CREDIT RISK MODELS
FOR FIVE MAJOR SECTORS IN INDONESIA
Ndari Surjaningsih, Ina Nurmalia Kurniati1, Reni Indriani2
1 Senior researcher and researcher in Macroprudential Policy Department BI, email:
[email protected] and [email protected]
2 Consultant in Macroprudential Policy Department.
This paper analyze Nonperforming Loan ratio to total credit (NPL), as a proxy for
ȱǰȱȱęȱȱȱȱ¢ȱ£ȱȱȱȱŗŗŝȱȱ
ȱȱȱȱȱŘŖŖŖŗȱȱŘŖŗŜřǯȱȱȱ¢ȱ ȱȱ
ȱȱ ȱȱȱȱȱȱȱ¢ȱȱ ȱȱȱ
ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ǯȱ
¢ȱȱĚȱěȱȱȱȱ¢ȱȱȱǰȱ
ȱȱȱĚȱĚȱȱȱȱ¢ǰȱǰȱ
ȱȱǯȱȱǰȱȱȱ¢ȱȱ ȱȱȱȱ
ȱ¢ǰȱǰȱȱȱǰȱ ȱȱ¡ȱȱ¢ȱěȱ
ȱȱȱȱǯȱȱȱ ȱȱȱȱȱ¢ȱȱ
ȱȱȱȱȱȱȱȱ ȱȱȱȱȱ
¢ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ
¢ȱȱȱȱȱȱ ȱȱȱȱȱ¢ȱ and trade sectors. Thus, during economic contraction phase, NPL in commodity and
ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ sectors.
Keywords: Banking, Panel Data Models.
ȱęȱDZȱ Řŗǰȱřřȱ
ABSTRACT
Bulletin of Monetary Economics and Banking, Volume 20, Number 4, April 2018 ŚŗŖ
I. INTRODUCTION
ȱ ȱ ęȱ ȱ ȱ ŘŖŖŞȱ ȱ ȱ ȱ ȱ ȱ ęȱ ¢ȱ
ȱȱ ȱǯȱȱȱ¢ȱȱȱȱȱęȱ
¢ȱ¢ǰȱȱȱȱȱȱȱȱȱȱȱȱȱ
¢ǰȱ ¢ȱ ȱ ǯȱ ȱ ǰȱ ȱ ȱ ȱ ęȱ ȱ ȱ
ȱȱ¢ǰȱ¢ȱȱȱȱȱ ȱȱȱ
ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱȱȱȱȱȱǯȱ
There are at least three approaches to assess credit risk in the banking
¢ǯȱȱęȱȱȱȱȱȱȱȱǻǼȱȱ
ȱȱȱǯȱȱȱȱǰȱȱȱȱ
ȱȱ¢ȱȱǰȱǯȱȱȱ¡ȱȱȱ loan into nonperforming loan for each bank. The third approach is to model bank’s
ȱ ȱȱȂȱǯȱ
From those approaches, the nonperforming loan model is the most commonly used to determine the impact of macroeconomic conditions on banking risk.
ȱȱȱȱ ȱǰȱȱęȱȱȱȱĚ¡ȱȱȱ
ȱȱȱȱȱȱȱȱǻȱǼȱȱ ȱȱȱȱȱȱ
ȱȮȱ¢ȱ¢ȱȱȱȱȱȱǯȱ
Based on that background, this study aims to analyze the determinant of NPL
ȱȱȱȱ¢ȱ ȱȱȱȱęȱȱǰȱ¢ȱ commodity sector, trade sector, manufacturing industry sector, construction sector,
ȱǯȱȱ¢ȱ ȱȱȱ¢ȱȱȱȱȱ ŗŗŝȱȱȱȱǰȱȱȱȱȱȱȱ
ȱŘŖŖŖŗȱȱŘŖŗŜřǯ
ȱ¢ȱěȱȱȱȱȱȱǯȱȱȱȱȱ data of all commercial banks in Indonesia, this study aims to examine the impact
ȱȱȱȱȱȱȱěȱȱǯȱ The aggregate credit risk model (total credit) has some limitations as it could lead
ȱȱȱȱȱȱȱȱęȱǯȱȱȱ
ȱȱȱȱȱȱȱȱȱȱȱȱǯ
ȱ¢ȱ ȱȱȱȱǰȱȱȱȱȱęȱǰȱȱĚȱ
¢ȱȱȱ ȱ ȱȱȱ¢ȱȱǯȱȱȱȱ
ȱ ȱȱȱȱȱĚȱȱȱȱȱȱȱǰȱ
¡ȱȱȱȱǯȱȱȱȱ¢ȱȱȱěȱȱȱȱ
ȱ¢ǰȱȱȱȱǰȱȱ ȱȱȱȱǯȱȱȱ
¡ȱȱ¢ȱȱȱȱȱȱȱȱȱǯȱȱĚǰȱȱ
ȱ¢ȱǰȱ¢ȱěȱȱȱȱȱȱȱȱȱ
ȱǰȱ ȱȱȱĚȱ¢ȱěȱȱȱ
ȱȱǰȱ¢ȱȱȱǰȱǰȱȱǯ
ȱ ¢ȱ ȱ ȱ ȱ ęȱ ǯȱ ȱ ȱ ȱ ȱ ȱ
ǰȱ ȱ ȱ ȱ ȱ ¢£ȱ ȱ ȱ ȱ ȱ
ȱȱȱȱȱȱȱǯȱȱȱȱȱ¢ȱ
ȱȱȱȱȱȱȱȱǯȱȱȱȱȱȱ
ȱ ȱ ȱ ¢ǰȱ ȱ ȱ ȱ ȱ ¡ȱ ȱ ȱ ȱ suggestions for further research.
