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WP/ 8 /2014

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Data Source Availability

4

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𝐶𝐼𝑥𝑦 =1

𝑇∑[𝐶𝑡

𝑥𝐶𝑡𝑦

+ (1 − 𝐶𝑡𝑥)(1 − 𝐶𝑡

𝑦)]

𝑇

𝑡=1

𝐶𝑡𝑥 𝐶𝑡

𝑥

𝐶𝑡𝑥 = {0, 𝑖𝑓 𝑥 𝑖𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑐𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑎𝑟𝑦 𝑝ℎ𝑎𝑠𝑒 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡; 1, 𝑖𝑓 𝑥 𝑖𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑒𝑥𝑝𝑎𝑛𝑠𝑖𝑜𝑛𝑎𝑟𝑦 𝑝ℎ𝑎𝑠𝑒 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡}

𝐶𝑡𝑦

= {0, 𝑖𝑓 𝑦 𝑖𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑐𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑎𝑟𝑦 𝑝ℎ𝑎𝑠𝑒 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡; 1, 𝑖𝑓 𝑦 𝑖𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑒𝑥𝑝𝑎𝑛𝑠𝑖𝑜𝑛𝑎𝑟𝑦 𝑝ℎ𝑎𝑠𝑒 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡}

1

35[−3,12,17,12, −3]

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20

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22

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24

25

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IHSG

Credit/GDP IHSG IHPR IHSG IHPR IHPR

Frequency

based filter89% 84% 25% 80% 38% 36%

Credit-

Credit/GDP-

IHSG

Turning

point

analysis

59% 22% 17% 26% 55% 61%Credit-

Credit/GDP

Narrow Credit Narrow Credit/GDP Common

Cycle

27

Cycle

Average Duration (Quarter)

Business Cycle (PDB)

Financial Cycle (Narrow Credit)

Financial Cycle (Broad Credit)

Broad

Credit/GDP

Credit/GDP IHSG IHSG

Frequency

based filter77% 51% 52%

Credit-

Credit/GDP

Turning

point

analysis

72% 2% 31%Credit-

Credit/GDP

Broad Credit Common

Cycle

28

Peak to peak 19 38 40

Trough to trough 17 39 35

Cycle 18 39 37

Financial Cycle / Business Cycle 2.10 2.04

29

30

31

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Aikman, D., A Haldane and B Nelson, “Curbing the Credit Cycle”, as presented at the Columbia

University Center on Capitalism and Society Annual Conference, New York, November

(Revised March 2011), 2010

Basel Committee on Banking Supervision, “Guidance for national authorities operating the

countercyclical capital buffer, Bank for International Settlement”, BIS, 2010.

Borio, C., “The Financial Cycle and Macroeconomics: What Have We Learnt?”, BIS Working Papers,

395, 2012

Bry, G., C. Boschan, “Cyclical Analysis of Time Series: Selected Procedure and Computer Program”,

National Bureau of Economic Research, Technical Paper 20, 1971.

Christiano, L.J., Fitzgerald, T.J., “The Band Pass Filter”, International Economic Review, volume 44,

issue 2, pages 435 – 465, 2003.

Claessens, S., Kose, M.A., Terrones, M., “How Do Business and Financial Cycles Interact?”, IMF

Working Paper, WP/11/88, 2011

Comin, D., Gertler, M., “Medium Term Business Cycle”, American Economic Review, Volume 96 No.

3, 2006

Drehmann, M., C. Borio and K. Tsatsaronis, “Characterizing The Financial Cycle: Don’t Lose Sight of

The Medium Term!”, BIS Working Paper, 380, 2012

English, W., Tsatsaronis, K., Zoli, E., “Assessing the Predictive Power of Measures of Financial

Conditions for Macroeconomic Variables”, BIS Paper No. 22, 2005

Everts, M., 2006, “Duration of Business Cycles”, Munich Personal RePEc Archive (MPRA), No.1219,

2006

Harding, D., Pagan, A., “Dissecting the Cycle: A Methodological Investigation”, Journal of

Econometrics, volume 49, pages 365 – 381, 2002.

Harding, D., Pagan, A., “Synchronization of Cycles”, Journal of Econometrics, 132, pages 59-79, 2006.

Hatzius, J., Hooper, P., Mishkin, F.S., Schoenholts, K.L., Watson, M.W., “Financial Condition Indexes:

A Fresh Look after the Financial Crisis”, National Bureau of Economic Research, 2010

Male, R. Louise, “Developing Country Business Cycle: Characterizing the Cycle and Investigating the

Output Persistence Problem”, 2009

Ng, T., “The Predictive Content of Financial Cycle Measures for Output Fluctuations”, BIS Quarterly

Review, June 2011

Utari, G.A.D., Arimurti, T., “Financial Cycles In The Era of Free Capital Flows”, Bank Indonesia

Research Report, 2014

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APPENDIX

A. Interpolation of Private Sector Foreign Debt

The recording of private sector foreign debt in quarterly format first started to be

done in 1992-Q2, while previously the data was yearly. Because of that, data interpolation

needs to be done from 1992-Q1 up to 1999-Q1. As a proxy, the growth pattern of PMTB

investment (Gross Fixed Capital Formation) is chosen, either real or nominal growth with

the following results:

Panel 1. Interpolation results at

level

Panel 2. Interpolation results in

growth (yoy)

Chart A.1. Interpolation using Nominal Investment (DSTA Data)

Panel 1. Interpolation results at

level

Panel 2. Interpolation results in

growth (yoy)

Chart A.2. Interpolation using Nominal Investment (SOFIE Data)

34

Panel 1. Interpolation results at

level

Panel 2. Interpolation results in

growth (yoy)

Chart A.3. Interpolation using Real Investment (SOFIE Data)

The interpolation which is selected is the first one, using nominal SOFIE data, because its

results are closest to yearly private sector foreign debt data.

B. Bry-Boschan Coding

Matlab code which was developed by Rand and Tarp (2002) already accommodates

the use of monthly, quarterly, and yearly data. In the original code, the Bry-Boschan

algorithm can only be used for the short term cycle with the wavelength (the distance from

the peak to the peak or trough to trough) and phase (the distance from the trough to peak

or peak to trough) which has already been specified for monthly, quarterly, and yearly data.

Specifically for the purposes of this study, modifications were only done to quarterly data,

so that the code can be used for a variety of different phases and cycles.

In general, the main algorithm that is used is still the same as the original code,

except in the weighting which is used in the Spencer curve. Following Everts (2006), the

weighting in the Spencer curve that is used is 1/35 [-3,12,17,12,-3]. Everts (2006) stated

that Harding and Pagan used 15-point smoothing of the Spencer curve, which is actually

more suitable for monthly data. The complete program code is as follows: