the 14th international convention of the east asian economic...

The 14th International Convention of the East Asian Economic Association

Chulalongkorn University, Bangkok, November 1-2, 2014

Convention Theme:

“Reinvigorating and Rebalancing in the Wake of Global and Local Shocks”

Carlos C. Bautista (University of the Philippines)

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Monetary policy transmission in a model of the Philippine economy*

Carlos C. Bautista Cesar E.A. Virata School of Business

University of the Philippines [email protected]

Abstract

The study builds a model of the Philippine economy using quarterly data from 2001Q4 to 2013Q4. The model is based on the IMF’s family of global projection models (GPM). Regularized maximum likelihood method, a Bayesian-like method, is used to estimate the parameters of the model. The impulse response functions of the model indicate reasonable dynamic properties. The results show that inflation during the period under consideration is largely driven by forward-looking expectations of economic agents that are partly propelled by exchange rate movements. This highlights the difficulty of anchoring expectations in an inflation targeting regime when the economy is small and highly open. The movement of the output gap is highly influenced by the US output gap especially during and after the recent global crisis. Upon the onset of the global crisis, exchange rate intervention by the Philippine monetary authorities led to consistent undervaluation of the domestic currency that helped mitigate the negative effects of the crisis on the output gap.

JEL Classification Codes: E37, E52, E58

Keywords: Monetary policy transmission, Global projection model, Inflation targeting, Exchange rate intervention, Philippines

December 2014

_____________________________

*Paper presented at The 14th International Convention of the East Asian Economic Association Chulalongkorn University, Bangkok, November 1-2, 2014. The author would like to thank Bhanupong Nidhiprabha, Pongsak Luangaram, and conference participants for comments on an earlier draft. The usual disclaimer applies.

mailto:[email protected]

Monetary policy transmission in a model of the Philippine economy

1 Introduction

Developments in macroeconomic theory in the past two decades have provided a

more realistic view of how the economy works. The New Keynesian framework, the

basis of current mainstream macroeconomic thinking, allows for price rigidities and

non-competitive behavior on the part of economic agents (See Galí, 2008). The

framework has presented a challenge to applied macroeconomists and has spawned

a wide literature on empirical macroeconomic modelling. Aside from the New

Keynesian dynamic stochastic general equilibrium (DSGE) models now in use in

most Central Banks, other models have been developed as policy analysis tools that

are easy to implement and use. One such class of models developed in the later part

of the 2000 decade by the IMF, the global projection models (GPM), is also New

Keynesian in spirit. Since then, other GPMs have been built for several countries

(see for example, Carabenciov et al., 2008a, 2008b, 2008c; Clinton et al., 2010;

Harmanta et al., 2011; Liu and Zhang, 2010). This study builds a small-scale

quarterly model of the Philippine economy that is based on the IMF-GPMs. It makes

use of publicly available Philippine quarterly data from 2001Q4 to 2013Q4. The data

are obtained from the websites of the IMF, the Bangko Sentral ng Pilipinas (BSP) and

the National Statistical Coordinating Board (NSCB). Regularized maximum

likelihood method, a Bayesian-like method, is used to estimate the parameters of the

model. The model’s dynamic properties are evaluated through its impulse response

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functions. Historical decompositions of the output gap, the inflation rate and the

policy rate are done.

A background on recent macroeconomic developments in the Philippine

economy is given in the next section as a way to motivate the selection of the

model’s parameters in the fourth section. The third section provides a detailed

description of the model, its provenance, and the modifications introduced in the

standard specification. The data and estimation results are presented in the fourth

section while the last section gives some concluding remarks.

2 The Philippine economy during the sample period

The early years of the 21st century mark the period where several countries

including the Philippines started adopting inflation targeting as the framework for

monetary policy. These are also the years when the effects of the 2000-2001

recession in advanced economies began tapering off. The events that transpired

show that the Philippine economy is highly vulnerable to external shocks. The

Philippines grew by less than 1 percent in 2001 as a result of the negative external

shocks. Recovery began with a growth rate of 1.78 percent the following year.

As the economy came out of an externally-generated recession, GDP growth rate

rose steadily (red line in Figure 1C) while the domestic currency slowly depreciated

because of rising import demand (blue line in Figure 1A.)1 This sent an alarming

signal to the BSP as the inflation rate rose to 7 percent in the first quarter of 2005.

1 The shaded area in the diagrams identifies the period of the recent global crisis, also known as the great recession of 2008-2009 in the US. The terms are used here interchangeably depending on the events being referred to.

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The problem of the BSP is to determine the sources of inflation which can originate

either from the supply side or the demand side. In the quarterly inflation reports

from 2002 to 2013, the BSP (2002-2013) decomposes headline inflation rate into

food and non-food inflation. Aside from pressures from the demand side, the BSP’s

analyses in a majority of the reports point to movements in food prices arising from

weather-related agricultural supply bottlenecks and in the prices of energy-related

products as mainly responsible for the general price level movements and its

volatility.2

To deal with a volatile currency, the BSP began an unannounced foreign exchange

intervention program in 2005 with the objective of minimizing the volatility of the

currency. This is thought to discourage speculative capital flows and help maintain

monetary stability.3 The year 2005 coincidentally, is also the year when the

currency began appreciating as foreign remittances from nationals working abroad

grew significantly more than in previous years while FDIs and portfolio investments

were encouraged by good growth prospects (See IMF, 2012). As shown in Figure 1A,

the currency continued to appreciate after a series of global crisis-induced

devaluations in 2008. It may also be noticed that the volatility of the inflation rate,

upon visual inspection, seems to have receded after 2009.

