Philippines: Selected Issues Paper

International Monetary Fund

doi:10.5089/9781455219971.002.A002

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Philippines: Selected Issues Paper

Author: International Monetary Fund

Publication Date:: 01 Mar 2011

eISBN:: 9781455219971

Language:: English

Keywords:: ISCR; CR; revenue; production function approach; affect growth; labor market; inflation expectation; revenue effort; expenditure shock; shock crowd; unemployment rate; employment growth; Total factor productivity; Fiscal consolidation; Fiscal stance; Inflation; Private investment; Global; Asia and Pacific; analysis model; inflation development; inflation dynamics; output-gap variability; Real exchange rates; Exchange rates; Inflation targeting; Output gap

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1. This chapter estimates a forecasting and monetary policy analysis system (FPAS) model for the Philippines. The appropriate monetary policy stance depends on inflation forecasts (and expectations), the distribution of exogenous shocks affecting the economy, and the monetary transmission mechanism. The Global Financial Crisis highlighted the importance of taking into account both domestic and global shocks (and uncertainty) as well as macro-financial transmission mechanisms in the design of monetary policy in emerging markets. An FPAS model that incorporates such elements can provide a systematic framework to consider policy trade-offs under an inflation forecast targeting (IFT) regime (see Laxton, and others, 2009).

Abstract

II. Forecasting and Monetary Policy Analysis System for the Philippines¹

A. Introduction

1. This chapter estimates a forecasting and monetary policy analysis system (FPAS) model for the Philippines. The appropriate monetary policy stance depends on inflation forecasts (and expectations), the distribution of exogenous shocks affecting the economy, and the monetary transmission mechanism. The Global Financial Crisis highlighted the importance of taking into account both domestic and global shocks (and uncertainty) as well as macro-financial transmission mechanisms in the design of monetary policy in emerging markets. An FPAS model that incorporates such elements can provide a systematic framework to consider policy trade-offs under an inflation forecast targeting (IFT) regime (see Laxton, and others, 2009).

2. In the Philippines, with the output gap closing in 2010 in light of the rapid economic recovery, and real policy rates significantly below pre-crisis levels, the monetary stance is assessed to be accommodative. An inflation forecast conditional on the WEO forecast for the U.S. economy, global oil prices, and estimated distributions for stochastic shocks in the Philippines suggests that the monetary stance may need to be tightened in the near term for inflation to remain within the Bangko Sentral ng Pilipinas’s (BSP) target range over the policy horizon.

B. BSP’s Inflation Targeting Framework

3. The Philippines’ shift to an IFT regime in 2002 has so far yielded low and stable inflation. The national government, through the Development Budget Coordinating Committee (DBCC), sets the inflation target based on the headline consumer price index (CPI) two years ahead in consultation with the BSP. The BSP has full powers over and responsibility for the announcement of the inflation target and the determination of appropriate monetary policy to achieve the target. The target was generally achieved during 2002–07, with average inflation very close to the midpoint average of the target. In early 2008, the extraordinary increase in global commodity prices influenced inflation developments and was closely followed by the global crisis.²

4. The inflation forecast is a major factor considered by the BSP when deciding whether monetary policy instruments should be adjusted to attain the inflation target.³ The government’s targets for annual headline inflation have been set at 4.0 percent for 2011 and 2012 with a tolerance interval of ± 1.0 percentage point (BSP, 2010). In July 2010, the BSP extended through 2014 the 3-5 percent inflation target to help anchor medium-term inflation expectations. According to the BSP, baseline inflation forecasts are generated from the BSP’s single equation model and the multi-equation model.⁴ The FPAS model estimated below could be viewed as a complementary approach to those econometric approaches.

