PROCEEDINGS
er
national S
tatis
tical Ins
titut
R
egional S
tatis
tics Conf
er
ence 20
17
“Enhancing S
tatis
tics, Pr
osper
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Bali, 20 - 2
4 Mar
ch 20
17
of The
Published by:
Statistics and its applications toward enhancement statistics to prospering human life
INTERNATIONAL STATISTICAL INSTITUTE
REGIONAL STATISTICS CONFERENCE 2017 (ISI RSC 2017)
ISBN
: 978-602-61659-0-9
Book Size
: 16,5 cm x 24 cm
Total Pages
: 1095 pages
Manuscript:
Statistics Department – Bank Indonesia
Cover Design:
Statistics Department – Bank Indonesia
Published by:
Statistics Department – Bank Indonesia
Printed by:
Statistics Department – Bank Indonesia
Prohibited to announce, distribute, communicate, and/or copy part or all of this book for
commercial purpose without permission from Bank Indonesia
Preface
The ISI Regional Statistics Conference (RSC) 2017 was organized by the ISI and its
South East Asia Regional Network (ISI-SEA Network) in collaboration with Bank Indonesia
(BI) as the co-host, and supported by Badan Pusat Statistik (Statistics Indonesia), Ikatan
Perstatistikan Indonesia (Association of Indonesia Statistician), and Forum Masyarakat
Statistik (Indonesia Statistics Community Forum).
The second ISI Regional Statistics Conference (ISI RSC 2017) was a three day
conference preceded by the ECB-BIS-BI Regional Seminar on Central Banking Statistics,
the Irving Fisher Committee (IFC)-BI Satellite Seminar on Big Data, and short courses.
The Main Conference program was held from 22 to 24 March 2017 in Bali International
Conference Center, Nusa Dua, Bali. The conference theme “Enhancing Statistics, Prospering
Human Life” was chosen to encourage researchers and practitioners who are actively involved
in statistical science in academia, industry, national statistical offices, national and international
agencies, central banks, as well as users to participate in discussions on how statistics may
contribute to prosper human life. The conference theme was wide enough to accommodate
participants with diverse interests.
There were three Plenary Sessions and parallel sessions discussing 193 papers in 29
Invited Paper Sessions (IPS) and 34 Contributed Paper Sessions (CPS).
This publication contains abstracts, papers and materials presented in the ISI RSC 2017.
Release of this publication has been made possible by the assistance and contribution of all
contributors. To all parties who have been involved in the completion of this publication, we
would like to express our sincere gratitude and high appreciation. Hopefully this publication
will be a useful resource for any purposes. Comments and suggestions to improve the
publication are always welcome.
Table of Contents
Preface ... iii
Welcoming Remarks ... xxiv
Wednesday, 22 March 2017
Opening Remarks
PS01: Banking and Finance
Information and Statistics in Public Policy Making ... 3
Boediono
Statistics for Banking and Finance ... 13
Stephen Grenville
The New Cross-Border Finance in Asia ... 29
Eli Remolona
IPS01: Statistics for Sharia Economics and Finance
Could Big Data Take Islamic Finance to the Next Frontier? ... 38
Zamir Iqbal
Measuring Islamic-Based Socioeconomic Progress: Issues and Challenges ... 39
Muhamed Zulkhibri
Prudential and Structural Islamic Financial Indicators (PSIFIs) – Soundness
Indicators for Islamic Finance ... 40
Md. Salim Al Mamun
Integration of Islamic Commercial and Social Finance for Socio-Economic
Development and Financial System Stability ... 41
Alan Agresti
Cluster Analysis for Ordered Categorical Data ... 45
Ivy Liu
Dispersion and Response Styles in Ordinal Regression ... 46
Gerhard Tutz
IPS04: Where To In Statistical Education Across School, All University
Disciplines and Beyond?
The Good, the Bad and Lessons for the Way Forward for Teaching Statistics
and Data ... 48
Helen MacGillivray
Teaching and Learning Statistics: Lessons from Malaysian Classrooms ... 49
Mohd. Majid Konting
Teaching Statistics in Indonesian Schools: Today and Future ... 50
Muhammad Arif Tiro
CPS01: Environmental & Natural Resources Statistics
Growth Externalities on the Environmental Quality Index of East Java Indonesia,
Spatial Econometrics Mode ... 52
Rahma Fitriani, Wara Alfa Syukrilla
Statistical Analysis for NDVI Trend and Variation Using MODIS Data in the
Cloud Forest of Khao Nan National Park, Thailand during 2000-2015 ... 59
Anusa Suwanwong, Noodchanath Kongchouy, Attachai Ueranantasun
Improved the Forecasting of ANN-ARIMA Model Performance: A Case Study
of Water Quality at the Offshore Kuala Terengganu, Terengganu, Malaysia .... 66
Muhamad Safiih Lola, Mohd Noor Afiq Ramlee, Vigneswary a/p Ponniah,
Nurul Hila Zainuddin, Razak Zakariya, Md Suffian Idris, Idham Khalil
Linear Mixed Models for Analyzing Total Weights of Fish in Na Thap River,
Southern Thailand ... 73
Time Series Models ... 78
Manik S. Awale
Transformation Cure Models for Enrichment Design in Targeted Clinical Trials 84
Chih-Yuan Hsu, Chen-Hsin Chen
A Powerful Method to Meta-Analysis for Testing No Treatment Effects ... 89
Kuang Fu Cheng
CPS03: Macroeconomic Statistics (1)
Evaluation of Inflation Targeting among ASEAN Countries, Does It Have
Impact on Unemployment and Economic Growth ... 94
Chindy Saktias Pratiwi, Putu Wira Wirbuana
Between Hawks and Doves: Measuring Central Bank Communication ... 101
Stefano Nardelli, David Martens, Ellen Tobback
Cyclic Structural Equation Models and Their Identifiability ... 108
Mario Nagase, Yutaka Kano
Forecasting Inflation Rate in Sulawesi Using Generalized Space-Time
Autoregressive (GSTAR) Models ... 115
Asrirawan, Sumantri