2 Throughout the sample period, a few devastating weather disturbances not only cut agricultural food production but also led to crippling destruction of infrastructure that reduced the economy’s capacity. It may be noted that the food, beverage and tobacco item in the consumer basket carries a weight of 58.5 percent. (See www.nscb.gov.ph/ru5/technotes/cpi.html)

3 Guinigundo (2013) notes that the purchases and sales of foreign exchange by the BSP are daily covert operations which, according to his preliminary econometric results, appear to have raised the volatility of the market when intervention is prolonged over consecutive trading days. However spot market intervention is effective in containing same-day volatility.

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Figure 1A Figure 1B

40

50

60

70

0

4

8

12

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Nominal Exchange Rate

Year-on-Year Inflation (right scale)

Price Movements vs Exchange Rate

Source of basic data: BSP; elibrary -data.imf.org

4

8

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Target (Median) Inflation Rate

Policy Rate Target Range

Inflation Target

Source of bas ic data: BSP; elibrary -data.imf.org

Figure 1C Figure 1D

-5

0

5

10

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

US Growth Philippine Growth

US & the Philippines : GDP Growth Rates (YoY)

Source of bas ic data: BSP; elibrary-data.imf.org

0

4

8

12

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Money Market Rate

Overnight RRP Rate

Nominal Interest Rates

Source of bas ic data: BSP; elibrary -data.imf.org

Figure 1B shows the inflation target and its range, superimposed with the policy

rate and the actual year-on-year inflation rate. The first five years of inflation

targeting was clearly a learning-by-doing period for the BSP. After the 2008-2009

global crisis, actual inflation has been within the target range in a majority of the

quarters. The spike in inflation in 2008 is associated with the abrupt increase in

crude oil prices during the global crisis. The BSP does not often use the policy rate to

influence monetary conditions because it has other tools like the reserve

requirement, the SDAs, the discount rate and open market operations that are

available. Accordingly, the policy rate is raised only when the BSP believes that the

inflation outlook is truly at risk, i.e., when the inflation rate is about to move out of

the target range.

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Figure 1C shows that the Philippines enjoyed the benefits of robust US growth

before the start of the great recession. During the period 2004 to 2007, the average

year-on-year growth rate of the Philippines stood at 5.8 percent. It is clear though

that the strongest co-movement of growth of the two countries occurred during the

most recent cycle of growth and recession. Growth of the Philippines was at its

lowest for the decade when the US was at the bottom of the great recession. The

Philippines also matched the increase in growth rates of short-lived recovery spurts

of the US in 2010 and 2012. Thus it is apparent in the above discussion that the

Philippines is a highly open economy that is vulnerable to both external and

domestic supply shocks. Accordingly, these shocks are taken into consideration

when the BSP formulates its aggregate demand management policies.

3 The Model

The model of this study is based on a series of country- and regional-level global

projection models (GPM) developed by the modeling team of the IMF.4 The

developers of the GPM intended the model to be used as an additional tool that

complements the multi-equation DSGE and single equation time series models used

by the policymakers. The need for this model in the IMF also arose because of the

apparent difficulties in formulating multi-country DSGEs that can be used in their

4 The first in a series of GPMs was a closed-economy model for the United States which was eventually extended to a multi-country open economy model (Carabenciov et al., 2008a, 2008b). Further extensions of the GPM to include the oil price and financial market activity were also done by Carabenciov et al. (2008c). This was subsequently followed by models for Indonesia (Andrle el al., 2009) and the Latin American region (Brazil, Chile, Colombia, Mexico and Peru; Canaes-Kriljenko et al., 2009). Other regions – the EU, Japan and emerging Asia – were later included (Carabenciov et al., 2013). The latest addition is a satellite GPM for China by Blagrave et al. (2013).

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global forecasting exercises. Because they are easier to build, GPMs linked to one

another are seen as a good alternative.

3.1 Behavioral equations

This study’s model like other GPMs contains three key equations that constitute

the core of the New Keynesian framework. The IS equation in (1) describes the

evolution of output. In this model, the IS function is expressed in terms of gaps of the

variables, which are defined as deviations of the variables from their equilibrium

values:

�̃�𝑡 = 𝛽1�̃�𝑡−1 + 𝛽2�̃�𝑡+1 − 𝛽3�̃�𝑡−1 + 𝛽4�̃�𝑡−1 + 𝛽5�̃�𝑡∗ + 𝜀�̃�𝑡 (1)

The output gap, �̃�𝑡, is a function of its lead and lag values, the lagged real interest

rate gap, �̃�𝑡−1, the lagged real exchange rate gap, �̃�𝑡−1, and the foreign output gap,

�̃�𝑡∗.5 The lead variable embodies the outlook of economic agents as to how the

direction of the economy influences current aggregate demand. The lagged

dependent variable allows some persistence in the output gap. This persistence can

potentially alter inflation dynamics depending on the value of β1 and how �̃�𝑡 enters

the aggregate supply equation. The real interest rate serves to link the monetary

sector with the real sector of the economy. The real exchange rate and foreign

output reflect the extent of influence of the external sector on the local economy.

5 Henceforth, variables with tildes are gap variables while those with bars are equilibrium values of the variables. Uppercase letters denote level variables; lowercase letters denote variables in logarithms except the interest rate variables. Variables with asterisks are foreign variables. All exogenous shocks are denoted by the Greek letter ε.

