V. KESIMPULAN DAN SARAN
5.2 Saran
Pentingnya pengetahuan tentang faktor-faktor yang memengaruhi impor adalah penting bagi suatu negara, karena impor merupakan salah satu komponen yang terdapat dalam neraca pembayaran dan memiliki peran penting dalam perekonomian. Dilihat dari hasil penelitian ini, untuk kawasan ASEAN+6, volatilitas nilai tukar memiliki pengaruh yang paling besar terhadap impor. Oleh karena itu, jika pemerintah berkeinginan untuk memperlancar permintaan impor, maka volatilitas nilai tukar riil harus diturunkan.
Dalam penelitian selanjutnya, disarankan untuk menggunakan data kuartalan sehingga bisa lebih terlihat pergerakannya. Selain itu, dapat juga disetakan variabel-variabel lainnya yang dapat memengaruhi impor dan dapat juga mengikutsertakan negara-negara ASEAN lainnya sehingga dapat diperoleh hasil yang lebih informati.
DAFTAR PUSTAKA
Akpokodge, G. dan Omojimite, B. U. 2009. The Effect of Exchange Rate
Volatility on the Imports of ECOWAS Countries. Medwell Journal:
340-346.
Alam, S. dan Ahmed, Q. M. 2010. Exchange Rate Volatility and Pakistan’s
Import Demand: An Application of Autoregressive Distributed Lag Model. EuroJournals Publishing.
Alam, S. dan Ahmed, Q. M. 2011. Exchange Rate Volatility and Pakistan’s
Bilateral Imports from Major Sources: An Application of ARDL Approach. Canadian Center of Science and Education, Vol.3, No.2, hal.
245-254.
Arize, A.C. 1998. The Effect of Exchange Rate Volatility on U.S. Imports: An
Empirical Investigation. International Economic Journal, Vol.12, No.3,
hal.31-40
Arize, A. C. dan Shwiff, S. S. 1998. Does Exchange Rate Volatility Affect Import
Flows in G-7 Countries? Evidence from Cointegration Models.
Routledge: 1269-1276.
Baltagi, B. H. 2005. Econometric Analysis of Panel Data. Third Edition. British
Library Cataloguing in Publication Data.
Cheong, Chongcheul. 2004. Does the Risk of Exchange Rate Fluctuation Really
Affect International Trade Flows Between Counties?. Economic Bulletin,
Vol.6 No.4, hal.1-8.
Choudhry, T. 2008. Exchange Rate Volatility and United Kingdom Trade:
Evidence from Canada, Japan, and New Zealand. Springer. Hal.607-619.
Delong, J. Bradford. 2002. Macroeconomics. New York: McGraw-Hill Higher Education.
Fauzi, A. J. F. A. 2007. Analisis Komparatif Keterkaitan Inflasi dengan Nilai
Tukar Riil di Kawasan Asia (ASEAN+3) dan Non Asia (Uni Eropa, Amerika Utara) [skripsi]. Bogor: Institut Pertanian Bogor.
Firdaus, M. 2011. Aplikasi Ekonometrika untuk Data Panel dan Time Series. Bogor: IPB Press.
Hadiwinata, B. S. 2002. Politik Bisnis Internasional. Yogyakarta: Kanisius.
Hossain, A. dan Chowdhury, A. 1998. Open-Economy Macroeconomics for
Developing Countries. UK: Edwar Elgar
Hubbard, R. dan O’Brien, A. 2009. Macroeconomic Third Edition. United States: Pearson Education.
Indra. 2009. Analisis Hubungan Intensitas Energi dan Pendapatan Perkapita:
Studi Komparatif di Sepuluh Negara Asia Pasifik [Tesis]. Bogor: Institut
Pertanian Bogor.
Kayis, A. dan Ozturk, E. 2005. The Effect of Exchange Rate Volatility on The
Bilateral Trade Flows. Sosyal Bilimier Dergisi. Suleyman Demirel
University, Vol.1, hal.147-155.
Koray, F. dan Lastrapes, D. 1989. Real Exchange Rate Volatility and U.S.
Bilateral Trade: a VAR Approach. The Review of Economics and
Statistics.
Mankiw, N. G. 2003. Teori Makroekonomi. Edisi Kelima. Erlangga, Jakarta. Mishkin, Frederic S. 2009. The Economics of Money, Banking, and Financial
Market. Lana S. dan Beta Y. G. [Penerjemah]. Jakarta: Salemba Empat.
