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A Global Macroeconomic
Forecasting Model
for the Philippines
Ruperto Majuca, Ph.D (Illinois), J.D.
De La Salle University, Manila
51st Philippine Economic Society Annual Meeting
November 2013 (Makati City, Philippines)
Outline
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Introduction
The Model’s Stochastic Equations
Estimation Methods
Estimation Results
Summary of Findings and Conclusions
Table of Contents

Introduction
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The Model’s Stochastic Equations
Estimation Methods
Estimation Results
Summary of Findings and Conclusions
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.?
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
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
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
The Global Projection Model
Designed to capture cross-regions and crosscountry 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

Table of Contents
 Introduction
 The Model’s Stochastic Equations
 Estimation Methods
 Estimation Results
 Summary of Findings and Conclusions
GPM Stochastic Equations, 1
Potential Output
NAIRU
Equilibrium Real Interest Rate
𝑟𝑖,𝑡 = 𝑅𝑖,𝑡 − 𝑅𝑖,𝑡
GPM Stochastic Equations, 2
Equilibrium Real Exchange Rate
Δ𝑍𝑖,𝑡 = 100Δ log 𝑆𝑖,𝑡 − (𝜋𝑖,𝑡 − 𝜋𝑢𝑠 ,𝑡 )/4
GPM Stochastic Equations, 3
Output Gap (Aggregate Demand / IS Curve)
Inflation (New Keynesian Phillips Curve)
GPM Stochastic Equations, 4
Uncovered Interest Parity
(Bilateral Real Exchange Rate)
Policy Interest Rate (Taylor Type Rule)
Unemployment Rate
GPM Stochastic Equations, 5
Equations Incorporating BLT_US
Table of Contents
 Introduction
 The Model’s Stochastic Equations
 Estimation Methods
 Estimation Results
 Summary of Findings and Conclusions
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)

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
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