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The supply side is represented by an open-economy hybrid New Keynesian

Philips curve:

𝜋𝑡 = 𝜆1𝜋𝑡+1 + (1 − 𝜆1)𝜋𝑡−1 + 𝜆2�̃�𝑡−1 + 𝜆3Δ𝑧𝑡 + 𝜀𝜋𝑡 (2)

The hybrid version permits some flexibility in modelling inflation persistence. It is

important to note that the hybrid version is not a trivial extension of the standard

New Keynesian Philips curve which does not allow for inflation inertia. The addition

of the lagged dependent variable is mainly an empirical necessity to accommodate

the persistence observed in the data. Its presence in the equation can also be

justified as a result of price setting behavior of monopolistically competitive firms

who base their inflation expectations on past inflation (See Gali and Gerler, 1999.) In

equation (2), a value close to 1 for the λ1 parameter means that inflation jumps

instantaneously with expectations and implies low inflation persistence. Other

sources of persistence are inherited from the lagged output gap and the real

exchange rate.

The policy reaction function is a standard Taylor rule equation:

𝑖𝑡 = 𝛾1𝑖𝑡−1 + (1 − 𝛾1)[�̅�𝑡 + 𝜋𝑡+4𝐴 + 𝛾2(𝜋𝑡+3

𝐴 − 𝜋𝑡+3𝑇 ) + 𝛾4�̃�𝑡] + 𝜀𝑖𝑡 (3)

The reaction function allows for interest rate smoothing through γ1. Here, policy

rate setting by monetary authorities depends on the deviation of the three quarters-

ahead year-on-year inflation rate forecast from the inflation target and the

magnitude of the output gap (given the neutral interest rate, �̅�𝑡 + 𝜋𝑡+4𝐴 .) The year-on-

year inflation rate, 𝜋𝑡𝐴, is the average of the current and three lags of the annualized

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quarterly inflation rate, πt, as defined in (8) below. This study uses the BSP’s

overnight reverse repurchase (ORRP) rate as the policy rate.

The canonical GPM model assumes that a country’s policymakers actively use an

inflation targeting strategy with the policy rate as the sole monetary policy tool. In

practice, monetary authorities in the Philippines make use of other policy tools for

aggregate demand management. This study relaxes the assumption of an active

targeting strategy so that other policy tools at their disposal can be included in the

modeling exercise. In particular, the study’s GPM is extended to permit an analysis

of the effects of the reserve requirement on the macroeconomy.6 This study takes

the cue from Blagrave et al (2013) and assumes that the inflation gap is the main

consideration in setting the reserve requirement ratio:

𝑅𝑞𝑡 = 𝜂1𝑅𝑞𝑡−1 + (1 − 𝜂1)�̅�𝑞𝑡+1 + 𝜂2(𝜋𝑡+3𝐴 − 𝜋𝑡+3

𝑇 ) + 𝜀𝑟𝑞𝑡 (4)

Blagrave et al’s (2013) specification for China is however more complex as the

decision to set the ratio is in addition assumed to depend on the output gap and the

shocks to the policy rate.

The reserve requirement ratio in this study is assumed to be a factor in the

movement of the nominal money market interest rate along with the policy rate:

6 Another frequently used tool by the BSP is the interest rate on special deposit accounts (SDA) of commercial banks with the BSP. The SDAs, introduced in 1998 to help the BSP manage liquidity, have effects similar to reserve deposits but unlike the latter are not required by law. For the second quarter of 2014, the BSP raised the reserve requirement ratio by 1 percent and the SDA rate by 25 basis points within a span of less than a month based on the inflation forecasts by the BSP staff. (see http://www.philstar.com/business/2014/05/09/1320893/bsp-raises-banks-reserve-requirements and http://www.bsp.gov.ph/publications/media.asp?id=3447)

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𝑖𝑚𝑡 = 𝜁1𝑖𝑡 + 𝜁2(𝑅𝑞𝑡 − �̅�𝑞𝑡) + 𝜀𝑖𝑚𝑡 (5)

where the reserve requirement ratio’s equilibrium value follows a unit root process:

�̅�𝑞𝑡 = �̅�𝑞𝑡−1 + 𝜀�̅�𝑞𝑡

The real interest rate, defined as the difference between the money market rate

and the expected inflation rate, 𝑟𝑡 = 𝑖𝑚𝑡 − 𝜋𝑡+1, enters the IS equation instead of the

real policy rate. Thus monetary policy is assumed to influence economic activity

through the money market interest rate. There are a few practical reasons for

modeling monetary transmission in this manner. As can be seen in Figure 1D, both

money market rate and the policy rate track each other quite well.7 The 91-day

treasury bills rate which has been the choice of researchers in examining monetary

and credit conditions in the Philippines is unfortunately beset with missing data for

some recent years and could not be used for this study’s purposes. Thus given its

tracking ability and the availability of all observations of its time series, the money

market rate was deemed the best rate to use in the model.

The GPM model by design does not have a fully articulated financial sector where

credit channels are available and where the impact of reserve requirement changes

could be felt directly. For this reason, it was decided to simply include the Rq gap in

7 The information given by the diagram is in fact the basis for setting the prior of η1 = 1 in the solution of the model presented in the results section below.

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equation (5).8 This means that the reserve requirement influences real activity

through its effects on the money market rate.

A version of the uncovered interest parity condition (UIPC) in real terms is

shown in equation (6). The UIPC effectively states that the real interest differential

expressed in terms of deviation from its equilibrium value equals the expected real

depreciation rate of the domestic currency plus some exogenous shock.

(𝑟𝑡 − �̅�𝑡) − (𝑟𝑡∗ − �̅�𝑡

∗) = 4 ⋅ (𝑧𝑡𝑒 − 𝑧𝑡) + 𝜀𝑟𝑟𝑡 (6)

Equation (6) also implies that, assuming no shocks, the expected level of the real

exchange rate is equal to the actual level whenever both interest rates are in

equilibrium.