Oktaviani, R. dan Novianti, T. 2009. Teori Perdagangan Internasional dan
Aplikasinya di Indonesia. Bogor: Institut Pertanian Bogor.
Sukar, A. H. Unanticipated Exchange Rate Risk and U.S. Imports. Journal of Applied Business Research, Vol.10, No.4, hal.19-23
Verbeek, Marno. 2004. A guide to modern econometrics. 2nd Edition. Chichester: john wiley & sons. Ltd.
Lampiran 1. Granger Causality Test Kasus seluruh kawasan
Pairwise Granger Causality Tests Date: 05/19/00 Time: 21:09 Sample: 2002 2010
Lags: 1
Null Hypothesis: Obs F-Statistic Prob. Y does not Granger Cause M 136 0.74160 0.3907 M does not Granger Cause Y 6.30139 0.0133 RER does not Granger Cause M 136 1.50118 0.2227 M does not Granger Cause RER 0.28579 0.5938 V does not Granger Cause M 136 5.65271 0.0189 M does not Granger Cause V 0.73061 0.3942 Pairwise Granger Causality Tests
Date: 05/19/00 Time: 21:10 Sample: 2002 2010
Lags: 2
Null Hypothesis: Obs F-Statistic Prob. Y does not Granger Cause M 119 3.34370 0.0388 M does not Granger Cause Y 0.92104 0.4010 RER does not Granger Cause M 119 0.88157 0.4169 M does not Granger Cause RER 3.43862 0.0355 V does not Granger Cause M 119 2.72687 0.0697 M does not Granger Cause V 0.13170 0.8767 Pairwise Granger Causality Tests
Date: 05/19/00 Time: 21:10 Sample: 2002 2010
Lags: 3
Null Hypothesis: Obs F-Statistic Prob. Y does not Granger Cause M 102 1.25940 0.2928 M does not Granger Cause Y 1.86532 0.1407 RER does not Granger Cause M 102 0.75415 0.5226 M does not Granger Cause RER 3.13704 0.0290 V does not Granger Cause M 102 1.55055 0.2066 M does not Granger Cause V 0.18506 0.9063
Kasus kawasan ASEAN+6
Pairwise Granger Causality Tests Date: 04/25/00 Time: 09:47 Sample: 2002 2010
Lags: 1
Null Hypothesis: Obs F-Statistic Prob. Y does not Granger Cause M 88 0.17663 0.6753 M does not Granger Cause Y 5.13037 0.0261 RER does not Granger Cause M 88 0.00919 0.9239 M does not Granger Cause RER 0.20406 0.6526 V does not Granger Cause M 88 11.8942 0.0009 M does not Granger Cause V 8.4E-06 0.9977 Pairwise Granger Causality Tests
Date: 04/25/00 Time: 09:47 Sample: 2002 2010
Lags: 2
Null Hypothesis: Obs F-Statistic Prob. Y does not Granger Cause M 77 2.46155 0.0925 M does not Granger Cause Y 0.33881 0.7138 RER does not Granger Cause M 77 0.32370 0.7245 M does not Granger Cause RER 3.99654 0.0226 V does not Granger Cause M 77 11.2951 5.E-05 M does not Granger Cause V 0.58758 0.5583 Pairwise Granger Causality Tests
Date: 04/25/00 Time: 09:47 Sample: 2002 2010
Lags: 3
Null Hypothesis: Obs F-Statistic Prob. Y does not Granger Cause M 66 1.04209 0.3807 M does not Granger Cause Y 0.92823 0.4328 RER does not Granger Cause M 66 0.28686 0.8347 M does not Granger Cause RER 3.20631 0.0295 V does not Granger Cause M 66 5.24012 0.0028 M does not Granger Cause V 0.27149 0.8457
Kasus kawasan non ASEAN+6
Pairwise Granger Causality Tests Date: 05/19/00 Time: 21:15 Sample: 2002 2010
Lags: 1
Null Hypothesis: Obs F-Statistic Prob. Y does not Granger Cause M 48 0.72491 0.3990 M does not Granger Cause Y 17.7945 0.0001 RER does not Granger Cause M 48 0.68066 0.4137 M does not Granger Cause RER 10.0044 0.0028 V does not Granger Cause M 48 5.73951 0.0208 M does not Granger Cause V 0.42230 0.5191 Pairwise Granger Causality Tests
Date: 05/19/00 Time: 21:16 Sample: 2002 2010
Lags: 2
Null Hypothesis: Obs F-Statistic Prob. Y does not Granger Cause M 42 1.03213 0.3663 M does not Granger Cause Y 7.98219 0.0013 RER does not Granger Cause M 42 0.42179 0.6590 M does not Granger Cause RER 2.68706 0.0814 V does not Granger Cause M 42 3.04405 0.0597 M does not Granger Cause V 0.02395 0.9763 Pairwise Granger Causality Tests
