F(2,11) 0.3216 Obs*Rsquared 3.727544 Prob. ChiSquare(2) 0.1551 Test Equation: Dependent Variable: RESID Date: 03/29/16 Time: 11:53 Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error tstatistic Prob. C 0.097924 1.992802 0.049139 0.9617 LOG_X1 0.015306 0.610231 0.025082 0.9804 LOG_X2 0.031174 0.131160 0.237676 0.8165 LOG_X3 0.105523 0.308920 0.341586 0.7391 LOG_X4_AS 0.212387 0.389533 0.545235 0.5965 LOG_X4_INDH 0.001090 0.074022 0.014721 0.9885 LOG_X4_JPG 0.174064 0.308725 0.563818 0.5842 RESID(1) 0.534549 0.345322 1.547975 0.1499 RESID(2) 0.271847 0.326810 0.831818 0.4232 Rsquared 0.186377 Mean dependent var 1.13E15 Adjusted Rsquared 0.405348 S.D. dependent var 0.110871 S.E. of regression 0.131434 Akaike info criterion 0.918459 Sum squared resid 0.190024 Schwarz criterion 0.470379 Log likelihood 18.18459 HannanQuinn criter. 0.830989 Fstatistic 0.314972 DurbinWatson stat 2.058818 Prob(Fstatistic) 0.944016 75

Lampiran 8 Uji Linieritas Ramsey RESET Test Equation: UNTITLED Specification: LOG_Y C LOG_X1 LOG_X2 LOG_X3 LOG_X4_AS LOG_X4_INDH LOG_X4_JPG Omitted Variables: Squares of fitted values Value df Probability tstatistic 0.490440 12 0.6327 Fstatistic 0.240532 (1, 12) 0.6327 Likelihood ratio 0.396921 1 0.5287 Ftest summary: Sum of Sq. df Mean Squares Test SSR 0.004589 1 0.004589 Restricted SSR 0.233553 13 0.017966 Unrestricted SSR 0.228964 12 0.019080 Unrestricted SSR 0.228964 12 0.019080 LR test summary: Value df Restricted LogL 16.12200 13 Unrestricted LogL 16.32046 12 Unrestricted Test Equation: Dependent Variable: LOG_Y Date: 03/29/16 Time: 11:54 Variable Coefficient Std. Error tstatistic Prob. C 11.74942 37.84789 0.310438 0.7616 LOG_X1 1.251166 3.195063 0.391594 0.7022 LOG_X2 1.479302 3.703967 0.399383 0.6966 LOG_X3 6.091590 15.26436 0.399073 0.6969 LOG_X4_AS 0.819971 2.066709 0.396752 0.6985 LOG_X4_INDH 0.056185 0.161470 0.347960 0.7339 LOG_X4_JPG 0.479081 1.216457 0.393833 0.7006 FITTED^2 0.400486 0.816584 0.490440 0.6327 Rsquared 0.797216 Mean dependent var 6.707914 Adjusted Rsquared 0.678926 S.D. dependent var 0.243776 S.E. of regression 0.138132 Akaike info criterion 0.832046 Sum squared resid 0.228964 Schwarz criterion 0.433753 Log likelihood 16.32046 HannanQuinn criter. 0.754295 Fstatistic 6.739475 DurbinWatson stat 1.467466 Prob(Fstatistic) 0.002155 76