The expected real exchange rate level is a weighted average of backward- and

forward-looking elements:

𝑧𝑡𝑒 = 𝜑𝑧𝑡+1 + (1 − 𝜑)𝑧𝑡−1 (7)

The expected depreciation rate in (6) is converted to annual rates to match the units

of the interest rates. The foreign country in the model is the United States, the

largest trading partner of the Philippines.

3.2 Stochastic processes and definitions in the model

A significant portion of the model consists of identities and stochastic processes

that show how variables evolve over time. Equation (8) is the quarterly inflation

8 In Blagrave et al. (2013), Rq enters the IS curve directly while in Bautista et al. (2013) the sum of the policy rate and a credit condition variable is used instead. The latter is a variable created to cover the effects of all other policy tools (including the reserve requirement ratio) except the former.

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rate calculated as the natural logarithm of the ratio of current and previous quarter

CPI and multiplied by 4 to yield the annual rate. Its average over the current and the

past three quarters shown in (9) is the year-on-year inflation rate. The inflation

target in (10) is a weighted average of its steady state value and its value in the past

quarter plus some exogenous disturbance. This specification allows the inflation

target to deviate from and revert back to its steady state value. The speed of

reversion of the target to its steady state value depends on the parameter ξ.

𝜋𝑡 = 4 ⋅ ln (𝑃𝑡𝑃𝑡−1

) (8)

𝜋𝑡𝐴 = (𝜋𝑡 + 𝜋𝑡−1 + 𝜋𝑡−2 + 𝜋𝑡−3)/4 (9)

𝜋𝑡𝑇 = 𝜉𝜋𝑠𝑠

𝑇 + (1 − 𝜉)𝜋𝑡−1𝑇 + 𝜀𝜋𝑇𝑡 (10)

The level of potential output, ln 𝐺𝐷𝑃̅̅ ̅̅ ̅̅𝑡, follows a stochastic trend in (11). However,

annual potential output growth, gt, can diverge from its steady state as a result of

exogenous shocks as shown in (12). As in (10), gt’s speed of reversion to its steady

state value depends on the parameter τ. The output gap is the log difference

between actual and potential GDP in (13).

ln 𝐺𝐷𝑃̅̅ ̅̅ ̅̅𝑡 = ln𝐺𝐷𝑃̅̅ ̅̅ ̅̅

𝑡−1 +𝑔𝑡4+ 𝜀�̅�𝑡 (11)

𝑔𝑡 = 𝜏𝑔𝑠𝑠 + (1 − 𝜏)𝑔𝑡−1 + 𝜀𝑔𝑡 (12)

�̃�𝑡 = ln𝐺𝐷𝑃𝑡 − ln𝐺𝐷𝑃̅̅ ̅̅ ̅̅𝑡 (13)

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Equations (14) to (16) detail the calculation of the real interest rate gap, �̃�𝑡. The

equilibrium value, �̅�𝑡, can deviate from and return to its steady state value at a speed

given by ρ:

𝑟𝑡 = 𝑖𝑚𝑡 − 𝜋𝑡+1 (14)

�̅�𝑡 = 𝜌�̅�𝑠𝑠 + (1 − 𝜌)�̅�𝑡−1 + 𝜀�̅�𝑡 (15)

�̃�𝑡 = 𝑟𝑡 − �̅�𝑡 (16)

The real exchange rate variable shown in (17) is calculated as the logarithm of

the ratio of the foreign price level in domestic currency terms, 𝑆𝑡𝑃𝑡∗, and the

consumer price index, Pt ; St is the nominal exchange rate expressed as the domestic

currency value of one unit of the foreign currency. In (18), the equilibrium real

exchange rate is assumed to obey a driftless random walk process; the real

exchange rate gap is given in (19).

𝑧𝑡 = ln (𝑆𝑡𝑃𝑡

∗

𝑃𝑡) (17)

𝑧�̅� = 𝑧�̅�−1 + 𝜀�̅�𝑡 (18)

�̃�𝑡 = 𝑧𝑡 − 𝑧�̅� (19)

Equations (20) to (22) describe the path of the foreign real interest rate, its

equilibrium counterpart and the foreign output gap:

𝑟𝑡∗ = 𝜌∗�̅�𝑠𝑠

∗ + (1 − 𝜌∗)𝑟𝑡−1∗ + 𝜀𝑟∗𝑡 (20)

13

�̅�𝑡∗ = �̅�∗�̅�𝑠𝑠

∗ + (1 − �̅�∗)�̅�𝑡−1∗ + 𝜀�̅�∗𝑡 (21)

�̃�𝑡∗ = 𝛽∗�̃�𝑡−1

∗ + 𝜀𝑦∗𝑡 (22)

The model has a set of equations that details how the unemployment rate is

determined:

𝑢𝑡 = �̅�𝑡 − �̃�𝑡

�̃�𝑡 = 𝛼1�̃�𝑡−1 + 𝛼2�̃�𝑡 + 𝜀𝑢𝑡

�̅�𝑡 = (1 − 𝛼3)�̅�𝑡−1 + 𝛼3�̅�𝑠𝑠 + 𝑢𝑡𝑔+ 𝜀𝑢𝑡

𝑢𝑡𝑔= (1 − 𝛼4)𝑢𝑡−1

𝑔+ 𝜀𝑢𝑔𝑡

(23)

However, this block of equations does not have a role in determining the path of

other variables in the model. There are 18 exogenous shocks in the model and 27

equations to solve for 27 endogenous variables.

4 The data and estimation results

The data used in the estimation of the parameters were obtained from the

websites of the BSP, the NSCB and the IMF’s electronic database web. Figure 2

shows the graphs of the ten measurement variables. The foreign measurement

variables which are fully exogenous in the model were estimated using a simple

closed economy GPM for the United States.9

9 Details of this model can be found in cba.upd.edu.ph/bautista/GPM-US. See Table 4 for the URLs of the raw data sources. All data except the unemployment rate cover the period 2002-2013. Prior to 2006, the unemployment rate hovered above 10 percent. A change of definitions in 2006 showed an average of 7.3 percent for the remaining period of the study.