C. FPAS Model

5. The monetary policy analysis is conducted by extending to better capture Philippine specific factors the small “New Keynesian” macroeconomic model of Berg, Karam, and Laxton (2006a).⁵ The FPAS model is a stripped down version of a stochastic general equilibrium (DSGE) model with rational expectations. In recent years, the macroeconomic literature has used DSGE models and small New Keynesian models to analyze economic behavior and to forecast future developments. The DSGE models are based on theoretical underpinnings and have been found to be useful for analyzing the effects of structural changes in the economy, as well as the effects of longer-term developments such as persistent fiscal and current account deficits. On the other hand, by virtue of their relatively simple structure, small New Keynesian models have been used for forecasting and policy analysis purposes in central banks and by IMF country desks. A number of inflation-targeting central banks have used similar models as an integral part of their FPAS (see Laxton and others, 2009).

6. To capture the commodity dependence and importance of the banking system in the Philippines, the baseline model is extended to incorporate oil prices and macrofinancial linkages through a credit conditions variable (Carabenciov and others, 2008). In addition, fiscal shocks are introduced to the FPAS model as in Honjo and Hunt (2006). The model features a small open economy including forward-looking aggregate supply and demand with microfoundations and with stylized (realistic) lags in the different monetary transmission channels. External shocks from the rest of the world are captured here by U.S. growth. Output developments in the rest of the world feed directly into the small economy as they influence foreign demand for Philippine products. Changes in foreign inflation and interest rates affect the exchange rate and, subsequently, demand and inflation in the Philippines (Figure 1).

7. The model is estimated using Bayesian techniques based on prior distributions for the parameters from cross-country work and assumptions about the Philippine economy. Bayesian estimation in a situation of a relatively small sample size (which is almost always the case for macro time series data) helps ameliorate the problems of classical econometric estimation, which often gives macro model results that are inconsistent and faced with simultaneity challenges. This is a particularly important aspect for the Philippines where there was a structural change in 2002 when BSP switched to an IFT framework, necessitating a sub-sample estimation from 2002 to 2010 to confirm the robustness of the full sample estimates. The model is estimated based on quarterly data from 1996 to 2010 using prior empirical knowledge about the parameters of interest for the Philippines or cross-country studies on emerging markets. All variables are seasonally adjusted using the X12 filter, with the exception of the interest rate and the exchange rate, and expressed in “gap” terms, defined as deviations from a Hodrick-Prescott time trend.

8. The parameter estimates shed new insight into the monetary transmission mechanism in the Philippines, the role of domestic and global shocks, and the weights placed on inflation, the output gap, and the exchange rate in an open-economy Taylor-rule. The model has four behavioral equations: (1) an aggregate demand or IS curve that relates the level of real activity to expected and past real activity, the real interest rate, the real exchange rate, foreign demand, the fiscal stance, and financial conditions; (2) a price setting or Phillips curve that relates inflation to past and expected inflation, the output gap, fuel prices, and the exchange rate; (3) an uncovered interest parity condition for the exchange rate, with some allowance for backward looking expectations; and (4) an open-economy Taylor-rule for setting the policy interest rate as a function of the output gap, expected inflation, and the exchange rate.

(1) The aggregate demand equation and results are as follows:

\begin{matrix} y g a p_{t} = β_{l d} y g a p_{t + 1} + β_{l a g} y g a p_{t - 1} - β_{R R g a p} R R g a p_{t - 1} + β_{Z g a p} Z g a p_{t - 1} + β_{R W y g a p} y g a p_{t}^{R W} \\ - β_{F B g a p} F B g a p_{t - 1} + β_{B L} η_{t} + ɛ_{t}^{y g a p} \end{matrix}

where ygap is the output gap, RRgap the real interest rate gap, zgap the real exchange rate gap, ygap^RW the output gap in the United States, FBgap the fiscal gap, η lending conditions based on the ratio of bank credit to GDP, β a series of parameters attached to these variables, and ε ^ygav an error term that captures other temporary exogenous demand shocks. (An extension of the model to include fiscal and lending conditions is detailed in Appendix 1).