Credit Risk Models for Five Major Sectors in Indonesia 411
II. THEORY
The credit risk is seen as one of the most essential and enormous risks in the
ȱ¢ǯȱȱȱ¢ȱěȱȱ¢ȱȱȱǻ¢Ǽȱȱ
ȱǰȱ ȱȱȱȱȱǯȱǰȱȱȱȱȱȱȱ
ȱęȱǰȱ¢ȱȱȱȱȱȱ¢ȱȱȱęȱ¢ǯ
Řǯŗǯȱ ȱȱȱȱ
ȱȱ ȱ ȱȱȱȱȱȱȱȱȱȱȱŘŖŗŜǯȱ
ȱ ȱ ȱ ȱ ŝǯŞŜƖȱ ȱ ǻ¢¢Ǽȱ ȱ ȱ ȱ ęȱ ȱ
ȱ ŘŖŗŜǰȱ ȱ ȱ ŞǯŞşƖȱ ǻ¢¢Ǽǯȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱȱ ȱȱȱȱȱȱȱȱȱȱ
ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ consolidation their nonperforming loans and took a more prudent approach in
ȱ ȱȱǻȱǰȱŘŖŗŝǼǯ
40.00 30.00 20.00 10.00 0.00 -10.00 -20.00
2013 - Sem I 2013 - Sem II 2014 - Sem I 2014 - Sem II 2015 - Sem I 2015 - Sem II 2016 - Sem I 2016 - Sem II Credit Growth (yoy) IDR Credit Growth (yoy) FX Credit Growth (yoy)
9.15%
7.86%
0.92%
ȱȱǰȱȱȱ ȱȱȱęȱȱȱ
ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ŘŖŖŖŗȱ ȱ ŘŖŗŜřǯȱ
¢ȱȱȱȱȱȱȱ¢ȱ ȱȱ
¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱ ǻ ȱ ŘǼǯȱ ȱ ȱ ŘŖŗŜřǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱ ȱ ŚŘƖȱ ǻȱ ŗǯŝȱ Ǽǰȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱȱ¢ȱśƖȱǻȱŖǯŘŗȱǼǯ
ȱŗǯȱȱȱ
Bulletin of Monetary Economics and Banking, Volume 20, Number 4, April 2018 412
ȱ ȱ ǰȱ ȱ ȱ ęȱ ȱ ȱ ȱ ȱ ȱ
¡ȱ¢ȱȱȱ ȱȱȱȱŗŞƖȱȱȱȱǯȱȱ
ǰȱȱȱ ȱȱȱȱ ȱ ¢ȱȱŗŞǯŝşƖȱǻ¢¢ǼȱȱŘŖŗśřȱȱ ȬŖǯŖŞƖȱǻ¢¢ǼȱȱŘŖŗŜřǯȱ ǰȱȱȱȱ ȱȱȱęȱ
ȱǻŚŘƖǼǰȱȱ¢ȱȱȱȱȱǰȱ ȱȱŞǯŚŜƖȱȱŘŖŗśřȱ
ȱşǯřŗƖȱȱŘŖŗŜřȱǻ ȱřǼǯ
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
2000Q32001Q22002Q12002Q42003Q32004Q22005Q12005Q42006Q32007Q22008Q12008Q42009Q32010Q22011Q12011Q42012Q32013Q22014Q12014Q42015Q32016Q2 26%
18%
9%
5%
42%
Trade Manufacturing Industry Commodity Construction Others Graph 2. Credit Shares per Economic Sector to Total Credit
25%
20%
15%
10%
5%
0%
-5%
8.71%
18.79%
6.01%
12.92%
6.85%
19.97%
18.03%
19.66%
8.46%
10.54%
9.31%
11.24%
8.89%
6.47%
3.17%
-0.08%
7.58%
5.76%
Trade Manufacturing Industry
Commodity Construction Others Total
2015Q3 2015Q4 2016Q1 2016Q2 2016Q3
ȱřǯȱȱ ȱȱȱ
Credit Risk Models for Five Major Sectors in Indonesia Śŗř
ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ŘŖŗŜǰȱ
ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ
ȱȱȱ ȱȱȱȱ ȱȱȱȱȱȱȱȱ
ȱȱǻȱǰȱŘŖŗŝǼǯȱ¢ǰȱȱŘŖŖŖȬŘŖŖŝǰȱȱȱȱ ȱȱȱȱǯȱȱȱȱ ȱ¢ǰȱȱȱȱȱȱȱ
ȱȱ ȱȱȱȱȱȱȱǻ ȱŚǼǯ
ȱ ȱ ȱ ŘŖŗŜřȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱ ȱ ȱ ȱ ȱ ȱ ŚǯřŝƖǰȱ ȱ ¢ȱ ȱ ȱ ǻŚǯŘŜƖǼǰȱ
ȱȱǻřǯŞŞƖǼǰȱ¢ȱȱǻřǯřŜƖǼǰȱȱȱȱǻŗǯŞƖǼȱȱ These NPL ratio is calculated from the total credit for each sector (Graph 5).
Graph 4. NPL shares per Economic Sector to Total NPL 100%
90%
800%
70%
60%
50%
40%
30%
20%
10%
0%
2000Q32001Q22002Q12002Q42003Q32004Q22005Q12005Q42006Q32007Q22008Q12008Q42009Q32010Q22011Q12011Q42012Q32013Q22014Q12014Q42015Q32016Q2 Trade Manufacturing Industry Commodity Construction Others
Bulletin of Monetary Economics and Banking, Volume 20, Number 4, April 2018 414
ȱȱ ȱȱȱȱȱȱŘŖŗśřǰȱȱȱȱ
ȱȱęȱȱȱȱȱȱȱŘŖŗŜřǯȱȱȱȱȱ
ȱ ȱȱȱřǯŞŞƖȱȱȱȱȱȱȱȱŘŖŗśřȱǻŘǯŜŘƖǼȱ (Graph 5).
ŘǯŘǯȱȱ
ȱȱǰȱȱȱȱȱȱȱȱȱȱȱȱȱ
ȱȱȱȱȱ¢ǯȱȱęȱȱȱȱȱȱ
ȱȱǻǼȱȱȱȱȱǯȱȱȱȱ
ǰȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ǰȱ ǯȱ ȱ transition matrix of performing loan into nonperforming loan for each bank. The
ȱȱȱȱȱȂȱȱ ȱȱȂȱǯȱ From those approaches, the nonperforming loan model is the most commonly used to determine the impact of macroeconomic conditions on banking risk.