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Regularized maximum likelihood estimation (RMLE) method, a Bayesian-like

method, is used to estimate the parameters of the model. RMLE is implemented

using the IRIS toolbox, a macro-modelling package developed for use in MATLAB.10

The procedure requires the user to set the initial values of the parameters or the

priors, their (upper and lower) boundary values and the standard deviations of the

distribution of the parameters which are assumed to be normal (See Beneš and

Fukač, 2008). The advantages and disadvantages of Bayesian econometrics and

Bayesian-like methods have been documented in other GPM papers (Carabenciov,

2008).

Bayesian estimation makes use of both the calibration procedures of CGE models

and classical estimation. This provides a method for the researcher’s prior beliefs

about the economy embodied in his chosen parameters to be adjusted in a way that

is consistent with the data. Here, the model is confronted with the data to generate

posterior estimates of the model parameters. Prior parameters may diverge from or

be close to the posterior estimates depending on the weights assigned by the

researcher to the data on the one hand and to the specified priors on the other hand.

The researcher, if he or she knows the parameter value with accuracy, has the

option to influence the estimate in that direction by specifying the tightness of the

distribution of the prior. Generally, a loose prior or a high standard deviation of the

prior distribution gives the data more weight in determining the posterior values.

This may be done when the researcher does not have sufficient knowledge about

10 All the information needed to operate the IRIS toolbox for macroeconomic modeling and forecasting in MATLAB can be found in http://iristoolbox.codeplex.com/.

15

the parameter’s values. Prior knowledge can come from various sources and can

vary from econometric estimates of previous studies to a simple observation of

behavior of economic agents given an understanding of the history and current

developments in the economy.

The posterior parameter estimates are shown in Table 1. There are no previously

published econometric estimates of the parameters that the author is aware of. This

study’s estimates nonetheless appear to confirm the importance of forward-looking

expectations as seen in the posterior estimate of λ1. The BSP inflation reports

indicate careful monitoring of expectations by the BSP as it affects the inflation

outlook (See BSP (2002-2013) and Guinigundo (2008)). As discussed in Section 2,

the price movements have been driven mostly by fluctuations in food prices owing

to agricultural food supply fluctuations which in turn are highly dependent on

weather conditions. Note that the posterior estimate of the output gap parameter in

the inflation equation, λ2 is not that far from its loosely specified prior. This estimate

is the second largest compared to those of other countries (See Table 3). It implies

fairly flexible prices and a moderately steep aggregate supply relation. The estimate

of λ2, together with a high estimate for λ1, shows that inflation persistence inherited

from a driving process may arguably be more important than intrinsic persistence.11

For some parameters where the study has no information, the priors are set to

0.5. Calibrated parameters are shown in Table 2. The steady state growth of

potential output is set to the average GDP growth of 5.7 percent for the period under

11 See Fuhrer (2009) for a taxonomy of persistence.

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study. The steady state inflation target and steady state real interest rate are set to

4.0 percent and 2.0 percent respectively. The estimates for the Philippines can be

compared with estimates for other countries. In Table 3, the estimate of the real

exchange rate coefficient in the IS equation in the Philippine model is much higher

than for the other countries. The Philippines also has the highest coefficient

estimates for the lead variable in the inflation equation and the lag variable in the

policy rule function. The other parameters for the Philippines fall within the range

of the group.

4.1 Impulse response functions

In practice, several versions of the same model but with differing degrees of

tightness of the priors are estimated. The result presented in Table 1 is chosen

primarily because it yields impulse response functions (IRFs) that are sensible,

intuitively plausible and do not contradict known theoretical results. In Figures 3 to

11, the IRFs of selected variables yield reasonable dynamic properties of the model.

In these simulation exercises, a disturbance that elicits a response from a variable is

a positive one unit increase in shock.

Figure 3 shows an increase in growth due to an aggregate demand shock. The

excess demand produces inflation that forces the monetary authorities to raise the

policy rate. This leads to an increase in the money market rate. However,

inflationary expectations rise more than the market rate to reduce the real interest

rate. This leads to a real exchange rate appreciation. In Figure 4, an inflation shock

that causes an increase in the inflation rate induces the monetary authorities to

17

raise the policy rate. Growth declines as the real rate rises and the real exchange

rate appreciates.

The effects of a monetary policy shock shown in Figure 5 manifest itself as an

increase in the policy rate that works its way to the money market rate. This leads to

a decline in growth owing to an increase in the real interest rate; a decline in the

inflation rate follows. By the UIPC, a real appreciation of the currency takes place. In

Figure 6, an exogenous shock to the money market rate that is not due to policy has

a similar effect on growth and inflation. However the tightening of monetary

conditions and the lower inflation rate allows for some relaxation of monetary

policy as seen by a decline in the policy rate. The IRFs for the reserve requirement

shock in Figure 7 are similar to those of the money market shock, the difference

being in the magnitude of the effects.

In Figure 8, an increase in the target inflation rate causes a decline in the policy

rate and the money market rate initially in the first two quarters but subsequently

rises. As inflation expectations adjust upwards, the real interest rate declines. This

creates excess demand and raises growth, depreciates the currency and raises the

inflation rate. Note that an attempt at disinflation by policymakers can be simulated

by a reduction in the inflation target. Hence, these effects would be the reverse.

Figure 9 shows the IRFs from a shock to the real exchange rate. The ensuing real

depreciation of the currency stimulates growth and leads to inflation and a rise in

the interest rates. Figure 10 shows that a shock to foreign demand raises growth as

the output gap widens and leads to inflation and rising nominal interest rates.