Food Price Information System Application: Survey Based Data Towards
Regional Inflation Management Protocol ... 122
Noor Yudanto, Handri Adiwilaga, Maxmillian T. Tutuarima
CPS04: Residential & Commercial Property Statistics
An Application of Hedonic Price Models in Property Markets: Consumer
Willingness to Pay and Property Price Index Estimation ... 130
Masarina Flukeria, Dewi Agita Pradaningtyas
A Micro-Level View of Housing Affordability in Malaysia Using an Age
Herina Prasnawaty Dewayany, Listyowati Puji Lestari, Ahmad Rasyid
CPS05: Probability Theory & Statistical Modelling (1)
A Naïve Method for Variable Reduction in Multidimensional Data ... 160
Noppachai Wongsai, Sangdao Wongsai, Apiradee Lim
Flexible Functional Clustering of Accelerometer Data Using Transformed
Input Variables ... 167
Yaeji Lim, Hee-Seok Oh, Ying Kuen Cheung
Technical Efficiency Analysis of SMEs in Nusa Tenggara Timur 2015:
An Integration of Stochastic Frontier Analysis and Six Sigma Methodology .. 170
Erli Wijayanti Prastiwi, Sari Ayutyas, Dewi Kurnia Ayuningtyas,
Valent Gigih Saputri
Prediction Intervals of Model Averaging Methods for High – Dimensional Data 176
Septian Rahardiantoro, Khairil Anwar Notodiputro, Anang Kurnia
The Characteristic Function Property of Convoluted Random Variable from a
Variational Cauchy Distribution ... 180
Dodi Devianto
IPS05: Survey Sampling
Small Area Models for Brazilian Business Skewed Data ... 188
Fernando Moura, André Felipe Azevedo Neves, Denise Britz do Nascimento Silva
IPS06: Financial Inclusion
Measures of Financial Inclusion – A Central Bank Perspective ... 190
Bruno Tissot, Blaise Gadanecz
Financial Inclusion and the G20 Agenda ... 197
Beatrice Timmermann, Philipp Gmehling
Measuring Financial Inclusion in Malaysia ... 200
Gastão de Sousa
IPS07: Modelling And Analysis With Application To Finance And Insurance
Real-time Valuation of Large Variable Annuity Portfolios:
A Green Mesh Approach ... 220
Ken Seng Tan, Kai Liu
Arbitrage Model with Factor-Augmented Predictors and Applications to
China’s Stock Market ... 221
Xiaorong Yang
A Bayesian Quantile Regression Model for Insurance Company Costs Data .. 222
Karthik Sriram
IPS08: Recent Advances in The Analysis of Biomic Data
Network Analysis of Next-Generation Sequencing Count Data ... 224
Somnath Datta
Statistical Association Based Analysis for Genomic Data ... 225
Susmita Datta
CPS06: Statistical Theory & Methodology (1)
Bayesian Approach to Errors-in-Variables in Poisson Regression Model ... 228
Adriana Irawati Nur Ibrahim, Nur Aainaa Rozliman, Rossita Mohammad Yunus
Bayesian Accelerated Life Testing ... 234
L. Raubenheimer
Alternative Techniques of Constructing Empirical Bayes (EB) Confidence
Intervals for Hierarchical Negative Binomial Generalized Linear Model ... 239
Mohanad F. Alkhasawneh
CPS07: Survey Method (1)
Implementation of Big Data for Official Statistics in Indonesia ... 242
Stephanie Gunawan, Agni Alam Awirya, Putriana Nurman
Participation of Balinese toward Tourism Do Government and Tourism
Industries Affect Participation? ... 251
Eka N. Kencana
The Use of Passenger Exit Survey to Estimate Tourist Spending as Driver of
Regional Economy ... 258
Agni Alam Awirya, Elisabeth R. S. Y. Silitonga, Meita Elshinta Siagian
CPS09: Probability Theory & Statistical Modelling (2)
Sample Size Determination for Attaining Maximum Power under Cost
Constraints ... 266
Bhargab Chattopadhyay, Pradipta Ray
Tracing and Applying the Formula in the Equivalent Methods of Two Way
ANOVA in Nonparametric Statistics... 271
Fitri Catur Lestari
A Family of Non-Parametric Tests for Decreasing Mean Time to Failure with
Censored Data ... 278
Deemat C. Mathew, Sudheesh K. Kattumannil
CPS10: Demography & Social Welfare Statistics (1)
Empirical Study of Unemployment Disparities and Labor Market Structures at
Sub National Level in Indonesia using Spatial Panel Data Analysis, 2004-2014 286
Putu Wira Wirbuana, Chindy Saktias Pratiwi
Under-five Mortality in India: Effects of Neighbourhood Contexts with an
Application of Multilevel Cox Proportional Hazard Model ... 293
Awdhesh Yadav
Grandparents’ Co-residence and Grandchildren’s Weight Status in China ... 294
Qinying He, Xun Li, Rui Wang
Stability ... 310
Arisyi Fariza Raz, Ina Nurmalia
Prediction Based Portfolio Optimization Model Using Neural Networks with an
Emphasis on Leading Stocks of NSE ... 317
Gajendra K. Vishwakarma, Chinmoy Paul
Classifying of Companies Listed in IDX LQ45 ... 324
Maiyastri, Dodi Devianto, Efa Yonedi
IPS09: Sectoral Financial Account For Monetary Policy Making
German Households‘ Portfolio Decisions and Balance Sheet Dynamics from a
Monetary Policy Perspective ... 332
Christine Annuß
Revisions of Estimation Methods for Cash Holdings by Households and
Corporations in Japan’s Flow of Funds Accounts... 338
Sayako Konno, Naoto Osawa, Ai Teramoto
Upgrading Financial Accounts with Central Balance Sheet Data – What’s in It
for Central Banks’ Policy? ... 343
João Cadete de Matos, Lígia Maria Nunes
Development of the South African Institutional Sector Accounts... 349
Joel Mokoena, Barend de Beer
IPS10: Statistical Theory And Methods
Extracting More Value from Confidentialised Tabular Data ... 356
Jarod Y.L. Lee
Detail-preserving Unsupervised Ensemble Clustering ... 357
Siow Hoo Leong
IPS12: Recent Advances in Failure Time Data Analysis
Status of HIV Infected Patients ... 364
Amit Nirmalkar, Mohan Kale, Manisha Ghate, Ramesh Paranjape, Bharat Rewari
Return Time Distribution Based Analysis of Dengue Virus ... 369
Trupti Vaidya, Mohan Kale, Vaishali Waman, Pandurang Kolekar,
Urmila Kulkarni-Kale