ȱȱȱȱ ȱǰȱȱęȱȱȱȱȱȬ
ȱĚ¡¢ȱȱȱȱȱȱȱȱȱȱǻȱǼȱȱ ȱȱȱ
ȱȱȱȱȮȱ¢ȱ¢ȱȱȱȱȱȱǯȱ
ȱȱȱȱȱ¢£ȱȱȱ ȱȱȱȱ
ȱǯȱȱȱȱǰȱȱȱȱǻŘŖŗřǼȱ
¡ȱ ȱ Ȭȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ
¢ǯȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ
¢ȱŘŖŖŝȱȱȱŘŖŗřǯȱȱȱȱȱȱȱȱȱ ȱ ¢ȱ ěȱ ¢ȱ ȱ ȱ ȱ ¡ǰȱ ȱ ȱ ȱ
¡ȱŗŖŖȱ¡ǰȱȱȱȂȱĜ¢ȱǯȱȱȱ¢ȱ
ǰȱ ȱ ȱ ¢ȱ ǻǼǰȱ ȱ ȱ ¢ȱ ȱ ǻǼȱ ¢ȱ
ěȱȱȱǯ
Graph 5. NPL Ratio per Economic Sector 6%
5%
4%
3%
2%
1%
0%
3.93%
2.62% 3.85%
2.47%
3.28%
4.91%
4.55%
4.26%
1.80% 1.78%1.81%
2.71% 3.05%3.10%
3.36%
3.88%
4.19%4.37%
Trade Manufacturing Industry
Commodity Construction Others Total
2015Q3 2015Q4 2016Q1 2016Q2 2016Q3
Credit Risk Models for Five Major Sectors in Indonesia 415
ȱ ǻŘŖŗŗǼȱ ¢£ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ¢ȱ
ȱ ȱ ȱ ȱ ȱ ȱ ȱ ŘŜȱ ȱ ȱ ȱ ȱ
ȱ ȱ ŗşşŞȱ ȱ ŘŖŖşǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱ ȱȱǻȱ Ǽǰȱȱ¢ȱǰȱȬȱ
ǰȱȱ¢ȱȱȱȱȱȱ ȱȱȱȱȱȱȱ
ǯȱȱȱȱȱȱȱȱǻǼȱ¢ȱȱȱ
ȱȱ ȱȱȱȱȱȱȱǯ
ǰȱ ȱ ȱ ¢ȱ ǻŘŖŗŘǼȱ ȱ ȱ ¢ȱ ȱ ¢ȱ
ȱȱǻǼȱ ȱȱ ȱǻȱȱǼȱȱȱȱ
ȱȱȱȱśŚȱȱȱȬȱȱȱȱȱ
ȱȱŗşşŚȱȱŘŖŖŚǯȱ¢ȱȱȱȱȱ ȱ ǰȱĚǰȱȱȱ
ȱ ȱ ȱ ęȱ ȱ ȱ Ěȱ ȱ ȱ ǯȱ ȱ ȱ
ȱȱȱȱȱ ȱȱȱȱȱȱȱȱȱȱǰȱ
ȱȱȱȱȱȱȱȱ¡ȱȱȱȱ¢ǰȱ ȱȱ
ȱę¢ȱěȱȱȱȱǯ
¢ȱ ȱ ȱ ¢ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ şȱ
ǰȱ ȱ ǰȱ ǰȱ ȱ ȱ ȱ ǻǼȱ ȱ ȱ
ȱȱȱŘŖŖŚŗȱȱȱŘŖŗŘŚǰȱȱȱȱǻŘŖŗřǼȱȱȱ
ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ
ȱȱȱ ȱȱěȱ £ȱȱȱǻ Ǽȱȱ¢ȱ ȱȱȱȱȱȱȱȱ ȱȱ¡¢ȱȱ
ȱȱȱȱȱȱȱȱĜ¢ȱǯȱȱȱȱ
ȱȱȱ ȱȱȱȱȱȱǻęȱǼȱȱȱȱ
ȱȱ¡ȱǻęĞȱǼȱȱȱȱěȱȱȱȱǯȱȱȱȱ
ǰȱȱ¡ȱȱ ȱ¢ȱȱ¢ȱȱǻęȱǼȱȱȱ
ȬȬȬ ȱ ȱ ǻ¡ȱ Ǽȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ratio.
ȱǻŘŖŗřǼȱ¢£ȱȱȱȱȱȱȱ ȱȱȱ
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ǰȱȱ¢ȱǰȱȱĚǰȱ¡ȱȱǰȱ
ȱȱ¢ȱ¡ǯȱȱȱȱȱȱȱ¢Ȭ
ȬȱǰȱȱǰȱȱȱȬȬȱȱȱȱ
ȱ ȱȱȱ ȱȱǯȱ
ȱ ȱ ȱ ȱ ŝśȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ǻŘŖŗśǼȱ ȱ
¢£ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ
ȱ ȱ ȱ ę¡ȱ ěȱ ȱ ȱ ȱ ǯȱ ȱ ȱ
ȱȱȱȱ ȱ ȱǰȱȱǰȱȱ¡ȱǰȱȱ
ȱȱȱę¢ȱěȱȱǯ
Curak et al.ȱǻŘŖŗřǼȱȱȱȱȱȱȱŗŖȱȱ
ȱǯȱȱ¢ȱȱȱȱȱȱŜşȱȱȱȱ
ȱȱȱȱȱŘŖŖřȱȱŘŖŗŖȱȱ ȱȱȱ¢ȱȱ
Bulletin of Monetary Economics and Banking, Volume 20, Number 4, April 2018 ŚŗŜ
ȱǯȱȱȱ ȱȱ ȱȱ ǰȱȱĚǰȱȱ
ȱȱȱȱȱ ȱȱǯȱ¢ǰȱȬęȱ
ȱȱȱȱ£ǰȱȱǻǼǰȱȱ¢ȱǰȱȱ¢ȱ
ȱȱȱȱǰȱȱěȱȱȱǯȱ
Beaton et al.ȱ ǻŘŖŗŜǼȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ panel data of all domestic and foreign banks in Eastern Carribean Currency Union ǻǼǯȱȱȱȱřŚȱȱȱŜȱȱȱȱȱȱȱ ŗşşŜŗȱ ȱ ŘŖŗśŚǯȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ǯȱ ȱ ȱ
ȱ¢ȱȱȱ ȱȱȱȱȱȱ
ȱę¡ȱěǰȱȱěǰȱȱ ȱ ȱȱǯȱȱȱ
ȱȱȱȱ ȱȱȱȱ ȱȱȱ
ȱǯȱȱȱȱȱ ȱȱȱȱȱȱȱ
ȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ¡¢ȱ ȱ
ȱ ȱ ¢ȱ ȱ ¢ȱ ȱ ȱ ǯȱ ȱ ęȱ
ȱȱȱȱȱę¢ȱȱ¢ȱȱȱȱǻǼȱȱ
ȱȱ ȱǰȱ ȱȂȱȱȱȱȱȱȱȱ
ȱȱȱȱȱǯȱ
ȱȱȱǰȱ¢ȱǻŘŖŗŚǼȱ¢£ȱȱȱȱ
ȱȱȱȱȱ¢ȱȱȱȱȱŘŖŗŖȱȱ ŘŖŗŘǯȱȱȱȱ ȱȱȱȱȱȱȱȱȱȱ
ȱȱȱǯȱȱȱ ȱȱ£ȱȱĜ¢ȱȱ
ȱȱȱȱěȱȱȱ ȱȱ ȱȱĚȱ¢ȱěȱ the NPLs.