Figure 11 shows the IRFs from a foreign interest rate shock. This leads to a real

18

depreciation (through the UIPC) and stimulates growth and raises the inflation rate.

The monetary authorities react by raising the policy rate.

4.2 The output gap, inflation, interest rates and the real exchange rate

This subsection presents the estimates of selected variables of the model. It also

examines how the three key equations of the model describe the Philippine

economy through a historical decomposition. This decomposition calculates the

breakdown of a variable’s fitted values into its explanatory variable components.

Figure 12A shows the graph of actual and potential GDP. The difference between

actual and potential GDP or the output gap is negative as the Philippines comes out

of the early 2000 recession. By 2004, it has turned positive until the fourth quarter

of 2008. As discussed in Section 2, continued growth during this period could partly

be attributed to external growth. This long period with positive output gaps is

accompanied by some inflation. Upon the onset of the global crisis however, a

relatively large negative gap was experienced until the early portion of 2010 after

which the economy started operating at close to full capacity. Positive gaps can be

observed in 2010, 2012 and 2013 when the US had short growth spurts. Figure 12B

shows the path of the real exchange rate and its equilibrium path. One can see a

general trend appreciation for both variables. It is interesting to note that prior to

the global crisis, the real exchange rate is either above or below, but very close to, its

equilibrium value. At the start of the global crisis however, the real exchange rate

began to be consistently undervalued. This may arguably be due to a more active

intervention of the BSP in the foreign exchange market in an effort to reduce

19

volatility. Within the context of the study’s model such undervaluation may not be a

bad idea as shown in the historical decomposition below.

Figure 13A shows the estimated and fitted values of the output gap. The lower

diagram decomposes the output gap based on the estimated posterior coefficients.

In the pre-global crisis period, positive output gaps are chiefly attributable to

persistence of the gap itself. During the crisis and post-crisis periods however, the

effects of the 2008-2009 great recession of the US economy are felt through

negative US output gaps (violet bars). The effect of the slack in US demand is

partially dampened by an undervalued currency as seen through positive real

exchange rate gaps (yellow bars). The undervaluation appears to reign in the effects

of a currency appreciation on the output gap. It is clear from the diagram that

appreciation is larger if from a non-equilibrium position, the currency is allowed to

reach equilibrium. In this case, the resulting smaller real exchange rate gap leads to

a wider negative output gap. The undervaluation has the effect of not discouraging

exports as much as it would have if the currency were properly priced.

The upper diagram of Figure 13B presents the estimated and fitted values of the

annualized quarterly inflation rate while the lower diagram shows the

decomposition. One can see from the diagram that throughout the sample period,

expected inflation accounts for most of the movements in the inflation rate.

Currency appreciation has a restraining effect on inflation. Lagged inflation and

lagged output gap account for the persistence in the inflation rate.

20

Figure 13C examines the movement of the policy rate based on the policy

function parameter estimates. One can observe strong inertia of the policy rate. It is

interesting to note that the deviation from target appears to matter only around the

time of and during crisis episodes. This can be seen in 2002-2003 after the 2000-

2001 recession and in 2008-2009 global crisis. One can observe from the diagram

that lagged output gap does not seem to matter for policy rate setting.

5 Concluding remarks

The quarterly macroeconomic model based on the IMF’s GPM model is

constructed for the Philippines. The model is shown to have reasonable dynamic

properties as a whole. It also performed satisfactorily in tracking the Philippine

economy. The results show that inflation during the period under consideration is

largely driven by forward-looking expectations of economic agents that are partly

propelled by exchange rate movements. This highlights the difficulty of anchoring

expectations in an inflation targeting regime when the economy is small and highly

open. The movement of the output gap is highly influenced by the US output gap

especially during and after the recent global crisis. Upon the onset of the global

crisis, exchange rate intervention by the Philippine monetary authorities led to

consistent undervaluation of the domestic currency that helped mitigate the

negative effects of the crisis on the output gap.

The model can be extended further by including oil prices as a factor in the

movement of the general price level and potential output and foreign remittances as

one of the factors in determining aggregate demand. Another extension is to build

21

similar GPM clones of other ASEAN trading partners and link them together through

the key equations and the exchange rate relations. These are areas of future

research. The study shows the importance of determining the causes of inflation

persistence. It finds that inflation persistence arising from persistence of the output

gap can be an important source of aggregate price stickiness. There is however no

research conducted at a micro level on price stickiness in the Philippines to validate

this notion. Further research along this line must therefore be conducted in the

future to confirm or refute this finding.

22

Table 1

Estimation results

Parameter estimates Standard deviation of shocks

Prior Posterior Prior Posterior mode dispersion mode dispersion mode dispersion mode dispersion

α1 0.750 0.632 0.122 0.218 𝜀�̅�𝑡 0.500 0.632 0.483 0.218

α2 0.500 0.632 0.036 0.124 𝜀�̃�𝑡 0.500 0.632 0.527 0.124

α3 0.200 0.632 0.085 0.134 𝜀𝑔𝑡 0.500 0.632 0.756 0.134 α4 0.500 0.632 0.492 0.493 𝜀𝑢𝑡 0.500 0.632 0.416 0.493 β1 0.700 0.158 0.589 0.106 𝜀𝑢𝑡 0.500 0.632 0.087 0.106 β2 0.250 0.158 0.192 0.109 𝜀𝑢𝑔𝑡 0.500 0.632 0.047 0.109