Berg, Karam, and Laxton (2006b) suggest that the value of β_lag will lie between 0.5 and 0.9, with a lower value for less advanced economies more susceptible to volatility. The coefficient of 0.68 for β_lag is comparable to other emerging markets. The lead of the output gap (β_ld) is typically small, between 0.05 and 0.15, and the estimated value for the Philippines is at the midpoint of that range. The parameter β_RRgap indicates the effectiveness of the monetary transmission mechanism, while β_zgap and β_RWygap depend on the importance of the exchange rate channel and the degree of openness. The posterior estimates of β_RRgaap and β_zgap suggest that the interest rate effect on aggregate demand is stronger than the exchange rate effect possibly reflecting the importance of factors beyond the exchange rate for competitiveness (e.g., costs of doing business). The value for β_RWygap is high, as would be expected given the Philippines’ non-diversified export dependence. The fiscal gap and bank lending conditions have the expected negative and positive impact, respectively, on aggregate demand. The former highlights a potential role for counter-cyclical fiscal policy and the latter the importance of a credit channel and financial accelerator effects.

(2) The Philips curve equation and results are as follows:

π_{t} = α_{π l d} π 4_{t + 1} + (1 - α_{π l d}) π 4_{t - 1} + α_{y g a p} y g a p_{t - 1} + α_{z g a p} (z_{t} - z_{t - 1}) + α_{o} (π_{t}^{o} - π^{o}) + α_{o l a g} (π_{t - 1}^{o} - π^{o}) + ɛ_{t}^{π}

where π4_t+1is the four-quarter ahead inflation rate (year/year), π_t 4_t-1the four-quarter lagged inflation rate, ygap the output gap, z_t - z_t-₁ the real depreciation, α the parameters, $π_{t}^{o}$ domestic fuel price inflation, and $ɛ_{t}^{π}$ an error term.

The α_πld parameter in the headline inflation equation determines the forward-looking component of inflation (while its inverse 1 -α_πld determines the backward-looking component). The parameter value can be interpreted as depending in part on the credibility of the central bank and in part on institutional arrangements regarding wage indexation and other price-setting mechanisms. A high value of α_πld, close to 1, would suggest that small changes in monetary policy cause large changes in price expectations. The α_ygap parameter depends on the extent to which output responds to price changes and, conversely, how much inflation is influenced by real demand pressures, and is typically between 0.25-0.50. This parameter ultimately depends on the “sacrifice ratio” (the loss of output necessary to bring down inflation) and is estimated to be relatively high in the Philippines at 0.45. The α _zgap parameter represents the short-term pass-through of (real) exchange rate movements into prices, and depends on trade openness, price competition, and monetary policy credibility. The exchange rate pass-through coefficient is estimated to be relatively low in the Philippines taking into account other factors such as global oil prices, which feed into higher inflation with a short lag.

(3) The uncovered interest parity equation and results are as follows:

z_{t} = δ_{z} z_{t + 1} + (1 - δ_{z}) Z_{t - 1} - [R R_{t} - R R_{t}^{R W} - ρ^{*}] / 4 + ɛ_{t}^{z}

where z_t is the real exchange rate (an increase represents a depreciation), RR_t the real interest rate, $R R_{t}^{R W}$ the U.S. real interest rate, ρ* the historical average risk premium on the domestic currency, δ_z the smoothness parameter, and $ɛ_{t}^{z}$ an error term. This equation, an uncovered interest rate parity condition, posits that the real exchange rate is a function of the expected real exchange rate (the first two terms), the real interest rate differential (the currency risk premium), and a disturbance term.

The δ parameter in the real exchange rate equation determines the relative importance of forward- and backward-looking real exchange rate expectations. If δ is equal to 1, the exchange rate behaves as in the Dornbusch overshooting model (the real exchange rate is a function of the future sum of all real interest rate differentials). The estimated coefficient of 0.75 makes monetary policy potentially a more effective tool, though the low exchange rate pass-through in the Philippines somewhat reduces its efficacy.