ȱ ȱ ȱ ȱ ȱ ǻǼȱ ȱ ǰȱ ¡ȱ ȱ
ȱǻŘŖŗśǼȱȱȱȱ¢ȱȱȱȱȱȱŘŜȱȱȱ
ǯȱȱȱȱȱȱȱ¢ȱ ȱȱěȱ
ȱȱȱȱȱȱŘŖŖşȱȱŘŖŗřǯȱȱȱȱȱȱ
ȱ ȱ ŘŖŗŘŗȱ ȱ ȱ ŘŖŗśŚǰȱ ȱet al. ǻŘŖŗśǼȱ ¢£ȱ ȱ ȱ ȱ
ȱ ȱ Řśȱ ȱ ȱ ę¡ȱ ěȱ ǯȱ¡ȱ ȱ ȱ ȱ
ȱ ȱ ȱ ȱ ȱ Ĝ¢ǰȱ ȱ ¢ȱ ȱ ǰȱ ȱ ȱ ȱ ȱ
ęȱȱȱȱǯȱȱȱȱȱ£ǰȱ ȱ ǰȱǰȱ
ȱȱȱȱĚȱȱȱę¢ȱěȱȱǯȱ¢ǰȱȱet al.
ȱȱȱǰȱȱ£ǰȱȱȱ ȱȱȱȱȱȱǰȱ ȱȱĜ¢ǰȱȱ¢ȱȱȱȱȱȱǰȱ
ȱȱȱěȱȱȱȱǯȱȱȱȱȱ¡ȱ
ȱȱ ȱ ȱȱȱęȱǯȱ¡ȱȱȱȱȱěȱ
ȱǰȱ ȱ ȱ ȱȱȱěȱȱǯ
Surjaningsih et al.ȱǻŘŖŗŜǼȱȱȱȱȱȱȱȱȱȱ
ȱ ȱ ¢ȱ ȱ ȱ ȱ ǻȱ ȱ ȱ Ǽȱ ȱ ¢ȱ
ȱ ¢ȱ ǻȱ ȱ ȱ ¢Ǽǯȱ ȱ ȱ ȱ ȱ
ȱȱȱŗŗŞȱȱȱȱȱȱ ȱŘŖŖŗŗȱȱ ŘŖŗśŗǯȱȱȱȱȱȱȱȱȱȱȱȱȱȱ¢ȱ
ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱ ǰȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ǰȱ ȱ
ȱȱȱ¢ȱȱ ȱ ȱǰȱȱȱȱȱȱ¢ȱ
ȱ ǻ¢Ǽǰȱ ȱ ȱ ȱ ȱ ȱ ěȱ ¡ȱ ȱ ǻ¢Ǽǯȱ
ȱȱȱ ȱȱȱ¢ȱȱȱȱȱȱȱȱ
Credit Risk Models for Five Major Sectors in Indonesia Śŗŝ
ȱȱȱę¢ȱ ȱȱ ȱ¢ǯȱȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ conditions compared to consumer loans.
Surjaningsih et alǯȱǻŘŖŗŜǼȱȱȱȱ¢ȱȱȱȱ¢ȱȱ ȱȱȱȱȱȱȱȱȱ¢ȱǯȱȱ
ȱȱȱȱǰȱȱěȱȱȱȱȱȱȱȱ
ȱȱęȱěȱȱǯ
ǯȱ ȱ řǯŗǯȱ¢
ȱ ȱ ȱ Ȃȱ ȱ ȱ ȱ ęȱ ȱ ȱ ǰȱ ȱ ȱ ȱ
ȱ ȱ ǰȱ ȱ £ȱ ¢ȱ ȱ ȱ ȱ ȱ ŗŗŝȱ
ȱȱȱǯȱ ȱȱ ȱȱȱȱȱDZȱ ę¡ȱ ěȱ ȱ ȱ ȱ ȱ ¢ȱ £ȱ ȱ ȱ
ǯȱȱȱ ȱȱȱȱ¢ȱȱȱ¢ȱ ǻŘŖŗŘǼǰȱȱǻŘŖŗřǼǰȱȱȱǯȱǻŘŖŗŜǼǰȱȱȱȱǯȱǻŘŖŗŜǼǯȱȱ
¡ȱěȱȮȱȬȱ
ȱ ęȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ ȱ ę¡ȱ ěȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ representation:
Where the index iȱ ȱ ȱ ȱ ȱ ȱt indicates quarterly time period. LNPLi,t denotes the logit transformation of the NPL ratio that can be expressed as:
ȱ ȱMACROtȱ ȱ ȱ ȱ ȱ ȱ ¡¢ȱ ȱ
ȱěȱȱǰȱμiȱȱȱęȱę¡ȱěǰȱȱΉi,t represents error term. C(L) denotes lag polynomials taking general form
(2)
ȱ¢ȱȱȱ¡¢ȱȱȱȱȱȱȱȱ
ȱ ȱȱȱȱȱȱȱȱǯȱȱ¢ȱȱ on the macroeconomic impact on bank credit risk.