The Association of Road Traffic Injuries with Injury Severity Score at
Region 11 Southern Province in Thailand ... 374
Natthika Sae-Tae, Sampurna Kakchapati, Apiradee Lim
CPS13: Macroeconomic Statistics (3)
Discrepancies in International Trade Statistics between Trading Partners ... 380
Mostafa M. Abd El-Naby
The Impact of ASEAN Economic Integration on Indonesia Foreign Direct
Investment: A Panel Gravity Model Approach... 385
Imansyah
Analysis of the Effects of Foreign Currency Supply and Demand on Rupiah
Exchange Rate ... 392
Piter Abdullah, Bayront Yudit Rumondor, Anggita Cinditya M. Kusuma,
Rahmat Eldhie Sya’banni
CPS14: Macrofinancial Statistics (2)
Robust Single-Index Model with Adjusted Beta: A Case-Study in Indonesia
Stock Exchange ... 400
Dedi Rosadi, Ezra Putranda Setiawan
Household Investment Prediction in the Stock Exchange of Thailand Using
Moving Artifical Neural Network ... 407
G. Tepvorachai, S. Siksamat, Y. R. Boonyaleephan
Application of Fuzzy Time Series Model to Forecast Indonesia
Stock Exchange (IDX) Composite... 414
Brilian Surya Budi
On the Efficiency of the Cochrane-Orcutt and Prais-Winsten
Regression for AR (1) Model ... 432
Jessa Jane D. Langoyan, Nelia S. Ereno
On the Total Least-Squares Approach to a Multivariate
Errors-in-Variables Model ... 437
Burkhard Schaffrin, Kyle Snow, Xing Fang
CPS16: Health & Social Statistics (3)
The Quadratic Effect of Life Expectancy on Economic Growth ... 444
Nazirul Hazim A. Khalim
On Modeling Transport Accident Deaths in Thailand Based on Poisson
Distributions ... 451
Nuntaporn Klinjun, Apiradee Lim, Wandee Wanishsakpong,
Khairil Anwar Notodiputro
CPS17: Demography & Social Welfare Statistics (2)
Assessing Household Welfare by Monitoring Specific Group Inflation
Rate in Jakarta ... 458
Ayu Paramudita
Dominance of the Richest in Brazilian Income Inequality Measured
with J-Divergence (1981-2015) ... 464
Marcos Dantas Hecksher, Pedro Luis do Nascimento Silva,
Carlos Henrique Leite Corseuil
A New Composite Indicator to Measure Wellbeing Index in Egypt ... 469
Mahmoud Mohamed Nagib ElSarawy
The Comparison of Three Methods Conjoint Analysis Based on Respondent
Time to Determine the Choice of Stimuli Cards ... 474
PS02: Statistical Methodology and Applications
Quantitative Risk in Commercial Banking ... 483
Agus Sudjianto
Fusion Learning: Fusing Inferences from Diverse Sources for More
Powerful Findings ... 499
Regina Y. Liu
Variable Selection Techniques for Analyzing Huge-Dimensional Datasets ... 521
Naveen Naidu Narisetty
IPS13: Payment System Data and Leading Indicators
Recent Developments in Payment Systems ... 544
Christian Dembiermont
Money Talks! Nowcasting Real Economic Activity with Payment Systems Data 545
Luís Teles Dias
Statistics to Support the Smooth Operation of Payment Systems in
the European Union ... 546
Rodrigo Oliveira-Soares, Hanna Häkkinen
Payment System Statistics to Support Policy Formulation in Indonesia ... 547
Farida Peranginangin
IPS14: Educating Students To Be Workforce-Ready Practitioners And
Users of Statistics
Preparing Engineers for Practice ... 556
Helen MacGillivray
IPS15: Time Series – Novel Methods and Applications
Applications of Distance Correlation to Time Series ... 558
Richard A. Davis, Muneya Matsua, Thomas Mikosch, Phyllis Wan
Efficiently Estimating Discrete and Continuous Time GARCH Models with
Irregularly Spaced Observations ... 559
Didit B. Nugroho, Tundjung Mahatma, Yulius Pratomo
IPS17: Input-Output Analysis
Functional Analysis of Industrial Clusters in Malaysia ... 568
Norhayati Shuja’, Yap Bee Wah
The Supply and Use Framework of National Accounts ... 575
Joerg Beutel
Processing Trade Activities: Measure and Contribution to Domestic Economy 582
Nur Adilah Hamid, Mohd Yusof Saari, Chakrin Utit, Ibrahim Kabiru Maji
CPS18: Survey Sampling & Survey Method
Adaptive Complete Allocation Sampling ... 590
Mohammad Salehi M.
Household’s Balance Sheets Survey: Indonesia Case ... 596
Widyastuti N., A.Khalim, A. Rasyid
Spatial Scan Statistics with a Restricted Likelihood Ratio for Ordinal
Outcome Data ... 597
Myeonggyun Lee, Inkyung Jung
Efficient Stratification Method for Socio - Economic Survey in Remote Areas 604
Adhi Kurniawan, Atika Nashirah Hasyyati
CPS19: Macrofinancial Statistics (3)
Prediction of Mortality Rates Using Latent Factors... 612
Chon Sern Tan, Ah Hin Pooi
The GARCH-EV-Copula Model and Simulation in Scenario Based Asset
Allocation ... 618
Gary David Sharp, Peter G.F. McEwan
Shongkour Roy, Sharif Mohammed Ismail Hossain
The Impact of Antenatal Care Program to Birth Weight on Pregnant Mother in
Indonesia: Instrumental Variable Regression Approach... 637
Choerul Umam
CPS21: Probability Theory & Statistical Modelling (3)
New Extension of Exponentiated Weibull Distribution with Properties and
Application to Survival Data ... 646
Mundher Abdullah Khaleel, Noor Akma Ibrahim
The Estimation of Parameters Spatial Autoregressive Geographically Weighted
Regression (SAR-GWR) by Means Instrumental Variable (IV) Approach ... 652
I Gede Nyoman Mindra Jaya, Budi Nurani Ruchjana, Yudhie Andriyana
Utilization of a Known Coefficient of Variation in the Linear Combination of
Normal Variance Interval Estimation Procedure ... 659
Sirima Suwan
CPS22: Health & Social Statistics (3)
Spatial and Temporal Distribution of Water Quality in Tropical Canal ... 666
Natthaphon Somching, Putri Fajriati, Piamsook Chandaravithoon
Estimating Medical Treatment Costs for Violence-related Injury in Thailand . 673
Wichayaporn Thongpeth, Emml-Benjamin Atta Owusu Mintah, Apiradee Lim
Statistical Modeling for Classification Cause of HIV Death Based on the 2005