Date: 05/19/00 Time: 21:16 Sample: 2002 2010
Lags: 3
Null Hypothesis: Obs F-Statistic Prob. Y does not Granger Cause M 36 1.38250 0.2678 M does not Granger Cause Y 6.23491 0.0021 RER does not Granger Cause M 36 0.74743 0.5327 M does not Granger Cause RER 2.57098 0.0734 V does not Granger Cause M 36 2.46538 0.0822 M does not Granger Cause V 0.45209 0.7178
Lampiran 2. Hasil Estimasi Faktor-Faktor yang Memengaruhi Impor Seluruh Kawasan
System-Generalized Method of Moments (SYS-GMM)
STATA Licensed to: STATAforAll Serial number: 93611859953
Single-user Stata network perpetual license:
979-696-4601 (fax)
979-696-4600 stata@stata.com 800-STATA-PC http://www.stata.com Special Edition College Station, Texas 77845 USA
4905 Lakeway Drive Statistics/Data Analysis StataCorp
___/ / /___/ / /___/ 12.0 Copyright 1985-2011 StataCorp LP /__ / ____/ / ____/
___ ____ ____ ____ ____ (R)
Standard: _cons GMM-type: LD.m
Instruments for level equation Standard: D.y D.rer D.v GMM-type: L(2/.).m
Instruments for differenced equation errors are recommended.
Warning: gmm two-step standard errors are biased; robust standard
_cons -3.875705 1.448465 -2.68 0.007 -6.714644 -1.036765 v .3061796 .051534 5.94 0.000 .2051748 .4071844 rer .0149525 .0618318 0.24 0.809 -.1062356 .1361406 y .1886631 .0551865 3.42 0.001 .0804996 .2968265 L1. .6539702 .0393351 16.63 0.000 .5768749 .7310656 m m Coef. Std. Err. z P>|z| [95% Conf. Interval] Two-step results
Prob > chi2 = 0.0000 Number of instruments = 39 Wald chi2(4) = 2808.78 max = 8 avg = 8 Obs per group: min = 8 Time variable: tahun
Group variable: country Number of groups = 17 System dynamic panel-data estimation Number of obs = 136 . xtdpdsys m y rer v, twostep
Hasil Estimasi dengan Pooled Least Square (PLS) H0: no autocorrelation 2 .35796 0.7204 1 -3.0251 0.0025 Order z Prob > z
Arellano-Bond test for zero autocorrelation in first-differenced errors . estat abond
Prob > chi2 = 0.9944 chi2(34) = 16.69518
H0: overidentifying restrictions are valid Sargan test of overidentifying restrictions
. estat sargan _cons .1624534 .247209 0.66 0.512 -.326585 .6514917 v .0414818 .0853006 0.49 0.628 -.1272631 .2102267 rer .0045018 .0073013 0.62 0.539 -.009942 .0189455 y -.0003463 .0071257 -0.05 0.961 -.0144426 .0137499 L1. .9791112 .0332956 29.41 0.000 .9132446 1.044978 m m Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 12.5804165 135 .09318827 Root MSE = .11156 Adj R-squared = 0.8665 Residual 1.63026659 131 .012444783 R-squared = 0.8704 Model 10.9501499 4 2.73753748 Prob > F = 0.0000 F( 4, 131) = 219.97 Source SS df MS Number of obs = 136 . reg m l.m y rer v
Hasil Estimasi Fixed Effect (FE)
F test that all u_i=0: F(16, 115) = 5.62 Prob > F = 0.0000 rho .99109673 (fraction of variance due to u_i)
sigma_e .08919684 sigma_u .94109314 _cons -9.228243 3.452422 -2.67 0.009 -16.06683 -2.389659 v .0703742 .0812078 0.87 0.388 -.0904828 .2312312 rer -.0707861 .122993 -0.58 0.566 -.3144116 .1728394 y .3930829 .1176848 3.34 0.001 .159972 .6261938 L1. .5501853 .0616602 8.92 0.000 .4280484 .6723222 m m Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.9512 Prob > F = 0.0000 F(4,115) = 100.03 overall = 0.0693 max = 8 between = 0.0505 avg = 8.0 R-sq: within = 0.7767 Obs per group: min = 8 Group variable: country Number of groups = 17 Fixed-effects (within) regression Number of obs = 136 . xtreg m l.m y rer v,fe
Lampiran 3. Hasil Estimasi Faktor-Faktor yang Memengaruhi Impor Kawasan ASEAN+6
System-Generalized Method of Moments (SYS-GMM)