ǻřǼ (1)
Bulletin of Monetary Economics and Banking, Volume 20, Number 4, April 2018 ŚŗŞ
ȱȱȱȱȱȱȱȱȱȱȱȱ a biased estimation because the strict exogenous characteristics of the regressor is
ȱȱǯȱȱȱȱ¢ȱ ȱȱȂȱȱǻŗşŞŗǼř. Nickell bias
ȱ ȱȱȱǰȱȱȱȱęȱȱȱȱȱȱ ȱ
ȱȱȱ ȱȱȱȱǯȱ ǰȱ ȱȱ ȱȱȱǰȱę¡ȱȱȱȱȱȱȱǯ
ȱȱȱȱȱȱȱȱȱȱȱ
ȱ¢£ȱȱȱ¢ǰȱȱę¡ȱěȱȱ ȱȱȱ¢ȱ¢ȱȱ
ȱȱȱȱǻŗşşŗǼȱ¢ȱ £ȱȱȱȱ ǻ Ǽȱ ȱȱȱȱ¢ȱȱȱȱǯ
¢ȱ £ȱȱȱȱǻ Ǽ
The dynamic approach is adapted to take into account the persistence of time ȱȱȱȱǯȱȱȱȱ Ĵȱȱȱ ȱDZ
řȱ ǰȱǯȱǻŗşŞŗǼǯȱȱȱ¢ȱȱ ȱ¡ȱěǰȱǰȱŚşǰȱ ŗřşşȬŗŚŗŜǯ
With i = 1, ..., N and t = 1, ..., T denoting the dimension of cross section and
ȱȱȱǯȱ΅ȱȱȱȱǰȱ denotes the independent
ȱȱȱk×1, ΆȱȱȱȱĜȱȱk×1, and Ήi,t represents an error term.
ȱ ȱ ȱ ǻŗşşşǼȱ ȱ ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱ ǂȱ řŖǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ŘŖƖȱ ȱ ȱ ȱ ȱ ǯȱ
ȱȱȱȱȱ¢ȱȱȱęȬěȱǯȱȱȱ
ȱȱěȱȱ ȱȱȱ¢ȱǯ
(4)
ȱ ȱ ȱ ǻȱ ȱ ȱ ěȱ Ǽȱ ȱ ȱ ȱ System GMM. The System GMM estimator adds restriction to the instrument
ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ¢ȱ
ȱȱȱȱǯ
řǯŘǯȱȱ
ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ by types of credit per major sector in Indonesia: (i) commodity sector (including
ȱȱǼDzȱǻǼȱȱȱDzȱǻǼȱȱ¢DzȱǻǼȱ
ȱ ǻ¢ȱ ȱ ȱ ǼDzȱ ȱ ǻǼȱ ȱ ǯȱ ȱ ȱ ŗŗŝȱ
ȱȱȱȱŗŖŚȱȱȱȱŗřȱȱȱȱ ŘŖŖŖȱŗȱȱŘŖŗŜȱřȱǻDZȱȱǼǯȱ
(5)
Credit Risk Models for Five Major Sectors in Indonesia Śŗş
ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ transformation as stated on equation (2). This transformation not only ensures that
ȱȱȱȱȱ and ȱǻȱȱ ȱŖȱȱŗǼȱȱȱ
ȱȱȱȱ¢¢ȱǯȱȱ¢ǰȱȱȱȱ
ȱȱȱȱȱŖȱȱȱǰȱȱȱȱȱȱȱ ȱ£ȱ ȱȱ£ȱȱȱȱȱȱȱȱȱǯȱȱȱǰȱ
ȱȱȱȱęǯȱȱȱȱȱ ȱ
ȱȱȱȱȱȱȱȱŖȱȱ¢ȱǰȱ¢ȱ ȱȱ
ȱȱȱȱȱȱȱȱŖȱǻǯǯȱŗŖȬŜǼǯȱȱ ȱȱȱ
ȱȱȱȱȱȱȱȱȱȱǯȱ
ȱ ¡¢ȱ ȱ ȱMACROt is describe in Table 1.
¢ȱȱ ȱ ȱȱȮȱ¢ȱȱǻrgdpsa_qǼȱȱ¡ȱȱȱ
ȱȱȱ ȱȱǯȱȱȱȱȱȱ ȱ ȱ
ȱȱȱǯȱĴȱȱȱ ȱȱȱȱȱȱȱȱ
ȱȱȱȱȱȱȱǯȱȱȱȱȱȱȱ
ȱȱȱȱȱȱ ȱȱȱ ȱȱǯȱȱȱ policy rate (chpolrateǼȱȱȱȱȱȱȱȱ¢ȱ¢ȱ ȱ
ěȱǯȱ¢ȱȱȱĚȱǻhp_qǼȱěȱȱȱȱ ȱ ǻ ȱ ěǼȱ ȱ ¢ȱ ¢ȱ ȱ Ěȱ ǻcomp_q)
ȱ¢ȱȱ¡ȱȱȱȱ¡ȱȱȱȱęȱǯȱ
ȱȱȱȱȱŘŖŖŖŗȱȱŘŖŗŜřǯȱ Table 1.
ȱȱȱȱȱ
rgdpsa_q ȱ ȱ ȱȱȱȱȱ¢ȱǰȱ¢ BI chpolrate ȬȬȱȱȱȱȱȱ¢ȱ BI nexr_q ȬȬȱȱȱȱȱȱȱ¡ȱǰȱȱȱ
ȱȱȱȱȱȱ
BI hp_q ¢ȱ ȱȱȱȱǻȱȱĚȱǼ BI comp_q ¢ȱ ȱȱ¢ȱȱǻ¢ȱȱĚȱǼ BI
IV. RESULTS AND ANALYSIS Śǯŗǯȱ¡ȱěȱȱȱ
ȱȱȱȱę¡ȱěȱȱȱȱěȱ¢ȱ
ȱȱ¡ȱǻ¡ȱę¡ȱěȱĜȱǼȱȱȱȱ
ȱŘǯȱȱȱȱȱȱȱȱȱȱǰȱȱȱ quality (payment collectability status) from current and special mention does not
¢ȱȱȱȱ ȱȱǯȱǰȱȱ¢ȱȱȱ
ȱ ȱȱǻ ȱǼȱȱȱȱȱȱȱ¡¢ȱǯȱȱȱ
ȱǰȱȱȱȱȱȱȱȱȱȱ
ȱěȱȱ¢ȱȱȱǯ
ȱǰȱȱȱȱ¡¢ȱȱȱȱęȱȱȱ ŗŖƖȱȱ¢ȱȱ¢ȱȱȱȱȬȱǻ¡Ǽȱ¡ȱȱ¢ȱ
ȱȱȱȱǯȱȱȱȱȱȱ¡ȱ
Bulletin of Monetary Economics and Banking, Volume 20, Number 4, April 2018 ŚŘŖ
ěȱȱȱǯȱȱȱ ȱȱȱȱȱȱȱ¢ȱ
ȱ ȱȱȱȱȱǯȱȱȱȱȱ ȱ ȱ
¢ȱěȱȱȱȱȱȱ¢ȱȱȱ¢ȱȱ
ǯȱȱȱȱȱȱȱȱ¢ȱȱȱǯȱ
¢ȱ ȱ Ěȱ ěȱ ȱ ȱ ȱ ¢ȱ ȱ ȱ
ǰȱ ȱ ȱ ȱ Ěȱ Ěȱ ȱ ȱ ȱ
¢ǰȱǰȱȱȱǯȱȱǰȱȱȱ¢ȱȱ ȱ
ȱ ȱ ȱ ȱ ¢ǰȱ ǰȱ ȱ ȱ ǰȱ ȱ ȱ
¡ȱȱ¢ȱěȱȱȱȱȱǯȱȱȱȱȱ¢ȱ
ȱ ȱ ǻŘŖŗŖřȱ ȱ ŘŖŗŜřǼȱ ȱ ȱ ȱ ȱ ȱ ȱ sector.