Verbal Autopsy Data ... 679
Amornrat Chutinantakul, Don McNeil, Phattrawan Tongkumchum,
Kanitta Bundhamcharoen
IPS18: Statistics and Public Health
Statistical Methods for Public Health and Medicine ... 686
Haikady N. Nagaraja
Palash Ghosh
Hybrid Filtering Purchase Prediction Modeling: A Case Study of an Online
Healthcare Store ... 695
Hermawan Adi Budyanto, Shorful Islam, Delmiro Fernandez-Reyes,
Agus Nur Hidayat
IPS21: Goodness of Fit and Change Point Problems
Bootstrap Parameter Change Test for Location Scale Time Series Models with
Heteroscedasticity ... 704
Sangyeol Lee
An Adaptive-to-Model Test for Parametric Single-Index
Errors-in-Variables Models ... 705
Lixing Zhu, Hira Koul, Chuanlong Xie
A Data-Dependent Choice of the Tuning Parameter for Goodness-of- T Tests
Employing Bootstrapped Critical Values ... 706
Leonard Santana, J.S. Allison, W.D. Schutte
IPS22: Financial Account and Balance Sheet
The Challenges in Compilation of National and Regional Balance Sheet ... 714
Bagus Dwi Karyawan
The Use of Financial Account & Balance Sheet in Assessing Financial System
Vulnerabilities ... 715
Arlyana Abubakar
National Balance Sheet of Thailand: How to Make It Work? ... 716
Yuwawan R. Boonyaleephan, S. Siksamat
Rich Debt, Poor Debt: Assessing Household Indebtedness and Debt
Repayment Capacity ... 722
Lau Chin Ching, Sheng Ling Lim, Muhamad Shukri Abdul Rani,
Siow Zhen Shing, Siti Hanifah Borhan Nordin
Ramya Rachmawati
Optimal Design of Shewhart -Lepage Schemes and Its Application in
Monitoring Service Quality ... 739
Amitava Mukherjee
CPS24: Macroeconomic Statistics (4)
Spatial and Temporal Analysis of Tourism Arrivals and Income Distribution
Patterns in Thailand ... 742
Anuch Nuaklong, Chotirat Sriwirat, Junthip Tiengtum, Patcharin Chimdit,
Wannapisit Thammakul, Raymond J. Ritchie, Sangdao Wongsai
Tourists’ Perceptions on Safety and Security: a Case Study of Phuket Island of
Thailand ... 747
Jaruwan Manui, Sangdao Wongsai
Forecasting Foreign Tourist Arrivals to Bali Using Bayesian Vector
Autoregression ... 754
I Wayan Sumarjaya
Do FTAs Promote Trade? Evidences from ASEAN Countries’ Bilateral and
Regional FTAs ... 759
Justin Lim Ming Han
Environmental and Social Assessment of Green Growth in Cairo Governorate
(as an Indicator for the Quality of Life) ... 766
Hanan Mosad, Mostafa Mohamed Salah
CPS25: Macrofinancial Statistics (4)
Maximum Likelihood Estimation of Non-Stationary Variance ... 774
Jetsada Laipaporn, Phattrawan Tongkumchum
Financial Stability Modelling for Establishing Financial Integration in ASEAN 781
Ferdinand David Aritonang
Can Cluster Analysis Help? ... 794
Tamanna Howlader, Fatema Fazrin
Spline Interpolation for Forecasting World Tuna Catches ... 801
Boonmee Lee, Don McNeil: Apiradee Lim
Short & Long Term Relationships among the Prices of East Kalimantan
Fresh Fruit Bunches, CPO & World Crude Oil Price ... 808
Sri Wahyuningsih, Abdussamad, Memi Nor Hayati
CPS27: Demography & Social Welfare Statistics (4)
Heteroscedasticity in Grouped Data: a Case of Infant Mortality
Data in Indonesia ... 810
Ray Sastri, Khairil Anwar Notodiputro
Trends of Age-Specific Mortality Rates for Female in 54 of the World’s
Most Populous Countries ... 815
Nirmal Gautam, Apiradee Lim, Attachai Ueranantasun, Metta Kuning
Statistical Modeling of Mortality and Morbidity among the Victims of Bhopal
Gas Disaster ... 822
Akanksha S. Kashikar
Prevention of Demographic Disaster with Awareness of National
Transfer Account ... 827
Yulia Mardani, Lilia Endriana, Anisa Nuraini
IPS19: Statistical Modelling, Risk Analysis and Risk Assessment
Port Value-at-Risk Estimation through Generalized Means... 832
M. Ivette Gomes, Fernanda Figueiredo, Lígia Henriques-Rodrigues
Models and Applications of BIB Designs in Life and Health Sciences ... 839
Teresa A. Oliveira, Amílcar Oliveira, Carla Francisco
and Illustrations ... 847
Amitava Mukherjee
IPS23: Enhancement in Monetary and Financial Statistics in The Post
Great Financial Crisis
Enhancing Global Financial Statistics After The Crisis – What is The Focus? . 850
Bruno Tissot
Ana Credit Overview and Implementation from an NCB`s Point of View ... 857
Sebastian Grünberg
Using the Zoom Lens in Banking Statistics ... 863
Jean-Marc Israël, Rodrigo Oliveira-Soares
Upgrading Monetary and Financial Statistics in the Wake of the Financial
Crisis - There’s Life Beyond Aggregate Data ... 870
Luís Teles Dias, António Jorge Silva
IPS24: Inference in Complex Method
Estimating Optimal Dynamic Treatment Regimes with Shared Decision Rules 878
Bibhas Chakraborty
High-Dimensional Variable Selection for Spatial Regression Models ... 879
Tapabrata (Taps) Maiti
Modeling Volatility of Daily Returns on Investments using Spline Functions 880
Dumaria R. Tampubolon, Don McNeil
IPS26: Health Statistics For The Wellbeing Of Nation
Recognizing the Importance of Private Hospitals in Elevating Malaysia’s
Health Care Industry ... 882
Sayeeda Kamaruddin
Determinants of the Status of Completeness of Basic Immunization in
Children Age 12-59 Months in Aceh Province 2015 (The Aplication of Ordinal
Logistic Regression Analysis) ... 883
Khairul Aidah Samah
CPS28: Sharia Financial and Economic Statistics
Corporate Demand Survey on Musyarakah and Mudarabah Financing
in Malaysia ... 892
Hamim Syahrum Ahmad Mohktar, Zuraeda Ibrahim, Zafiruddin Baharum,
Shariza Abdul Ghani, Azren Rizuani Aziz
Comparing the Technical Efficiency of Leading Baitul Maal wat Tamwil and
Conventional Cooperatives in Indonesia ... 899
Ascarya
Friday, 24 March 2017
IPS27: Advances in Statistical Process Control Scheme For Risk-Free Monitoring
