While univariate comparisons show significant differences be-tween Isl terjemahan - While univariate comparisons show significant differences be-tween Isl Bahasa Indonesia Bagaimana mengatakan

While univariate comparisons show s

While univariate comparisons show significant differences be-
tween Islamic and conventional banks, these differences could be
driven by other bank or country characteristics. To assess differ-
ences in business model, efficiency, asset quality, and stability
across different bank types, we therefore run the following
regression:
Bank
i
;
j
;
t
¼
a
þ
b
B
i
;
j
;
t
þ
c
C
j

Y
t
þ
d
I
i
þ
e
i
;
t
ð
1
Þ
where Bank is one of our measures of business orientation, effi-
ciency, asset quality, and stability of bank i in country j in year t,
B is a vector of time-varying bank characteristics, C

j
Y
t
are coun-
try-year-fixed effects, I is a dummy taking the value one for Islamic
banks and
e
is a white-noise error term. We thus compare Islamic
and conventional banks within a country and a specific year. Below
we also use additional specifications, including interacting the Isla-
mic bank dummy with size dummies and with the market share of
Islamic banks. We allow for clustering of the error terms on the
bank level, i.e. correlation among the error terms across years with-
in banks. We prefer to cluster on the bank- rather than country-le-
vel, as some of the countries in our sample host significantly more
banks than others and we have only 22 countries. Simulations have
shown that standard errors can be biased downwards in these two
cases (
Nichols and Schaffer, 2007
).
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Hasil (Bahasa Indonesia) 1: [Salinan]
Disalin!
While univariate comparisons show significant differences be-tween Islamic and conventional banks, these differences could bedriven by other bank or country characteristics. To assess differ-ences in business model, efficiency, asset quality, and stabilityacross different bank types, we therefore run the followingregression:Banki;j;t¼aþbBi;j;tþcCjYtþdIiþei;tð1Þwhere Bank is one of our measures of business orientation, effi-ciency, asset quality, and stability of bank i in country j in year t,B is a vector of time-varying bank characteristics, CjYtare coun-try-year-fixed effects, I is a dummy taking the value one for Islamicbanks andeis a white-noise error term. We thus compare Islamicand conventional banks within a country and a specific year. Belowwe also use additional specifications, including interacting the Isla-mic bank dummy with size dummies and with the market share ofIslamic banks. We allow for clustering of the error terms on thebank level, i.e. correlation among the error terms across years with-in banks. We prefer to cluster on the bank- rather than country-le-vel, as some of the countries in our sample host significantly morebanks than others and we have only 22 countries. Simulations haveshown that standard errors can be biased downwards in these twocases (Nichols and Schaffer, 2007).
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Hasil (Bahasa Indonesia) 2:[Salinan]
Disalin!
Sementara perbandingan univariat menunjukkan perbedaan yang signifikan menjadi-
bank syariah dan konvensional tween, perbedaan-perbedaan ini dapat
didorong oleh bank atau negara karakteristik lainnya. Untuk menilai berbeda-
ences dalam model bisnis, efisiensi, kualitas aset, dan stabilitas
di seluruh jenis bank yang berbeda, karena itu kita jalankan berikut
regresi:
Bank
i
;
j
;
t
¼
a
þ
b
B
i
;
j
;
t
þ
c
C
j
?
Y
t
þ
d
I
i
þ
e
i
;
t
ð
1
Þ
mana Bank adalah salah satu langkah kami orientasi bisnis, terdistribusikan
efisiensi, kualitas aset, dan stabilitas bank i di negara j pada tahun t,
B adalah vektor dari waktu- berbagai karakteristik bank, C
?
j
Y
t
adalah negara-
mencoba-tahun tetap efek, saya adalah dummy mengambil nilai satu untuk Islam
bank dan
e
adalah istilah error putih-noise. Dengan demikian kita membandingkan Islam
bank dan konvensional dalam suatu negara dan tahun tertentu. Di bawah ini
kami juga menggunakan spesifikasi tambahan, termasuk berinteraksi dengan Isla-
mic boneka Bank dengan dummies ukuran dan dengan pangsa pasar
bank syariah. Kami memungkinkan untuk clustering dari istilah kesalahan pada
tingkat Bank, yaitu korelasi antara istilah kesalahan di tahun dengan-
di bank. Kami lebih memilih untuk cluster di bank- daripada negara-le-
vel, seperti beberapa negara di tuan rumah sampel kami secara signifikan lebih
bank daripada yang lain dan kami hanya memiliki 22 negara. Simulasi telah
menunjukkan bahwa kesalahan standar dapat menjadi bias ke bawah dalam kedua
kasus (
Nichols dan Schaffer, 2007
).
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