Table 2.
¡ȱěȱȱȱǻǼȱȱ
ŗǯȱ
¢ Řǯȱ
řǯȱ
ȱ
¢
4.
śǯȱ
NPL Ratio (Logit transf), L2
ŖǯŖŜŗ ** ŖǯŖŞş *** ŖǯŖşř *** ŖǯŗŖş *** ŖǯŖŘŜ
ǻŖǯŖŘŚǼ ǻŖǯŖŘŗǼ ǻŖǯŖŘŚǼ ǻŖǯŖŘŘǼ ǻŖǯŖřŖǼ
NPL Ratio (Logit transf), L1
ŖǯŝŝŖ *** ŖǯŝŗŘ *** ŖǯŝŖś *** Ŗǯŝŗş *** ŖǯŞŞŘ ***
ǻŖǯŖŘŗǼ ǻŖǯŖŘŝǼ ǻŖǯŖřŖǼ ǻŖǯŖŚŝǼ ǻŖǯŖřŝǼ
rgdpsa_q, L4 ȬŖǯŖŞŜ ** ȬŖǯŖŞŜ *** ŖǯŖŘř ȬŖǯŖŖŘ ȬŖǯŖŜŚ **
ǻŖǯŖřśǼ ǻŖǯŖŘŜǼ ǻŖǯŖřŗǼ ǻŖǯŖŚŖǼ ǻŖǯŖŘŝǼ
ȏǰȱř ŖǯŖŖř ȬŖǯŖŗŝ ȬŖǯŖŝŞ ** ŖǯŖŘř ȬŖǯŖŖś
ǻŖǯŖŘşǼ ǻŖǯŖŘŝǼ ǻŖǯŖřŚǼ ǻŖǯŖřşǼ ǻŖǯŖŘřǼ
rgdpsa_q, L2 ȬŖǯŖŜŜ ** ȬŖǯŖŗŗ ȬŖǯŖŜŝ ** ŖǯŖŝş * ȬŖǯŖřŜ *
ǻŖǯŖřŘǼ ǻŖǯŖřŖǼ ǻŖǯŖřŘǼ ǻŖǯŖŚŖǼ ǻŖǯŖŘŖǼ
rgdpsa_q, L1 ȬŖǯŖŝŗ ** ȬŖǯŖřŚ ȬŖǯŖŘŚ ȬŖǯŖŝŝ * ȬŖǯŖŗŚ
ǻŖǯŖřŘǼ ǻŖǯŖŘşǼ ǻŖǯŖřŜǼ ǻŖǯŖŚŚǼ ǻŖǯŖŘŝǼ
chpolrate, L4 ȬŖǯŗŗŜ *** ȬŖǯŖŚŜ ** ȬŖǯŖŗŞ ȬŖǯŖŞŖ ***
ǻŖǯŖŘŝǼ ǻŖǯŖŗşǼ ǻŖǯŖřŞǼ ǻŖǯŖŗşǼ
ǰȱř ŖǯŗřŜ *** ŖǯŖŚř * ȬŖǯŖŗŝ ŖǯŖŝŘ ***
ǻŖǯŖŚŗǼ ǻŖǯŖŘśǼ ǻŖǯŖśŖǼ ǻŖǯŖŘśǼ
chpolrate, L2 ȬŖǯŖŜŞ * ŖǯŖřŜ ŖǯŖŜř ŖǯŖřŖ
ǻŖǯŖŚŗǼ ǻŖǯŖŘŝǼ ǻŖǯŖŚŜǼ ǻŖǯŖŘŚǼ
chpolrate, L1 ŖǯŖŘŖ ȬŖǯŖŗŖ ŖǯŖŗŞ ȬŖǯŖŘś
ǻŖǯŖŘşǼ ǻŖǯŖŗşǼ ǻŖǯŖřŝǼ ǻŖǯŖŗşǼ
nexr_q, L4 ŖǯŖŖŗ
ǻŖǯŖŖŘǼ
¡ȏǰȱř ȬŖǯŖŖř
ǻŖǯŖŖŘǼ
nexr_q, L2 ŖǯŖŖŖ
ǻŖǯŖŖŘǼ
nexr_q, L1 ŖǯŖŖŘ
Credit Risk Models for Five Major Sectors in Indonesia 421
ŚǯŘǯȱȱ
ȱȱǰȱȱȱȱȱȱȱ¡ȱěȱȱȱ ȱ conduct GMM estimation as a comparison. The estimation results are presented
ȱȱřǯ
Table 2.
¡ȱěȱȱȱǻǼȱȱȱǻǼ
ŗǯȱ
¢ Řǯȱ
řǯȱ
ȱ
¢
4.