Control Charts for Attribute Control Based on Life Distributions with
Applications ... 908
Am´ılcar Oliveira, Teresa A. Oliveira
Control Charts for Simultaneous Monitoring of Unknown Parameters of a
Shifted Exponential Distribution ... 909
Zhi Lin Chong, Amitava Mukherjee
IPS28: Financial Modelling and Analytics
Prediction of Personal Bankruptcy Using Data Mining Techniques ... 912
Yap Bee Wah, Sharifah Heryati Syed Nor, Shafinar Ismail
IPS29: Government Finance Statistics
Comparative Study of Government Finance Statistics Compilation and
Utilization in Indonesia, Malaysia and Australia ... 914
Dr. Mei Ling
CPS29: Demography & Social Welfare Statistics (5)
A Panel Data Analysis of the Role of Human Development Index in Poverty
Reduction in Papua 2010 – 2015 ... 929
Faisal Arief, Erli Wijayanti Prastiwi
CPS30: Macrofinancial Statistics (5)
Implementation of Cox Proportional Hazard in Discontinuities Payment at
Risk Management of Insurance Premiums ... 936
Jazi Munjazi, Farhan Trunna Mahadika, Danardono, Danang Teguh Qoyyimi
Economic Policy Uncertainty and Financial Market Volatility: Evidence
from Japan ... 942
Takayuki Morimoto
CPS31: Macroeconomic Statistics (6)
Nowcasting Household Consumption and Investment ... 948
Tarsidin, Idham, Robbi Nur Rakhman
Modelling Regional Economic Growth in East Java Province 2009-2014
Using Spatial Panel Regression Model ... 955
Ahmad Thoifur, Erni Tri Astuti
Estimation of Environmental Kuznets Curve for CO
2Emissions and Methane
Emissions: Empirical Analysis for Indonesia ... 961
Debita Tejo Saputri, Budiasih
Growth Diagnostic: Evidence of Bali Province ... 968
Putriana Nurman, Ganis Arimurti, Umran Usman, Donni Fajar Anugrah,
Robbi Nurrakhman, Evy Marya Deswita
CPS32: Statistical Computing & Technology
Simulation Study Multistage Clustering for Classify Stationary, Trend, and
Seasonal Time Series Data Based on Autocorrelation Distance with
Hierarchical Algorithm ... 970
Mohammad Alfan Alfian Riyadia, Aldho Riski Irawana, Dian Sukma Pratiwia,
Kartika Fithriasaria
Sisa Pazi, Chantelle Clohessy , Gary Sharp
Support Vector Machines with Adaptive Fruit Fly Optimization Algorithm
Based on Velocity Variable (VFOA) for Classifying High Dimensional Data .. 981
Mukhlis, Bony Parulian Josaphat
CPS33: Environmental & Natural Resources Statistics (2)
Statistical Modeling for Wind Direction and Velocity in Pattani, Thailand ... 988
Marzukee Mayeng, Nittaya McNeil, Somporn Chuai-aree
Comparison of Temperatures between Bureau of Meteorology (BOM) and
Moderate Resolution Imaging Spectroradiometer (MODIS) ... 994
Suree Chooprateep, Wandee Wanishsakpong
A Study of Temperature Changes and Patterns in Australia Based on Cluster
Analysis ... 1000
Wandee Wanishsakpong, Khairil Anwar Notodiputro
Modeling of Temperature Patterns around Kathmandu Valley of Nepal from
2000 to 2016 ... 1003
Ira Sharma, Phattrawan Tongkumchum, Attachai Ueranantasun
Combined Wavelet Fuzzy Logic (WFL) to Predict Drought Events in
Indonesia Using Reanalysis Dataset ... 1008
Heri Kuwanto, Dinni A. R., Taufanie, Dedy D. Prasetyo
CPS34: Demography & Social Welfare (6)
Pattern of Utilization of Antenatal Care in Nepal (2001-2015) ... 1016
Jonu Pakhrin Tamang, Nittaya McNeil, Phattrawan Tongkumchum,
Sampurna Kakchapati
Direct and Indirect Effect of Urbanization, Unemployment, Poverty, and
Absorbtion of Non-Agricultural Labor Force on the City Development
Performance ... 1022
Muhammad Arief Eko Pratama
The Comparison of Respondent Confidence in the Stimulation Card
Evaluation on Three Methods in the Conjoint Analysis ... 1035
Fitri Catur Lestari
PS03: Emerging Challenges In Data Collection, Survey Methodology and
Implications For Official Statistics
Modernised Business Process and Some Challenges in BPS Statistics Indonesia 1045
Heru Margono
Emerging Challenges in Data Collection, Survey Methodology and
Implications for Official Statistics ... 1059
Norhayati Shuja’
Emerging Challenges in Data Collection, Survey Methodology, and Implication
for Official Statistics: Banking Sector Economist’s Needs and Perspectives ... 1071
Anton Gunawan
Closing Remarks... 1085
Index ... 1087
Welcoming Remarks
Dr. Sugeng
Deputy Governor, Bank Indonesia
Welcome Address
at the International Statistics Institute-Regional Statistics Conference
(ISI-RSC) 2017
hosted by the International Statistical Institute (ISI) and its South East Asia Regional Network in collaboration with Bank Indonesia (BI),
Statistics Indonesia (Badan Pusat Statistik/ BPS) and Ikatan Statistisi Indonesia (ISI)
22 March 2017, Bali, Indonesia
“Enhancing Statistics Prospering Human Life”
Yang kami hormati Bapak Prof. Boediono, Vice President of Republic of Indonesia in 2009
to 2014;
President of the International Statistical Institute, Mr. Pedro Luis do Nascimento Silva;
Former Deputy Governor of Reserve Bank of Australia and ANU Professor, Prof. Stephen
A. Grenville;
BIS Chief Representatives for Asia and the Pacific, Mr. Eli Remolona;
Distinguished resource persons, ladies and gentlemen,
1.
It is my great honor and delight to welcome all of you to the International Statistics
Institute – Regional Statistics Conference on “Enhancing Statistics Prospering Human
Life”. I would like to particularly extend a warm welcome to my honorable colleagues,
distinguished speakers and guests from around the world who have travelled a long way
to be with us today.
2. This conference is a joint collaboration between International Statistical Institute
(ISI) and its South East Asia Regional Network with Bank Indonesia (BI), Statistics
Indonesia (Badan Pusat Statistik/ BPS) and Ikatan Statistisi Indonesia (ISI).
3.