β3 0.300 0.158 0.120 0.095 𝜀𝜋𝑡 1.500 0.632 2.540 0.095 β4 0.150 0.158 0.158 0.069 𝜀�̅�𝑡 0.500 0.063 0.649 0.069 β5 0.150 0.158 0.167 0.089 𝜀𝑖𝑡 0.500 0.632 0.323 0.089 β* 0.800 0.316 0.980 0.043 𝜀𝜋𝑇𝑡 0.500 0.632 0.669 0.043 η1 0.500 0.158 0.665 0.407 𝜀𝑟𝑟𝑡 0.500 0.632 0.652 0.407 η2 0.500 0.158 0.374 0.126 𝜀�̅�𝑡 2.000 0.632 3.248 0.126

γ1 0.700 0.158 0.884 0.065 𝜀𝑦∗𝑡 0.500 0.632 0.554 0.065 γ2 1.500 0.474 1.264 0.158 𝜀�̅�∗𝑡 2.000 0.063 2.615 0.158 γ4 0.200 0.158 0.179 0.095 𝜀𝑟∗𝑡 2.000 0.632 2.388 0.095

λ1 0.700 0.158 0.888 0.053 𝜀𝑟𝑞𝑡 0.500 0.632 0.749 0.053

λ2 0.450 0.316 0.345 0.029 𝜀𝑖𝑚𝑡 0.500 0.632 0.014 0.029

λ3 0.500 0.158 0.186 0.243 𝜀�̅�𝑞𝑡 1.000 0.632 1.302 0.243

φ 0.500 0.632 0.751 0.022

ρ 0.500 0.632 0.037 0.020

τ 0.500 0.632 0.370 0.066

ξ 0.500 0.158 0.498 0.102 ζ1 1.000 0.158 1.025 0.083 ζ2 0.500 0.158 0.288 0.115

Table 2

Calibrated Parameters

𝑔𝑠𝑠 5.7

𝜋𝑠𝑠𝑇 4.0

�̅�𝑠𝑠 2.0

�̅�𝑠𝑠∗ 1.9

𝜌∗ 0.8

�̅�∗ 0.9

�̅�𝑠𝑠 7.3

23

Table 3 Comparison of Estimates

IS Equation Philips Curve Policy Rule Equation

β1 β2 β3 β4 β5 λ1 λ2 λ3 γ1 γ2 γ4

Egypt 0.453 0.164 0.079 0.047 0.093 0.647 0.479 0.091 0.865 1.273 0.310 Indonesia 0.428 0.149 0.164 0.035 0.178 0.270 0.209 0.107 0.628 1.384 0.186 LatinAmerica 0.488 0.180 0.162 0.050 0.189 0.573 0.233 0.149 0.622 1.224 0.189 Brazil 0.366 0.143 0.129 0.049 0.110 0.589 0.198 0.276 0.774 1.149 0.150 Chile 0.389 0.155 0.154 0.049 0.098 0.564 0.168 0.298 0.735 0.908 0.176 Columbia 0.650 0.231 0.106 0.050 0.259 0.397 0.158 0.098 0.695 1.005 0.183

Mexico 0.720 0.273 0.121 0.049 0.189 0.532 0.255 0.094 0.725 1.064 0.143 Peru 0.425 0.179 0.159 0.051 0.127 0.618 0.196 0.286 0.771 1.133 0.178

Philippines 0.589 0.192 0.120 0.158 0.167 0.888 0.345 0.186 0.884 1.264 0.179

Source: Arbatli and Moriyama (2011); This study’s calculation for the Philippines.

Table 4 Data sources

Variable Website Units

Real GDP www.nscb.gov.ph Bil 2000 ₱

Unemployment rate www.nscb.gov.ph in percent Reserve requirement ratio www.bsp.gov.ph in percent Overnight RRP rate www.bsp.gov.ph in percent Money market rate www.bsp.gov.ph in percent US monetary policy related interest rate elibrary-data.imf.org in percent Philippine CPI elibrary-data.imf.org 2005 = 100 US CPI elibrary-data.imf.org 2005 = 100 US real GDP elibrary-data.imf.org Bil 2005 $ Exchange rate elibrary-data.imf.org ₱/$

24

Figure 2

Historical Data: Philippines, 2002Q1 – 2013Q4

-20

0

20

40

2002 2004 2006 2008 2010 2012

Consumer Price Index (in logs x 100)

680

720

760

2002 2004 2006 2008 2010 2012

Real GDP (deseasonalized, in logs x 100)

340

360

380

400

420

2002 2004 2006 2008 2010 2012

Real Exchange Rate (in logs x 100)

