London, Microsimulation in decision support The latest news about our results József Csicsman

Contents Introduction Microsimulation Research Group in BUTE Formal presentation at IFIP WS-s Microsimulation theory Microsimulation in practice Research data sets from 2004 Problems of modelling demographic changes Application in Student Loan forecast Applications in bank sector and telcos

Introduction

Microsimulation research group at the Information and Knowledge Management Department of Budapest University of Technology and Economics was founded in Cooperation with the Hungarian Central Statistical Office (KSH) International cooperation (EU) Custom economic applications Students graduates with practical SAS knowledge (more than 50 former students work in the field of SAS application in financial sector) Calculus and BUTE cooperation

Microsimulation Research Group Models simulate large representative populations of these low-level entities (using probabilities, laws, rules or empirical facts) : Common development group for the technical background of Microsimulation Real applications from 2003

Formal Presentation in IFIP WS-s Microsimulation Service System, Statistical Matching presented by Péter Baranyai in Budapest Microsimulation Servise System based on SAS and Application of Microsimulation in Decision Support presented by Balázs Látó in Cork

Microsimulation

Microsimulation has been used for decades in economics and other areas. The microsimulation procedure examines social and economic changes by assessing the effect of each provision with small units and the description of the overall effects is derived from these assessments. It has essential role in decision support.

Workflow of Microsimulation

Microsimulation Service System

Statistical Matching A new function of the Microsimulation Service System How to merge the records of two (or more) datasets having no key variable Based on statistical analysis, and distribution of other variables Example: Simulation of marriages Replacement of missing or corrupt data from other surveys

Application possibilities Demographic, social and economic impacts of various measures Improving the quality of statistical surveys Aging of datasets (bringing data of former surveys up-to- date) More accurate forecast of probable events International comparisons (competitiveness, tax and subsidy systems…)

Research data sets from 2004

Microsensus at HCSO, 2004 and recording income data correction of data with Microsimulation Service System of Calculus Household statistic survey at HCSO, 2004 Creation of research data set with statistical matching relatively good data about consumption and income

Problems of modelling demographic changes

We cannot use weighted data for demographical simulation (because of small sample size) Multiplication to the complete population Marriage and devorce simulation models the most complicated method

Problems of modelling demographic changes Birth and death simulation models Migration in Hungary is too big (the hungarian population hasn’t decreased, however, birth rate is too small and death rate is higher than other European countries)

Population, vital events in Hungary Denomination Population, 1 January male female Number of females per thousand males Density per km ² Marriges number Divorces number Live births number Death number Natural increase, decrease ( – ) number

Application in Student Loan forecast

Student Loan forecast Hardly predictable number of persons who require Student Loan Simulation of demographical changes till 2007 Merging real student loan data with simulated population Simulation till 2010

Student Loan forecast Estimation of paid and unpaid loans problems of high level intrest rate in Hungary pay-backs are too frequent, thus traditional bank estimations aren’t usable

Applications in Bank Sector

Applications in Bank sector As we discussed before: Conducting stress test analysis and creating reports Replacement of missing data merging simulated research data sets with real client data Predicting success of new business products Support for credit scoring

Applications in Bank sector Income is not an efficient enough indicator in Hungarian bank sector Income isn’t considered as determinant information about the financial background of individuals Consumption is closely connected with financial background, thus it provides more relevant information

Applications in Bank sector Take changes of consumption, income, etc. into consideraion data used for modelling had been registered in different terms problem with compatibility applying microsimulation in order to age data

Stress test Examines the probability and possible effects of unforseen events Stress test for Hungarian banks (2006) BASEL2 mandatory for banks concentrating on the extreme values of unexpected events increased inflation unemployment and exchange rate the drastic effect of these matters on the credit system

Stress test Done on the research dataset The test examines the possible outcomes and effects of an unforeseen event Conditions: Exchange reate of CHF rises from 160 HUF to 200 HUF Those who have an amortization instalment greater than their income’s 30%: Failure rate in the income decimals % % % Another 3% cannot make repayments because of the rise in unemployment

Applications in Telcos Replace missing demographic data by using statistical matching Correction of corrupt marketing survey data Forecast of marketing strategies aimed at avoiding attrition Fraud protection

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