I do appreciate the great enthusiasm from all participants, ranging from policy makers,
economists, statisticians, scholars, practicioners and also students to attend this
important conference that will discuss a broad set of statistical issues of interest not
only to the central banks, but also to a broader usage in the economy. We have here
we undertake statistics enhancement to attain prosperous human life?
6. In a finite world, under conventional wisdom, human prosperity in economic terms
calls for continuing economic growth as the means to deliver it. Continuing growth
means that it needs to be sustainable. Thus, statistics enhancement should touch upon
all aspects pertaining to sustainable economic growth, as a means to attain human
prosperity. This basic understanding is essential for policy makers, academia, economist,
analysts as well as statistician to understand the statistics that reflects an amalgam of
influences to the prospering human life.
Distinguished speakers and participants,
7.
As we may recall, the year 2016 have brought about times of prolonged uncertainties.
The global economy showed resilience but with sub-par economic growth at only 3.8%.
In 2017, growth is set to pick up better compared to last year despite such optimism
are still centered over a considerable amount of uncertainty. Indonesia recorded a 5.02%
growth in 2016 and is expected to reach 5.2% in 2017 with all three major rating
agencies now having a positive outlook.
8.
In support of attaining sustainable economic growth, this conference shall discuss several
aspects of statistics related to macro-financial statistics, which consist of three upmost
important aspects that are statistics for central banks, sharia eonomic & finance and
financial statistics. Other topics for discussion include statistical theories and official
statistics.
9. The dynamic nature of macroeconomic and financial world undoubtedly demands
reliable data and information of various economic events and indicators.
Macro-Financial Statistics aids in the formulation of monetary policy and macro prudential
measures. Moreover, a robust central banking statistics is critical to support policy
analysis. This includes to understand the interaction between financial market conditions
and economic activity, the identification and measurement of spill-over risks in financial
sector activities as well as the interaction between monetary policy, financial stability
and the payment system.
9. Another approach for sustainable economic growth is founded through Islamic Finance.
Islamic Finance have been part of the global financial system that has developed rapidly
in the last two decades. In short, the basic principles of Islamic finance have social
and environmental goals that are drivers to sustainable economic development and
enhancing resiliency of the financial sector. Therefore, to support the advancement
of sharia finance and economy, statistically robust indicators must be available. This
calls the need for speeding up the development of the statistics for sharia financial and
economic as part of our dynamic economy.
10. To attain the betterment human life, official statistics should also be enhanced. The
most important issue with regards to official statistics is its accuracy. Official statistics
useful once it is put into concrete actions. We believe that a successful enhancement of
these many areas of statistics will help us to effectively and efficiently address the critical
focus in uplifting the economic progress and at the end, the quality of life.
13. In closing, let me offer my special thanks to all the distinguished speakers for taking
part in this important endeavor and for sharing with us your expertise. All of us here
very much look forward to your contributions.
To all conference participants, I hope your active involvement to have productive
discussions, as I have already noticed there are many experts present among us. Thank
you for being here.
Last but not least, I wish you a fruitful conference. And don’t miss your chances here to
also explore this beautiful island of Gods. Have a wonderful and enjoyable stay in Bali.
Thank you.
Information and Statistics in Public Policy Making
Boediono
Statistics for Banking and Finance
Stephen Grenville
The New Cross-Border Finance in Asia
Eli Remolona
Plenary Seasson 01 (PS01):
Banking and Finance
BIConfBali22317.XX
INFORMATION AND STATISTICS IN PUBLIC POLICY
MAKING
ΨBoediono
Let me thank the organizers for inviting me to this important
conference. To all participants, welcome to Bali. Before
proceeding, though, I feel obliged to offer my reason why I,
being a non-statistician, might usefully speak in the forum of
professional statisticians such as this.
While I am not a statistician by profession, statistics have
never been far from my works throughout most of my career. I
took up my first job in government more than thirty years ago
at our national planning agency - Bappenas – to lead the
Bureau for Economics and Statistics. As the bureau’s name
suggests my main responsibility was to supply the institution's
needs of data – especially economic data - for the planning
process. It did not itself collect data but instead relied on other
more formidable data collecting agencies. Conveniently as it
turned out, I was also given the responsibility of overseeing the
programs and the annual budget of our national statistical
office - BPS.
In the subsequent years, as I increasingly took up the decision
making responsibility, my role invariably shifted from
facilitating the production of statistics to one of a principal
user of statistics. So I thought it might be useful to share in this
forum how we, the users of statistics and information in
BIConfBali22317.XX
INFORMATION AND STATISTICS IN PUBLIC POLICY
MAKING
ΨBoediono
Let me thank the organizers for inviting me to this important
conference. To all participants, welcome to Bali. Before
proceeding, though, I feel obliged to offer my reason why I,
being a non-statistician, might usefully speak in the forum of
professional statisticians such as this.
While I am not a statistician by profession, statistics have
never been far from my works throughout most of my career. I
took up my first job in government more than thirty years ago
at our national planning agency - Bappenas – to lead the
Bureau for Economics and Statistics. As the bureau’s name
suggests my main responsibility was to supply the institution's
needs of data – especially economic data - for the planning
process. It did not itself collect data but instead relied on other
more formidable data collecting agencies. Conveniently as it
turned out, I was also given the responsibility of overseeing the
programs and the annual budget of our national statistical
office - BPS.
In the subsequent years, as I increasingly took up the decision
making responsibility, my role invariably shifted from
facilitating the production of statistics to one of a principal
user of statistics. So I thought it might be useful to share in this
forum how we, the users of statistics and information in
general, see their role in policy decision making in government.
Let me start with the ideal information situation in which any
policy maker would love to be whenever he/she has to make
decision: all the relevant data with unquestionable accuracy
are available in real time at his/her finger tip. Alas, that ideal
situation is never to be. Even in the best of circumstances the
hard reality is that, information wise, policy makers are always
'behind the curve'.
Why? The main reason why it is so is that a policy maker is
always bound by a time table. At a particular juncture he or
she has to come up with a decision on what actions to take on
the basis of the 'best' information available to him or her at
that critical time, which most probably are neither complete
nor very accurate. Very often to get that 'best' information his
or her team have to scramble to assemble data from different
sources, in and outside the bureaucracy. The assembled
information consists of data with differing completeness and
quality, a kind of “information salad’ or ‘information soup’. The
policy maker has to make the best use of it and make a
decision.
To be fair to the statisticians and other data producers, I must
add that in reality the problems of policy making do not come
only from the 'supply side' or the availability and quality of
information. Very serious problems could in fact occur on the
'demand side' or the way the available data are being used.
