1. Presented by: Riznaldi Akbar University of Western Australia Supervisor: Prof. Yanrui Wu, Dr. Bei Li Understanding Indonesia External Debt Crises: A Penalized Logistic Regression Approach

2. Outline 1) Motivation 2) Definition of debt crisis 3) Relevant literature 4) Model specification 5) Event study analysis 6) Principal component analysis 7) Penalized logistic regression 8) Results

3. Motivation 1). Indonesia experienced multiple debt crises (from 1998 to 2005). 2 ). Our data series are somewhat limited. There are only annual 42 observations (1970-2011)  a standard logistic regression might not apply:  The small sample sizes might lead to inaccurate estimates of parameters  Data over fitting  Separation problem. 3) A penalized logistic regression address this issue  A penalty on the log likelihood will penalize models that have large regression coefficients more heavily.

4. Definition of debt crisis 1) A large arrears on debt (principal and interest) of more than 5% of total debt stocks (Detragiache & Spilimbergo, 2001; Cohen & Valadier, 2011) 2) Debt rescheduling and/or restructuring (Detragiache & Spilimbergo, 2001; De Paoli & Saporta, 2006) 3) Missed or delayed repayment of the debt principal and interest (S&P; Moodys) 4) Country defaulted by rating agency or ask IMF concessional loan of more than 100% quota (Manasse, 2003) 5) The wide spread on foreign currency denominated government bond of more than 1000 bps over US Treasuries (Sy, 2004).

5. We will define a debt crisis as: 1) A large arrears on debt (principal and interest) of more than 5% of total debt stocks. 1) Debt rescheduling and/or restructuring.

6. Crisis episodes 19981999200020012002200320042005 Debt arrears/total debt stocks (in %) 1.8%4.3%5.3%6.5%7.3%12.6%12.9%0.1% Debt rescheduling (US$ billion) 3.895.202.462.775.073.0802.61 Debt crisis? Yes Indonesia experienced debt crises from 1998 to 2005. Indonesia either has large debt arrears of more than 5% of the total debt stock or rescheduling their debts)

7. Literature review A logistic regression model for predicting debt crisis (Ciarlone & Trebeschi, 2005; Fuertes & Kalotychou, 2007; Manasse et al., 2003). A logistic regression is one of most commonly used methods for debt crisis prediction. Easy to interpret Suit for binary outcomes. Other methods including: signal approach, decision tree, clustering, neural network, etc.

8. Model specification: Logistic regression model

9. Event study analysis  Provides some understandings about the behavior of macro and debt indicators around the time of crises  The 3 years crisis window is chosen

10. GDP growth (annual, %) EntryExit

11. Inflation (annual, %) EntryExit

12. Short term debt (% of total external debt) EntryExit

13. Central government debt (% of GDP) EntryExit

14. Public debt to GDP EntryExit

15. Principal component analysis (PCA)  Original variables: 15 covariates  Reduce number of covariates:  our data sets are limited  to have a less degree of freedom Only retain components with high correlation. Some indicators have very high factor loadings.

16. Factor loadings Variable Definition Component #1#2#3#4#5 x1 Average maturity on new external debt commitments (years) 0.919 x2 Interest payments on external debt (% of exports of goods, services and primary income) 0.953 x3 Interest payments on external debt (% of GNI) x4 Short-term debt (% of total external debt) x5 Total debt service (% of exports of goods, services and primary income) 0.97 x6 Total reserves (% of total external debt) -0.904 x7 Central government debt, total (% of GDP) 0.926 x8 Public debt (% of GDP) -0.93 x9 Exports of goods and services (% of GDP) x10 Inflation, consumer prices (annual %) 0.914 x11 GDP growth (annual %) -0.897 x12 Real interest rate (%) -0.896 x13 Domestic credit to private sector (% of GDP) x14 Money and quasi money growth (annual %) x15 Cash surplus/deficit (% of GDP) cv

17. A penalized logistic regression Why we use penalized regression? 1) Data over-fitting 2) The estimated parameters are bias and non-solution exists 3) Multicolinearity 4) Separation problem. Solution: Regularization or penalized log likelihood function (penalizes on large coefficient values).

18. Regularization (FIRTH’s estimates) -Keep all the features, but reduce magnitude or values parameter θ j.. -This method works well when we have a lot of features, each of which could contribute to predicting crisis. -We will minimize the cost function J(Θ): Regularization

19. Separation problem Crisis year 1998 - 2005 Crisis year 1998 - 2005 Notes: With public debt to GDP > 50%, this indicator could perfectly predict the occurrence of the debt crisis. 50%

20. Result: Standard logistic regression DatasetsResult 15 variablesSeparation problem occurred (x8* predicts data perfectly) Principal Components: - 1 component C1 is not significant - 2 components C1, C2 are not significant - 3 components Separation occurred (C3** predict data perfectly) - 4 components Separation occurred (C3** predict data perfectly) - 5 components Separation occurred (C3** predict data perfectly) Notes: *x8 is the ratio of public debt to GDP. **C3 is mainly explained by the public debt to GDP.

21. Result: a penalized logistic regression Data sets Odd ratio Remarks 15 variables - No variables are significant Principal Components: - - 1 component - C1 is not significant - 2 components - C1 and C2 are not significant - 3 components 0.78 (C3) C3 is significant - 4 components 0.80 (C3) C3 is significant - 5 components 0.81 (C3) C3 is significant

22. Conclusion 1) Some indicators have high predictive power in predicting debt crisis in Indonesia especially public debt to GDP, GDP growth and inflation. 2) A standard logistic regression is not preferable method for the debt crisis prediction with small sample sizes. Our study shows separation problem and over-fitting exists. 3) The penalized regression shows that the ratio of public debt to GDP is the most significant indicator to predict debt crisis in Indonesia.

23. Thank you