A Global Macroeconomic Forecasting Model for the Philippines

1. A Global Macroeconomic Forecasting Model for the Philippines RupertoMajuca, Ph.D (Illinois), J.D. De La Salle University, Manila 51st Philippine Economic Society Annual Meeting November 2013 (Makati City, Philippines)

2. Outline • Introduction • The Model’s Stochastic Equations • Estimation Methods • Estimation Results • Summary of Findings and Conclusions

3. Table of Contents • Introduction • The Model’s Stochastic Equations • Estimation Methods • Estimation Results • Summary of Findings and Conclusions

4. Motivating Questions • How does a slowdown in U.S. or a U.S. debt default affect PH economy, directly & indirectly via effects on EU, China, Japan, ASEAN? • How does a debt crisis in EU, or China slowdown, affect U.S., China, Japan, ASEAN, and PH directly & indirectly? • What has greater impact on PH, shocks from the U.S., EU, China, Japan, ASEAN, or its own shocks? • What are the ripple effects of the shocks to Philippine GDP, unemployment, inflation, interest rates, exchange rates, etc.?

5. Research Interests • Economic & financial linkages PH with ASEAN, U.S., EU, China, Japan, & those economies’ linkages with each other • Transmission of shocks from U.S., E.U., Japan, and China to ASEAN, & PH • Quantifying the ripple effects to ASEAN & AMSs’ GDP growth, inflation, interest rates, exchange rate, & unemployment • Implications for policy and macroeconomic management

6. Research Methodology, 1 • Traditional PH models (equation-by-equation OLS, ECM) • NEDA QMM • PIDS • Ateneo (AMFM), others • Simultaneity bias, exogeneity issue • Estimates are biased and inconsistent • Increasing sample cannot cure bias in estimates • Lucas (1976) critique • Coefficient estimates are not policy invariant • Lucas: conclusions and policy advice based on these models are invalid and misleading

7. Research Methodology, 2 • Post Lucas critique. Now standard: modern, dynamic quantitative economics • Dynamic stochastic general equilibrium (DSGE models) • Global projection models • Utilizes state of the art: Bayesian methods • This work: global projection model to analyze interplay of key macroeconomic variables across countries/regions • U.S., E.U., Japan, China, ASEAN, PH • GDP growth, inflation, interest rates, exchange rate, unemployment

8. The Global Projection Model • Designed to capture cross-regions and cross-country macroeconomic linkages (e.g., US, EU, Japan, China, ASEAN, AMS) • Traces cross-border ripple effects of key macroeconomic variables (GDP growth, inflation, interest rates, exchange rates, unemployment) • Bayesian estimation techniques • Priors plus Bayesian updating via Kalman filter; Markov Chain Monte Carlo

9. Table of Contents • Introduction • The Model’s Stochastic Equations • Estimation Methods • Estimation Results • Summary of Findings and Conclusions

10. GPM Stochastic Equations, 1 Potential Output • NAIRU Equilibrium Real Interest Rate

11. GPM StochasticEquations, 2 Equilibrium Real Exchange Rate

12. GPM Stochastic Equations, 3 Output Gap (Aggregate Demand / IS Curve) • Inflation (New Keynesian Phillips Curve)

13. GPM Stochastic Equations, 4 • Uncovered Interest Parity • (Bilateral Real Exchange Rate) Policy Interest Rate (Taylor Type Rule) • Unemployment Rate

14. GPM Stochastic Equations, 5 Equations Incorporating BLT_US

15. Table of Contents • Introduction • The Model’s Stochastic Equations • Estimation Methods • Estimation Results • Summary of Findings and Conclusions

16. Bayesian Estimation • Mixture between classical estimation and calibration of macro models • Puts some weight on the priors and some weight on the data • Combine prior and MLE estimation via Kalman filter • Recover posterior distribution via MCMC (Metropolis Hastings)