The 'cook', if I could metaphorically call the supporting team
who are tasked to process and analyze the assembled
information and present actionable options, for weak technical
expertise or lack of sound judgment, may not do a good job.
The options are then flawed or misleading. Once such options
find their way to the decision maker it is hard to expect a
the decision maker happens to be a supremely wise and
extremely knowledgeable person. A rarity indeed.
Important as they are, I will not dwell further on the ’demand
side’ problems. My comments that follow will be largely on the
‘supply side’ ones. Inevitably, my Indonesian experience will
influence my story. And I will remain focus on public policy
making.
Let me underscore that policy decision making is essentially a
multistage input-output process. The quality of the resulting
policy is the sum total of the qualities of all those inputs and
outputs along the information chain. To improve the quality of
the end product – the final policy outcome - therefore one must
look into the possibility of improving the quality of the output
of each related institution along that chain.
To begin, we should recognize the fact that in formulating
policies, national governments rely mostly on information
generated within and by its own institutions. The national
statistical office usually stands out as its principal source for
basic economic and social information. In this country three
other institutions deserve special mention. The central bank is
the sole source of monetary statistics, finance ministry for
fiscal statistics and the financial services authority for data on
banking and other financial instititutions. These four
institutions are the first-tier information providers for policy
making.
Certain other institutions also collect data related to their
respective functions but with more limited coverage and
the decision maker happens to be a supremely wise and
extremely knowledgeable person. A rarity indeed.
Important as they are, I will not dwell further on the ’demand
side’ problems. My comments that follow will be largely on the
‘supply side’ ones. Inevitably, my Indonesian experience will
influence my story. And I will remain focus on public policy
making.
Let me underscore that policy decision making is essentially a
multistage input-output process. The quality of the resulting
policy is the sum total of the qualities of all those inputs and
outputs along the information chain. To improve the quality of
the end product – the final policy outcome - therefore one must
look into the possibility of improving the quality of the output
of each related institution along that chain.
To begin, we should recognize the fact that in formulating
policies, national governments rely mostly on information
generated within and by its own institutions. The national
statistical office usually stands out as its principal source for
basic economic and social information. In this country three
other institutions deserve special mention. The central bank is
the sole source of monetary statistics, finance ministry for
fiscal statistics and the financial services authority for data on
banking and other financial instititutions. These four
institutions are the first-tier information providers for policy
making.
Certain other institutions also collect data related to their
respective functions but with more limited coverage and
generally of lesser quality. They are the second-tier
information providers. To name a few: ministry of home
of agriculture for agriculture-related statistics, ministry of
industry for industrial production statistics, ministry of
transports for air, sea and land transport capacities and
traffics, ministry of public works for the state of road and
irrigation systems.
The quality of the information vary greatly across institutions,
notably among the second-tier ones. It shows the differing
capacities in their information gathering and processing. But
actually it reflects a deeper and more general problem - a lack
of appreciation of the critical role of good information in
making good decision. In today’s world it is generally accepted
that accumulated institutional knowledge and effective
information system are the foundation of a “smart” institution
(and hence smart policies). It seems though that such a view
has not caught on in many government institutions. It is one of
the fundamental challenges of a country's bureaucratic
reformers.
The potential of improving information capability in the
institutions I mentioned earlier is substantial. There are still
enough rooms for raising the operational standards of even the
first-tier institutions to the international best practices. And
clearly there are plenty of rooms to level up the information
capability of those second-tier agencies through redefining
information gathering function in each of them, providing
sufficient number of qualified personnel and securing adequate
budget for them. To be sure partial efforts have been made
along this line. But to make them stick the initiatives must be
substantively incorporated in their respective long term
reforms agenda. Better still if they are made to be an integral
part of a broader plan for national bureacratic reform.
Systematic efforts along this line in my view will give the
Recently I have been trying to follow the lively discussions
among statisticians and data scientists on the potential
benefits of using privately collected “big data” in improving the
operations in both government and business. If we believe
that the key to national progress is better public policies and
better business conduct, then we must take the issue seriously.
For a non specialist like myself, though, it is too complex an
issue to jump in. So let me make only some general comments
on it.
The first point I wish to make is that not only the private sector
but the government could also be a producer of big data. There
are many routine government processes at the national and
subnational levels that could generate continuous streams of
large scale and uptodate information. If digitized, they could
become invaluable big data systems. Raising the standards of
the digital technology usage and practices in government
agencies would directly improve their 'traditional' activities in
information gathering and processing while indirectly also
raise the probability of success of any planned government’s
cooperation schemes with the private sector in utilizing other
big data systems. Digitizing government’s administration
processes will give even larger payoffs as it helps raise the
efficiency and integrity of the day-to-day operations of the
bureaucracy.
This is a big, long term job with many challenges. Some of them
may spring up at the very beginning. Thus a common problem
is that the existing IT systems of government agencies are not
compatible one another. Let me relate a story. I once was
easily reshaped and reoriented. The reason, though, is not so
much technological as institutional, namely bureaucratic
inertia or resistance toward change. The important lesson
from the case study was that getting a firm hold of their IT
budgets was the minimum requirement. You need more than
that. You must have some reserve energy for breaking many
forms of institutional inertia and resistance. One form that we
found particularly difficult to deal with has a root in the
so-called ‘vendor driven’ planning practices. By the end of its
term the task force at best registered only a partial success.
Nevertheless I would reiterate that digitizing government
processes and developing government-owned ‘big data’ is a
truly worthwhile effort and should be redoubled in the future.
There is a big promise from the possibility of utilizing
non-government big data which recently have grown exponentially
as a result of the ever expanding digitization of ordinary social
and economic processes. We are told that currently we are still
at the beginning of a long process. If the government could tap
these enormous sources of information, the quality of its
administrative and policy decisions could be vastly improved
with far less costs, and the society stands to gain.
These new sources of information are useful for strengthening
and sharpening the 'traditional' policies. For instance, they
potentially will make obsolete surveys such as those on
consumers' confidence, investors' confidence and employment
situation. Such surveys are essential for calibrating
macroeconomic policy stance. Eventually they will be replaced
by direct and real-time readings of the relevant big data. There
are other instances, such as in health, education, poverty
alleviation and transportation where the use of big data offers
entirely new policy perspectives and possibilities.
The use of privately collected information by government
involves a combination of the use of compulsion and
voluntarism. Government can issue regulations compelling the
private parties to share their information with the government.
But in democracy and market economy there are political and
economic limits to the application of the coercive power of the
state. When the limits are reached we will have to rely on
voluntary cooperation agreements between the government
and the private parties. Such 'public-private partnership' in
information sharing is essential but may not be easy to come
by, especially in the newly digitized social and economic
processes.