śǯȱ
ǻŖǯŖŖřǼ
hp_q, L4 ŖǯŖŗŞ ŖǯŖŖŚ ŖǯŖŝŚ ***
ǻŖǯŖŗŘǼ ǻŖǯŖŗŘǼ ǻŖǯŖŗşǼ
ȏǰȱř ȬŖǯŖřŖ ** ȬŖǯŖŘŞ ** ȬŖǯŖřŘ
ǻŖǯŖŗŘǼ ǻŖǯŖŗŘǼ ǻŖǯŖŘŖǼ
hp_q, L2 ȬŖǯŖŖş ŖǯŖŗŞ ȬŖǯŖŚŚ **
ǻŖǯŖŗŚǼ ǻŖǯŖŗŚǼ ǻŖǯŖŘŗǼ
hp_q, L1 ȬŖǯŖŖŚ ȬŖǯŖŜŝ *** ȬŖǯŖŘŗ
ǻŖǯŖŗŗǼ ǻŖǯŖŗśǼ ǻŖǯŖŘřǼ
comp_q, L4 ȬŖǯŖŖŗ ŖǯŖŖŘ ŖǯŖŖŗ
ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ
ȏǰȱř ŖǯŖŖŖ ŖǯŖŖŘ * ŖǯŖŖŖ
ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ
comp_q, L2 ȬŖǯŖŖř *** ȬŖǯŖŖŗ ȬŖǯŖŖŘ *
ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ
comp_q, L1 ŖǯŖŖŖ ŖǯŖŖŖ ŖǯŖŖŘ
ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ ǻŖǯŖŖŘǼ
Dummy ȬŖǯŖśŚ
ǻŖǯŖřşǼ Common intercept
term
ȬŖǯŘŘř ǻŖǯŗŖřǼ
*** ȬŖǯŚŗŚ ǻŖǯŗŖşǼ
*** ȬŖǯřşś ǻŖǯŗŗşǼ
*** ȬŖǯśřŚ ǻŖǯŗŘŖǼ
*** ȬŖǯŘśş ǻŖǯŗŜŚǼ
ȱ ŚŖŝŜ śŘŝş ŚşŖř řŜŜŘ ŜřşŖ
Total Groups ŗŖŝ 114 115 ŗŖŝ 115
Ȭȱǻ Ǽ ŖǯŜŞŚ ŖǯŜŘś ŖǯŜŘś ŖǯŜŘŗ ŖǯŜŞŗ
Ȭȱǻ Ǽ Ŗǯşŝŗ Ŗǯşşś ŖǯşşŖ ŖǯşŞř ŖǯŝŜř
DZȱȘȘȘȱȱęȱȱŗƖǰȱȘȘȱęȱȱśƖǰȱȘȱęȱȱŗŖƖǯȱȱȱȱȱȱȱǻǼǯ
Bulletin of Monetary Economics and Banking, Volume 20, Number 4, April 2018 422
ȱřǯ
Generalized Method of Moments (GMM estimation results)
ŗǯȱ
¢ Řǯȱ
řǯȱ
ȱ
¢
4.
śǯȱ
NPL Ratio (Logit transf), L2
ŖǯŖśŘ ŖǯŖŞŜ *** ŖǯŖŜŝ * Ŗǯŗśś *** ŖǯŖŚŜ
ǻŖǯŖřŚǼ ǻŖǯŖřŗǼ ǻŖǯŖřŚǼ ǻŖǯŖŚŖǼ ǻŖǯŖŘşǼ
NPL Ratio (Logit transf), L1
ŖǯŝŞŘ *** ŖǯŝŘŚ *** ŖǯŜŞř *** ŖǯŝŞŘ *** ŖǯşŘŗ ***
ǻŖǯŖřŘǼ ǻŖǯŖřŝǼ ǻŖǯŖřŚǼ ǻŖǯŖśŗǼ ǻŖǯŖřŜǼ
Rgdpsa_q, L4 ȬŖǯŖśř ȬŖǯŖŜś ** ŖǯŖŖŞ ŖǯŖŖŘ ȬŖǯŖřŚ
ǻŖǯŖřśǼ ǻŖǯŖŘŝǼ ǻŖǯŖřśǼ ǻŖǯŖŚŜǼ ǻŖǯŖŘśǼ
ȏǰȱř ŖǯŖŖŘ ȬŖǯŖŖŞ ȬŖǯŖşŗ ** ŖǯŖřŜ ŖǯŖŘŚ
ǻŖǯŖřřǼ ǻŖǯŖŘŞǼ ǻŖǯŖřŝǼ ǻŖǯŖřşǼ ǻŖǯŖŘŜǼ
Rgdpsa_q, L2 ȬŖǯŖŝŘ ** ȬŖǯŖŗř ȬŖǯŖŞŘ *** ŖǯŖŞŗ ** ȬŖǯŖŗŞ
ǻŖǯŖřŜǼ ǻŖǯŖŘşǼ ǻŖǯŖřŘǼ ǻŖǯŖřşǼ ǻŖǯŖŘŖǼ
Rgdpsa_q, L1 ȬŖǯŖśŖ ȬŖǯŖřř ȬŖǯŖŘŜ ȬŖǯŖśŞ ŖǯŖŗŜ
ǻŖǯŖřřǼ ǻŖǯŖřŖǼ ǻŖǯŖřŜǼ ǻŖǯŖŚřǼ ǻŖǯŖŘřǼ
Chpolrate, L4 ȬŖǯŗŘŖ *** ȬŖǯŖŚş ** ȬŖǯŖŚŘ ȬŖǯŖŞŘ ***
ǻŖǯŖŘŞǼ ǻŖǯŖŘŗǼ ǻŖǯŖřŚǼ ǻŖǯŖŗşǼ
ǰȱř ŖǯŗŚś *** ŖǯŖśŖ * ȬŖǯŖŖŞ ŖǯŖŞř ***
ǻŖǯŖŚŗǼ ǻŖǯŖŘŜǼ ǻŖǯŖŚŝǼ ǻŖǯŖŘŚǼ
Chpolrate, L2 ȬŖǯŖŞŜ ** ŖǯŖŘř ŖǯŖŜŗ ŖǯŖŖş
ǻŖǯŖŚřǼ ǻŖǯŖŘŜǼ ǻŖǯŖŚŞǼ ǻŖǯŖŘŘǼ
Chpolrate, L1 ŖǯŖřŘ ȬŖǯŖŗŖ ŖǯŖŚŜ ȬŖǯŖŗŗ
ǻŖǯŖŘŞǼ ǻŖǯŖŗŝǼ ǻŖǯŖŚŗǼ ǻŖǯŖŘŖǼ
Nexr_q, L4 ŖǯŖŖŖ
ǻŖǯŖŖŘǼ
¡ȏǰȱř ȬŖǯŖŖŚ **
ǻŖǯŖŖŘǼ
Nexr_q, L2 ȬŖǯŖŖŗ
ǻŖǯŖŖřǼ
Nexr_q, L1 ŖǯŖŖŘ
ǻŖǯŖŖřǼ
ȏǰȱŚ ŖǯŖŗś ŖǯŖŖŝ ŖǯŖşŝ ***
ǻŖǯŖŗŚǼ ǻŖǯŖŗŘǼ ǻŖǯŖŘŗǼ
ȏǰȱř ȬŖǯŖŘś ** ȬŖǯŖŘş ** ȬŖǯŖŗŗ
ǻŖǯŖŗŘǼ ǻŖǯŖŗŚǼ ǻŖǯŖŘŗǼ