STATA Licensed to: STATAforAll Serial number: 93611859953
Single-user Stata network perpetual license:
979-696-4601 (fax)
979-696-4600 stata@stata.com 800-STATA-PC http://www.stata.com Special Edition College Station, Texas 77845 USA
4905 Lakeway Drive Statistics/Data Analysis StataCorp
___/ / /___/ / /___/ 12.0 Copyright 1985-2011 StataCorp LP /__ / ____/ / ____/
___ ____ ____ ____ ____ (R)
Standard: _cons GMM-type: LD.m D.v Instruments for level equation Standard: D.y D.rer
GMM-type: L(2/2).m L(1/.).v Instruments for differenced equation errors are recommended.
Warning: gmm two-step standard errors are biased; robust standard
_cons -1.950712 .8199086 -2.38 0.017 -3.557703 -.3437204 rer -.15887 .0640769 -2.48 0.013 -.2844584 -.0332816 y .1295035 .0335794 3.86 0.000 .063689 .1953179 v -6.139596 2.534945 -2.42 0.015 -11.108 -1.171195 L1. .7359815 .0506174 14.54 0.000 .6367733 .8351897 m m Coef. Std. Err. z P>|z| [95% Conf. Interval] Two-step results
Prob > chi2 = 0.0000 Number of instruments = 60 Wald chi2(4) = 1841.16 max = 8 avg = 8 Obs per group: min = 8 Time variable: tahun
Group variable: country Number of groups = 11 System dynamic panel-data estimation Number of obs = 88 . xtdpdsys m y rer, twostep pre(v) maxldep(1)
Hasil Estimasi dengan Pooled Least Square (PLS) H0: no autocorrelation 2 .1566 0.8756 1 -2.5273 0.0115 Order z Prob > z
Arellano-Bond test for zero autocorrelation in first-differenced errors . estat abond
Prob > chi2 = 1.0000 chi2(55) = 9.900828
H0: overidentifying restrictions are valid Sargan test of overidentifying restrictions
. estat sargan _cons -.1018989 .2931043 -0.35 0.729 -.6848715 .4810737 v -7.541707 3.872315 -1.95 0.055 -15.24359 .1601716 rer -.0182242 .0134872 -1.35 0.180 -.0450497 .0086013 y .0163584 .0111843 1.46 0.147 -.0058867 .0386035 L1. .9561433 .0419971 22.77 0.000 .8726128 1.039674 m m Coef. Std. Err. t P>|t| [95% Conf. Interval] Total 10.2410218 87 .117712895 Root MSE = .11846 Adj R-squared = 0.8808 Residual 1.16467104 83 .014032181 R-squared = 0.8863 Model 9.07635079 4 2.2690877 Prob > F = 0.0000 F( 4, 83) = 161.71 Source SS df MS Number of obs = 88 . reg m l.m y rer v
Hasil Estimasi Fixed Effect (FE)
F test that all u_i=0: F(10, 73) = 4.65 Prob > F = 0.0000 rho .99188097 (fraction of variance due to u_i)
sigma_e .09873516 sigma_u 1.0913135 _cons -8.080665 4.474307 -1.81 0.075 -16.99795 .8366168 v .6065125 5.265066 0.12 0.909 -9.886749 11.09977 rer -.0257372 .1563236 -0.16 0.870 -.3372898 .2858154 y .339838 .1454063 2.34 0.022 .0500437 .6296323 L1. .5967571 .0782243 7.63 0.000 .4408564 .7526578 m m Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.9514 Prob > F = 0.0000 F(4,73) = 73.96 overall = 0.0482 max = 8 between = 0.0265 avg = 8.0 R-sq: within = 0.8021 Obs per group: min = 8 Group variable: country Number of groups = 11 Fixed-effects (within) regression Number of obs = 88 . xtreg m l.m y rer v, fe
Lampiran 4. Hasil Estimasi Faktor-Faktor yang Memengaruhi Impor Kawasan Non ASEAN+6
Arellano Bond-Generalized Method of Moments (AB-GMM)
STATA Licensed to: STATAforAll Serial number: 93611859953
Single-user Stata network perpetual license:
979-696-4601 (fax)
979-696-4600 stata@stata.com 800-STATA-PC http://www.stata.com Special Edition College Station, Texas 77845 USA
4905 Lakeway Drive Statistics/Data Analysis StataCorp
___/ / /___/ / /___/ 12.0 Copyright 1985-2011 StataCorp LP /__ / ____/ / ____/
___ ____ ____ ____ ____ (R)
Standard: _cons
Instruments for level equation Standard: D.y D.rer
GMM-type: L(2/.).m L(1/.).v Instruments for differenced equation errors are recommended.