For the traditionally highly regulated sectors such as the
financial sector, voluntary cooperations mean information
sharing arrangements beyond what is mandated by prevailing
prudential regulations which themselves are continually
evolving. From the regulators' and policy makers' points of
view, obviously, more, better and more timely data would be
very helpful for their routine prudential surveillance job and,
even more crucially, for policy makers in managing the fluid
situation in times of crisis. But we know that beyond certain
points compulsion becomes harmful for the efficient
operations of financial institutions and markets, and most
probably also for individual customers.
Judicious combination of regulations and cooperations is
therefore key to the success of the endeavor. And since the use
of big data for supporting policies most likely entails new
institutional arrangements, new territories and new modus
operandi, experts advice us to start with small scale
The use of privately collected information by government
involves a combination of the use of compulsion and
voluntarism. Government can issue regulations compelling the
private parties to share their information with the government.
But in democracy and market economy there are political and
economic limits to the application of the coercive power of the
state. When the limits are reached we will have to rely on
voluntary cooperation agreements between the government
and the private parties. Such 'public-private partnership' in
information sharing is essential but may not be easy to come
by, especially in the newly digitized social and economic
processes.
For the traditionally highly regulated sectors such as the
financial sector, voluntary cooperations mean information
sharing arrangements beyond what is mandated by prevailing
prudential regulations which themselves are continually
evolving. From the regulators' and policy makers' points of
view, obviously, more, better and more timely data would be
very helpful for their routine prudential surveillance job and,
even more crucially, for policy makers in managing the fluid
situation in times of crisis. But we know that beyond certain
points compulsion becomes harmful for the efficient
operations of financial institutions and markets, and most
probably also for individual customers.
Judicious combination of regulations and cooperations is
therefore key to the success of the endeavor. And since the use
of big data for supporting policies most likely entails new
institutional arrangements, new territories and new modus
operandi, experts advice us to start with small scale
experimentations then from there move on to scale them up,
only after lessons have been learnt and extracted from the
To close my talk let me summarize its main points.
• The quality of policy making is determined by the quality
of the available information and the way the available
information is being used.
• In policy making governments still rely mainly on
information generated by their own agencies. A key step
to improve the quality of policy making is therefore by
systematically raising the information producing
capability of the relevant institutions.
• Digitizing routine government processes will improve the
quality of policy making while indirectly also gives large
benefits with the improvements in the efficiency and
integrity of the government bureaucracy.
• The growth of privately collected big data opens up a new
posibility of vastly improving public policies with far less
costs. The key is how to evolve a judicious combination of
regulations and voluntary cooperation schemes. The best
way to move forward is to start with small experiments
and as lessons gained, move on to scale them up.
Thank you.
Statistics for Banking and Finance
Stephen Grenville
Lowy institute for International Policy
Bali
23 March 2017
1
Introduction
• Narrow focus: how statistics can
enhance the central bank’s policies.
• I’ll set out problems and hope you
Introduction
• Three primary roles:
–
monetary policy
–
Financial stability
–
Financial sector development
3
Introduction
• Hong Kong’s former view that policy needed
very few statistics.
• 2008 global financial crisis triggered demand
for many more statistics, especially
internationally comparable time-series.
• Of course more is better, but this is not
costless. Prioritise between domestically
oriented statistics and the demands of the
global institutions.
Introduction
• IMF SDDS
• G20 Data Gaps Initiative
• BIS Banking Statistics
5
Introduction
• Why might (say) Indonesia’s priorities differ
from global priorities?
–
Very diverse economy, so broad aggregates don’t
capture complexity.
–
Growing fast, so changing quickly
Introduction
• Speed of collection may be important (e.g.
during a crisis)
• Frequency of collection? For most policy
issues, small advantage in frequent collection.
• Trade-off between quantity and quality
7
Monetary Policy
• Starting point might be mechanical policy rule
(usually a Taylor Rule)
i = r* + a (p – p*) + b ( Y – Y*)
• Each one of these components presents
measurement problems
Monetary policy
• Interest rate. What interest rate
matters for policy?
• Natural (long-term) interest rate.
Changing over time. Close to zero in
the US now???
9
Monetary policy
• Inflation
–
Even historic rates are problematic (Boskin)
–
Inflation targeting frameworks emphasize
INFLATION EXPECTATIONS, not actual
–
Too many different series (e.g. underlying
versus headline)
Monetary policy
• Output
–
Delays in measuring actual output
–
Problems of measuring potential
output. Productivity; terms of
trade; end-point problem with
trend-based measures
11
Monetary policy
• And all this has to be
FORWARD-LOOKING
–
This involves forecasts and surveys
We are not good at forecasting
13
Monetary policy
• As well, there are non-formula factors:
–
Headwinds from business/household
confidence, bank credit conditions, political
uncertainty
• So greater reliance on surveys, with all their
problems (subjective, changeable, sensitive to
environment)
Broader macro
• Foreign capital flows. Not principally a
statistical issue: rather analytical and
structural, but there are important
behavioural differences. Need for granularity
and disaggregation. How much netting and
what does it mean? Currency mismatches?
Quality of the debt: who is hedged and with
whom?
•
15
Broader macro
• Hence FLOW of FUNDS: “from whom and to
whom”
• Plus assets/liabilities
Financial stability
• Need for better financial stability policy was
main ‘take-away’ from 2008 GFC. But main
problems were operational (inadequate
prudential supervision and forbearance) and
policy, not shortage of statistics.
17
Financial stability
• From around 2000 onwards (thanks to Borio
and Lowe etc) there was a recognition that
financial cycle differs from business cycle, but
even now we don’t yet know what
components should be in the measure of the
financial cycle (clearly not just GDP, and it is
more than credit) and how this cycle behaves.
The financial cycle
19
Financial stability
• Problems arise in sub-set of aggregates rather
than total. Aggregates don’t generally cause
crisis, but which sub-sets?
• Gross versus net
Financial stability
• Usually about TAIL RISKS. Hard to measure
and low probability, but usually not a surprise.
But what to do even if tail risk is identified:
enumeration of tail risks is unhelpful for
policy-making. A probabilistic approach?
21
Financial development
Conclusions
Priorities:
–
Inflation expectations. The heart of inflation
targeting: the anchor. How to maximise
benefit from surveys?
–
Better analysis of tail risks in financial
stability
.
–
More generally, how to handle risk.
–
Flow of funds-plus (i.e. with assets/libilities)
23