17. Estimation Strategy • Start with GPM4 (US, EU, Japan, China); estimate coefficients • Proceed with GPM5 (US, EU, Japan, China + ASEAN), fixing coefficient for GPM4. Assumes ASEAN doesn’t change GPM4 coefficients • Then proceed with GPM6 (US, EU, Japan, China, ASEAN + Philippines), mutatis mutandis • 250,000 MH draws each stage; first 30% used as burn-in

18. Data Requirements • Consumer price index • Real gross domestic product • Nominal interest rate • Nominal exchange rate • Unemployment rate • Bank lending variable for US • CPI, GDP and ER are in logs

19. Table of Contents • Introduction • The Model’s Stochastic Equations • Estimation Methods • Estimation Results • Summary of Findings and Conclusions

20. Estimation Results: GPM5 Parameters

21. Estimation Results: GPM5 S.D. of Structural Shocks

22. Estimation Results: GPM6 Parameters, 1

23. Estimation Results: GPM6 Parameters, 2

24. Estimation Results: GPM6 S.D. of Structural Shocks

25. Impulse Responses:Shock to U.S. Output Gap

26. Impulse Responses:Shock to U.S. Output Gap

27. Impulse Responses:Shock to Euro Area Output Gap

28. Impulse Responses:Shock to Euro Area Output Gap

29. Impulse Responses:Shock to Japan Output Gap

30. Impulse Responses:Shock to Japan Output Gap

31. Impulse Responses:Shock to China Output Gap

32. Impulse Responses:Shock to China Output Gap

33. Impulse Responses:Shock to U.S. Output Gap

34. Impulse Responses:Shock to Euro Area Output Gap

35. Impulse Responses:Shock to U.S. Output Gap

36. Impulse Responses:Shock to Euro Area Output Gap

37. Impulse Responses:Shock to Japan Output Gap

38. Impulse Responses:Shock to China Output Gap

39. Impulse Responses:Shock to ASEAN Output Gap

40. Impulse Responses:Shock to Philippine Output Gap

41. Impulse Responses:Shock to Philippine Output Gap

42. Table of Contents • Introduction • The Model’s Stochastic Equations • Estimation Methods • Estimation Results • Summary of Findings and Conclusions

43. Findings and Conclusions, 1 • Existing PH models (NEDA QMM, PIDS, AMFM, etc.) using equation-by-equation OLS, ECM) • Simultaneity bias/inconsistency issue • Lucas (1976): coefficients not policy invariant; conclusions & policy advice are invalid and misleading • Now standard: modern, dynamic quantitative economics (utilizing Bayesian methods) • Dynamic stochastic general equilibrium (DSGE models) • Global projection models

44. Findings and Conclusions, 2 • This work: cross-region ripple effects to key macro variables (GDP growth, inflation, unemployment, etc.) traced via GPM • Greatest influence on ASEAN macroeconomic variables come from ASEAN’s own internal shocks; followed by shocks from U.S., China, Japan, then Euro area, in that order • ASEAN own AD shocks’ impact on ASEAN GDP, 0.4 ppt; US AD impact (peaks after 5 or 6 quarters), about 1/7 of ASEAN impact; China AD shock, about 1/9 ASEAN’s; Japan, 1/10; EU, 1/11

45. Findings and Conclusions, 3 • For AMS like PH, domestic shocks also capture much of the influence on own macroeconomic variables. In the case of PH, this is followed by shocks from the U.S., Japan and China, then ASEAN and Euro area. • PH AD shock’s impact to PH GDP, about 0.5 percent; US shock’s impact (peaks after about 5 or 6 quarters), about 1/7 of PH shock’s impact; Japan and China, about 1/10; ASEAN and EU, about 1/17.

46. Findings and Conclusions, 4 • Impulse responses of PH macro variables • Shock to domestic AD results in 0.5% increase on PH real GDP on impact; positive impact persists for more than 2 years • Results in decrease in umeployment (lasts for ~ 3 years before returning to steady state) • Demand pull increase in inflation • Appreciation in currency • BSP increase policy rates via Taylor-type reaction function

47. Thank You!