Warning: gmm two-step standard errors are biased; robust standard
_cons -58.70728 24.35128 -2.41 0.016 -106.4349 -10.97966 rer -.1001764 .4923335 -0.20 0.839 -1.065132 .8647795 y 2.24386 .8788666 2.55 0.011 .5213132 3.966407 v -.1758792 .1551878 -1.13 0.257 -.4800417 .1282833 L1. -.1584503 .2083035 -0.76 0.447 -.5667178 .2498171 m m Coef. Std. Err. z P>|z| [95% Conf. Interval] Two-step results
Prob > chi2 = 0.0000 Number of instruments = 41 Wald chi2(4) = 112.15 max = 7 avg = 7 Obs per group: min = 7 Time variable: tahun
Group variable: country Number of groups = 6 Arellano-Bond dynamic panel-data estimation Number of obs = 42 . xtabond m y rer, twostep pre(v)
Hasil Estimasi dengan Pooled Least Square (PLS) H0: no autocorrelation 2 1.2004 0.2300 1 -2.1024 0.0355 Order z Prob > z
Arellano-Bond test for zero autocorrelation in first-differenced errors . estat abond
Prob > chi2 = 1.0000 chi2(36) = 4.141134
H0: overidentifying restrictions are valid Sargan test of overidentifying restrictions
. estat sargan _cons 1.496718 .6539318 2.29 0.027 .1779385 2.815497 v .0494105 .0649241 0.76 0.451 -.0815214 .1803424 rer -.0110209 .0155544 -0.71 0.482 -.0423893 .0203475 y -.0005977 .0164093 -0.04 0.971 -.0336902 .0324949 L1. .6998877 .0938049 7.46 0.000 .5107121 .8890634 m m Coef. Std. Err. t P>|t| [95% Conf. Interval] Total .746224692 47 .015877121 Root MSE = .08202 Adj R-squared = 0.5763 Residual .289256873 43 .006726904 R-squared = 0.6124 Model .45696782 4 .114241955 Prob > F = 0.0000 F( 4, 43) = 16.98 Source SS df MS Number of obs = 48 . reg m l.m y rer v
Hasil Estimasi Fixed Effect (FE)
F test that all u_i=0: F(5, 38) = 8.94 Prob > F = 0.0000 rho .99650489 (fraction of variance due to u_i)
sigma_e .05913887 sigma_u .99857837 _cons -29.99071 6.974515 -4.30 0.000 -44.10988 -15.87154 v -.0509285 .0615726 -0.83 0.413 -.1755758 .0737187 rer -.5107995 .1809218 -2.82 0.008 -.8770565 -.1445424 y 1.195524 .2542529 4.70 0.000 .6808157 1.710232 L1. .1210597 .1121101 1.08 0.287 -.1058953 .3480146 m m Coef. Std. Err. t P>|t| [95% Conf. Interval] corr(u_i, Xb) = -0.9923 Prob > F = 0.0000 F(4,38) = 26.42 overall = 0.0030 max = 8 between = 0.0472 avg = 8.0 R-sq: within = 0.7355 Obs per group: min = 8 Group variable: country Number of groups = 6 Fixed-effects (within) regression Number of obs = 48 . xtreg m l.m y rer v, fe