Table of Contents
 Introduction
 The Model’s Stochastic Equations
 Estimation Methods
 Estimation Results
 Summary of Findings and Conclusions
Estimation Results:
GPM5 Parameters
alpha1_AS
alpha2_AS
alpha3_AS
beta1_AS
beta2_AS
beta3_AS
gamma1_AS
gamma2_AS
gamma4_AS
lambda1_AS
lambda2_AS
lambda3_AS
rho_AS
phi_AS
tau_AS
rr_bar_AS_ss
growth_AS_ss
beta_reergap_AS
Prior
distribution
beta
gamm
beta
gamm
beta
gamm
beta
gamm
gamm
beta
gamm
gamm
beta
beta
beta
norm
norm
gamm
Prior
mean
0.750
0.100
0.500
0.650
0.150
0.150
0.750
1.100
0.500
0.500
0.400
0.050
0.500
0.600
0.050
1.500
5.000
0.050
Prior s.d.
0.1000
0.0500
0.2000
0.1000
0.1000
0.1000
0.1000
0.1000
0.2000
0.1000
0.1000
0.0100
0.2000
0.1000
0.0200
0.1000
0.2000
0.0200
Posterior
mode
0.8221
0.0687
0.4512
0.6353
0.0943
0.0690
0.9430
1.0857
0.4719
0.6299
0.3774
0.0480
0.0110
0.6625
0.0427
1.4835
5.0157
0.0472
s.d.
0.0356
0.0160
0.0548
0.0247
0.0320
0.0126
0.0155
0.0346
0.0576
0.0296
0.0198
0.0032
0.0689
0.0258
0.0049
0.0494
0.0651
0.0074
Estimation Results:
GPM5 S.D. of Structural Shocks
Prior
distribution
Prior
mean
Prior
s.d.
Posterior
mode
s.d.
RES_PIE_AS
invg
3.000
Inf
3.6982
0.4221
RES_Y_AS
invg
0.500
1.0000
0.2406
0.1084
RES_RS_AS
invg
0.600
1.0000
0.2213
0.0347
RES_LGDP_BAR_AS
invg
0.200
Inf
18.5265
0.9173
RES_G_AS
invg
0.100
Inf
0.0460
0.0381
RES_RR_BAR_AS
invg
0.200
Inf
0.1877
0.5291
RES_UNR_GAP_AS
invg
0.600
1.0000
0.2488
0.0478
RES_UNR_BAR_AS
invg
0.100
Inf
0.0461
0.0493
RES_UNR_G_AS
invg
0.100
Inf
0.0472
0.0237
RES_LZ_BAR_AS
invg
5.000
Inf
4.8714
0.8198
RES_RR_DIFF_AS
invg
1.000
Inf
0.4591
0.3230
Estimation Results:
GPM6 Parameters, 1
Prior
distribution
Prior
mean
Prior s.d.
Posterior
mode
s.d.
alpha1_PH
beta
0.750
0.0500
0.7810
0.0434
alpha2_PH
gamm
0.100
0.0500
0.0882
0.0503
alpha3_PH
beta
0.500
0.2000
0.4673
0.3086
beta_fact_PH
gamm
0.150
0.1000
0.1171
0.1024
beta1_PH
gamm
0.650
0.1000
0.5710
0.0806
beta2_PH
beta
0.150
0.0500
0.1234
0.0446
beta3_PH
gamm
0.150
0.0200
0.1310
0.0181
gamma1_PH
beta
0.900
0.0500
0.9101
0.0207
gamma2_PH
gamm
1.100
0.5000
0.8872
0.3600
gamma4_PH
gamm
0.500
0.2000
0.4034
0.1745
growth_PH_ss
norm
5.000
0.2000
5.0000
0.2000
lambda1_PH
beta
0.500
0.0500
0.5616
0.0477
lambda2_PH
gamm
0.400
0.1000
0.3522
0.0876
lambda3_PH
gamm
0.050
0.0300
0.0390
0.0292
lambda1_RS_PH
beta
0.500
0.1000
0.4469
0.0868
Estimation Results:
GPM6 Parameters, 2
phi_PH
beta
0.600
0.0500
0.6303
0.0364
pietar_PH_ss
gamm
4.714
0.3000
4.6951
0.2994
rho_PH
beta
0.500
0.2000
0.2675
0.1123
rr_bar_PH_ss
norm
1.500
0.5000
1.5000
0.5000
tau_PH
beta
0.050
0.0200
0.0436
0.0188
beta_reergap_PH
gamm
0.050
0.0100
0.0480
0.0098
chi_PH
beta
0.050
0.0100
0.0481
0.0098
growth_PH_ss
norm
5.000
0.2000
5.0401
0.1632
pietar_PH_ss
gamm
4.714
0.3000
4.6951
0.2994
rr_bar_PH_ss
norm
1.500
0.5000
1.4629
0.4574
beta_reergap_PH
gamm
0.050
0.0100
0.0503
0.0097
Estimation Results:
GPM6 S.D. of Structural Shocks
Prior
distribution
Prior mean
Prior s.d.
Posterior
mode
s.d.
RES_PIETAR_PH
invg
0.250
Inf
0.1028
0.0338
RES_PIE_PH
invg
3.000
Inf
3.2548
0.4240
RES_Y_PH
invg
0.500
1.0000
0.4392
0.0933
RES_RS_PH
invg
0.600
1.0000
0.2533
0.0486
RES_LGDP_BAR_PH
invg
0.200
Inf
0.0900
0.0353
RES_G_PH
invg
0.100
Inf
0.0442
0.0168
RES_RR_BAR_PH
invg
2.500
Inf
1.8752
0.4157
RES_UNR_GAP_PH
invg
1.000
1.0000
0.9615
0.1265
RES_UNR_BAR_PH
invg
0.100
Inf
0.0464
0.0192
RES_UNR_G_PH
invg
0.100
Inf
0.0477
0.0210
RES_LZ_BAR_PH
invg
5.000
Inf
5.7296
1.3704
RES_RR_DIFF_PH
invg
1.000
Inf
0.4587
0.1857
RES_DOT_LZ_BAR_PH
invg
0.100
Inf
0.0461
0.0188
Impulse Responses:
Shock to U.S. Output Gap
Impulse Responses:
Shock to U.S. Output Gap
Impulse Responses:
Shock to Euro Area Output Gap
Impulse Responses:
Shock to Euro Area Output Gap
Impulse Responses:
Shock to Japan Output Gap
Impulse Responses:
Shock to Japan Output Gap
Impulse Responses:
Shock to China Output Gap
Impulse Responses:
Shock to China Output Gap
Impulse Responses:
Shock to U.S. Output Gap
Impulse Responses:
Shock to Euro Area Output Gap
Impulse Responses:
Shock to U.S. Output Gap
Impulse Responses:
Shock to Euro Area Output Gap
Impulse Responses:
Shock to Japan Output Gap
Impulse Responses:
Shock to China Output Gap
Impulse Responses:
Shock to ASEAN Output Gap
Impulse Responses:
Shock to Philippine Output Gap
Impulse Responses:
Shock to Philippine Output Gap
Table of Contents





Introduction
The Model’s Stochastic Equations
Estimation Methods
Estimation Results
Summary of Findings and
Conclusions
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
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
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.
Findings and Conclusions, 4
■ Impulse responses of PH macro variables

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


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
Thank You!