ȏǰȱŘ ȬŖǯŖŗř ŖǯŖŖş ȬŖǯŖŚŗ *
ǻŖǯŖŗśǼ ǻŖǯŖŗŜǼ ǻŖǯŖŘŘǼ
ȏǰȱŗ ȬŖǯŖŖś ȬŖǯŖŝś *** ȬŖǯŖŗŝ
ǻŖǯŖŗŖǼ ǻŖǯŖŗśǼ ǻŖǯŖŘşǼ
Comp_q, L4 ȬŖǯŖŖŗ ŖǯŖŖŘ ŖǯŖŖŗ
ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ
ȏǰȱř ŖǯŖŖŖ ŖǯŖŖř * ȬŖǯŖŖŘ **
ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ
Credit Risk Models for Five Major Sectors in Indonesia ŚŘř
ȱ ǰȱ ȱ ȱ ȱ ¡¢ȱ ȱ ęȱ ȱ ȱ ȱ ŗŖƖȱ
ęȱȱȱ¢ȱȱ¢ȱȱȱȱȬȱǻ¡ȱŗǼǯȱ
ȱȱȱȱ ȱȱęȱȱȱȱę¡ȱȱȱǯȱ
ǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ǻȱ ȱ řǼȱ ęȱ ȱ
ȱȱȱȱǯȱǰȱȱȱȱȱȱȱȱȱ
¢ȱȱȱę¡ȱěȱ ȱȱǯȱ
Śǯřǯȱ
ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ macroeconomic determinants changed in permanent basis. Equation (1) can be
Ĵȱę¢ȱȱȱȱȱȱȱŚǯŗȱȱ DZȱ
ȱřǯ
Generalized Method of Moments (GMM estimation results) (Continued)
ŗǯȱ
¢ Řǯȱ
řǯȱ
ȱ
¢
4.
śǯȱ
Comp_q, L2 ȬŖǯŖŖŘ * ȬŖǯŖŖŗ ȬŖǯŖŖŘ **
ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ
Comp_q, L1 ŖǯŖŖŖ ŖǯŖŖŖ ŖǯŖŖŗ
ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ ǻŖǯŖŖŗǼ
Dummy ȬŖǯŗśŚ *
ǻŖǯŖŞŗǼ Common intercept
term
ȬŖǯŘŝŜ ǻŖǯŗřşǼ
** ȬŖǯŚŗŝ
ǻŖǯŗŜřǼ
*** ȬŖǯŚŜŞ ǻŖǯŗśŖǼ
*** ȬŖǯŘŚş ǻŖǯŗşŖǼ
ȬŖǯŗŚş ǻŖǯŗŗřǼ
ȱ ŚŖŝŜ śŘŝş ŚşŖř řŜŜŘ ŜřşŖ
Total groups ŗŖŝ 114 115 ŗŖŝ 115
Total Instruments ŗřŚ 124 ŗŘŖ ŗŘŖ 142
ȱȱȬ Ŗǯşřŝ ŖǯŚŜŚ ŖǯŚŖŚ ŖǯŞŘŝ ŖǯŞŚŚ
ȬȱǻŗǼȱȱ
Ȭ
ŖǯŖŖŖ ŖǯŖŖŖ ŖǯŖŖŖ ŖǯŖŖŖ ŖǯŖŖś
ȬȱǻŘǼȱȱ
Ȭ
ŖǯřŝŖ ŖǯŚŗş Ŗǯŝŝś ŖǯŖŘş ŖǯśŚŚ
DZȱȘȘȘȱȱęȱȱŗƖǰȱȘȘȱęȱȱśƖǰȱȘȱęȱȱŗŖƖǯȱȱȱȱȱȱȱǻǼǯ
ǻŜǼ
Bulletin of Monetary Economics and Banking, Volume 20, Number 4, April 2018 424
ȱ ǻȬǼȱ ¢ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱ ȱ ȱ ȱ ȱ ǻȏǼǰȱ ȱ ȱ ȱ ¢ȱ ȱ ǻǼǰȱ
¡ȱȱ ȱǻ¡ȏǼǰȱȱȱĚȱǻȏǼǰȱȱ¢ȱȱ
ĚȱǻȏǼȱȱȱȱ ȱȱȱǻŝǼȱȱǻŗŗǼȱȱ DZ
ǻŝǼ ǻŞǼ ǻşǼ ǻŗŖǼ (11)
ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ
ȱȱȱȱȱȱȱȱŚǯ Table 4.
ȱȱȱȱ ȱȱȱȱȱȱ
ŗǯȱ
¢ Řǯȱ
řǯȱ
ȱ
¢
4.
śǯȱ
ȱȱ¢ȱ ȱȱȱȱȱȱȱǻǼȱȱȱ¢ȱȱDZ
Rgdpsa_q ȬŖǯŖŜŘ ȬŖǯŖřś ȬŖǯŖřŚ ŖǯŖŖŜ ȬŖǯŖŜŗ
Chpolrate ȬŖǯŖŖŞ ŖǯŖŖś ŖǯŖŗř ȬŖǯŖŖŘ
Nexr_q ȬŖǯŖŖŗ
Comp_q ȬŖǯŖŖŜ ȬŖǯŖŗŝ ȬŖǯŖŖŜ
ȏ ȬŖǯŖŖŗ ŖǯŖŖŗ ŖǯŖŖŗ
ȱ ȱ Śǰȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ ȱ long run if the quarterly rates of the macroeconomic determinants increased by
ȱ ȱ ȱ ȱ ȱ ȱ ǯȱȱ ȱ ȱ ȱ ȱ ǻǼȱȱȱȱ ȱ ȱǻȱȱŚȱȱȱȱȱǼȱ ȱ
ȱ ȱ ȱ ȱ ȱ ȱ ¢ȱ ȱ ¢ȱ ŖǯŖŜŘǰȱ ŖǯŖřśȱ ȱ ȱ ȱ
ǰȱŖǯŖřŚȱȱȱȱǰȱȱŖǯŖŜŗȱȱȱȱǰȱȱ ȱ
ȱȱȱȱȱȱȱȱ¢ȱŖǯŖŖŜǯ