0

4

8

12

2002 2004 2006 2008 2010 2012

Money Market Rate

12

16

20

24

2002 2004 2006 2008 2010 2012

Reserve Requirement Ratio

-4

-2

0

2

4

2002 2004 2006 2008 2010 2012

Equilibrium US Real Interest Rate

-4

-2

0

2

4

2002 2004 2006 2008 2010 2012

US Real Interest Rate

2

4

6

8

2002 2004 2006 2008 2010 2012

Overnight RRP Rate

-8

-4

0

4

2002 2004 2006 2008 2010 2012

US Output Gap

6

7

8

9

2006 2007 2008 2009 2010 2011 2012 2013

Unemploy ment Rate

2006 - 2013

25

Figure 3 Aggregate Demand Shock

-1

0

1

0 10 20 30 40

GDP Growth Response

to an Aggregate Demand Shock

.0

.4

0 10 20 30 40

Inflation Rate Response


0.0

0.4

0.8

0 10 20 30 40

Output Gap Response


0

2

4

0 10 20 30 40

Quarterly GDP Growth Response


.0

.5

0 10 20 30 40

Quarterly Inflation Rate Response


.0

.1

.2

0 10 20 30 40

Policy Rate Response


.0

.2

0 10 20 30 40

Money Market Rate Response


-.4

.0

0 10 20 30 40

Real Interest Rate Response


-.4

-.2

.0

0 10 20 30 40

Real Exchange Rate Response


26

Figure 4 Inflation Shock

-.04

.00

.04

0 10 20 30 40

GDP Growth Response

to an Inflation Rate Shock

.0

.1

.2

0 10 20 30 40



-.04

-.02

.00

0 10 20 30 40

Output Gap Response


-.05

.00

.05

0 10 20 30 40



0.0

0.5

1.0

0 10 20 30 40



.00

.02

0 10 20 30 40



.00

.04

0 10 20 30 40



.00

.04

.08

0 10 20 30 40



-.04

.00

0 10 20 30 40



27

Figure 5 Monetary Policy Shock

0

0 10 20 30 40

GDP Growth Response

to a Policy Rate Shock

-.8

-.4

.0

0 10 20 30 40



-.8

-.4

.0

0 10 20 30 40

Output Gap Response


-2

-1

0

0 10 20 30 40



-1.0

-0.5

0.0

0 10 20 30 40



.0

.4

0 10 20 30 40



.0

.4

0 10 20 30 40



0

1

0 10 20 30 40



-1.0

-0.5

0.0

0 10 20 30 40



28

Figure 6 Money Market Interest Rate Shock

-.2

.0

.2

0 10 20 30 40

GDP Growth Response

to a Money Market Rate Shock

-.1

.0

0 10 20 30 40



-.2

-.1

.0

0 10 20 30 40

Output Gap Response


-.4

.0

0 10 20 30 40



-.1

.0

0 10 20 30 40



-.06

-.04

-.02

.00

0 10 20 30 40



.0

.4

.8

0 10 20 30 40



0.0

0.5

1.0

0 10 20 30 40



-.2

.0

0 10 20 30 40



29

Figure 7 Reserve Requirement Shock

-.1

.0

.1

0 10 20 30 40

GDP Growth Response

to a Reserve Requirement Shock

-.1

.0

0 10 20 30 40



-.1

.0

0 10 20 30 40

Output Gap Response


-.2

.0

0 10 20 30 40



-.1

.0

0 10 20 30 40



-.04

.00

0 10 20 30 40



.0

.1

.2

0 10 20 30 40



.0

.2

0 10 20 30 40



-.2

-.1

.0

0 10 20 30 40



30

Figure 8 Inflation Target Shock

-.025

.000

.025

0 10 20 30 40

GDP Growth Response

to an Inflation Target Shock

.00

.02

.04

0 10 20 30 40



.00

.02

.04

0 10 20 30 40

Output Gap Response


.00

.05

0 10 20 30 40



.00

.02

.04

.06

0 10 20 30 40



.000

.005

.010

0 10 20 30 40



-.01

.00

.01

0 10 20 30 40



-.04

.00

0 10 20 30 40



.00

.04

0 10 20 30 40



31

Figure 9 Real Exchange Rate Shock

-.04

.00

.04

0 10 20 30 40

GDP Growth Response

to a Real Exchange Rate Shock

.00

.02

0 10 20 30 40



.00

.04

0 10 20 30 40

Output Gap Response


.0

.1

.2

0 10 20 30 40



.00

.04

0 10 20 30 40



.00

.01

0 10 20 30 40



.00

.01

.02

0 10 20 30 40



.00

.02

0 10 20 30 40



.0

.1

.2

.3

0 10 20 30 40



32

Figure 10 Foreign Demand Shock

-.2

.0

.2

0 10 20 30 40

GDP Growth Response

to a US Aggregate Demand Shock

.0

.1

0 10 20 30 40



.0

.1

.2

0 10 20 30 40

Output Gap Response


.0

.5

0 10 20 30 40



.0

.1

.2

0 10 20 30 40



.04

.08

0 10 20 30 40



.05

.10

0 10 20 30 40



-.1

.0

.1

0 10 20 30 40



-1.0

-0.8

-0.6

0 10 20 30 40



33

Figure 11 Foreign Interest Rate Shock

-.1

.0

0 10 20 30 40

GDP Growth Response

to a US Real Interest Rate Shock

.00

.04

0 10 20 30 40



.00

.05

0 10 20 30 40

Output Gap Response


.0

.2

0 10 20 30 40



.00

.05

0 10 20 30 40



.00

.01

.02

.03

0 10 20 30 40



.00

.02

.04

0 10 20 30 40



-.02

.00

.02

0 10 20 30 40



.0

.2

0 10 20 30 40



34

Figure 12A

1000

1200

1400

1600

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Actual Potential

Bill

ion

Pe

so

s (

20

00

price

s)

Source of basic data: www.nscb.gov .ph

Philippines

Gross Domestic Product

Figure 12B

350

360

370

380

390

400

410

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

RER Equilibrium

Source of basic data: BSP; elibrary -data.imf .org

Philippines

Real Exchange Rate

35

Figure 13A

-3

-2

-1

0

1

2

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Estimated Predicted

Source of basic data: www.nscb.gov .ph

Philippines

Output Gap

-4

-3

-2

-1

0

1

2

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Lagged output gap

Expected output gap

Real interest rate gap

Real exchange rate gap

US output gap

IS Equation

36

Figure 13B

-4

0

4

8

12

16

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Actual Predicted


Philippines

Annualized Quarterly Inflation Rate

-2

0

2

4

6

8

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Expected inflation rate

Lagged inflation rate

Lagged output gap

Real depreciation rate

Inflation Equation

37

Figure 13C

3

4

5

6

7

8

9

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Actual Predicted


Philippines

Policy Interest Rate

-2

0

2

4

6

8

10

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Lagged policy rate

Neutral interest rate

Deviation from target

Output gap

Policy Equation

38

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