(4) The open-economy Taylor-rule and results are as follows:

R S_{t} = γ_{R S l a d} R S_{t - 1} + {(1 - γ_{R S l a g})}^{*} (R R_{t}^{*} + π 4_{t} + γ_{π} [π 4_{t + 1} - π_{t + 4}^{*}] + γ_{y g a p} y g a p_{t} + γ_{z g a p} z g a p_{t}) + ɛ_{t}^{R S}

The γ parameters in the monetary policy rule equation depend on the speed and extent to which the monetary authorities adjust the nominal interest rate, and the relative importance of the inflation target versus the real activity target. There is a high degree of interest smoothing in the Philippines but the BSP does aggressively respond to inflation forecasts (expectations) above the targeted level. It is common for central banks to pay some attention to real activity even in a “pure” inflation targeting framework and, thus, for the γ_ygap coefficient to be greater than zero. We choose a value of 0.5 as a prior, in line with other countries. This is borne out in the Philippines where the weight on real activity is comparable to other emerging markets. γ_zgap reflects the weight on the real exchange rate, which has been observed to be quite significant in emerging markets (see Stone and others, 2009). The estimated coefficient indicates that the BSP appears to place some weight on exchange rate developments in conducting monetary policy, although the coefficient is less than many other emerging markets.

9. The impact of global demand shocks on the economy is significant. As far as the impulse response functions are concerned (Appendix Figures 1–3), the model shows reasonable and expected patterns. Global demand shocks have a significant impact on output and inflation dynamics, requiring a monetary policy response to help stabilize the economy. Domestic demand shocks also have a large impact on aggregate demand and inflation developments.

10. The variance decomposition of all stochastic shocks suggests that demand and supply shocks are nearly equally important in accounting for inflation dynamics. This equality contrasts somewhat with the perceived dominance of supply-side factors in Asia among many policy-makers and results of pure empirical vector-autoregression (VAR) based studies, highlighting the need for more systematic and country-specific analyses to better identify and interpret shocks.

11. Using the estimated parameters and distributions for the stochastic shocks, solutions are derived for the variability in inflation and the output gap under alternative monetary policy reaction functions. First, simulations of the FPAS model search for the “optimal” values for the coefficients of the Taylor rule by minimizing a loss function of inflation and output variability. The loss function takes the form of a standard quadratic form given by:

L = Σ_{t = 0}^{\infty} λ_{π} {(π_{t} - π^{T})}^{2} + λ_{y} {(y g a p_{t})}^{2}

where λ_π and λ_y are the relative weights on inflation and output-gap variability and π^t is the inflation target. For simplicity, the weights are assumed to be equal. Second, the analysis compares the standard deviation of inflation and the output gap of the estimated model with one with the “optimal” coefficients of the Taylor rule given the same size and types of shocks faced by the economy. The simulations suggest that a greater weight on inflation and a smaller weight on output and the exchange rate would help to reduce macroeconomic volatility (text table).

12. The model-based forecast suggests that in order for inflation to remain within the BSP’s target range, the monetary stance would need to be tightened over the next two years. The analysis forecasts inflation and other real economy factors (such as the output gap) conditional on the WEO forecast for the U.S. economy, global oil prices, and estimated distributions for stochastic shocks including supply-side factors in the Philippines.⁶ The simulations based on the estimated Taylor-rule suggest that over the next two years the BSP would have to unwind the monetary easing that it put in place during the crisis in order to keep headline inflation within the 3–5 percent target range (text figure).

D. Conclusion

13. The forecasting and monetary policy analysis model of the Philippine economy presented in this chapter could be helpful to guide monetary policy decisions. The model estimates shed light on the monetary transmission mechanism and impact of domestic and global shocks. The out-of-sample forecast performance of the model is good. The model could also be used for risk-assessments of the baseline inflation forecast and counter-factual scenarios conditional on the global economic outlook and alternative policy reactions functions. Preliminary simulations suggest that the appropriate response of the BSP to keep inflation within target range would be to roughly unwind its crisis-related monetary easing over the next two years. Philippines’ susceptibility to global demand conditions also suggests that the timing of the monetary normalization could be recalibrated in the event of renewed global turmoil.

14. Further work could extend the FPAS model for the Philippines in a number of directions. First, the impact of shocks to potential output could be explicitly accounted for, rather than implicitly gauged through shocks to the output gap. Second, the implications of food prices for “core” inflation and inflation expectations could be further explored, given the large weight of food in the Philippines consumer price index. Finally, the recent surge in capital inflows to the Philippines could warrant a more explicit modeling of their role, possibly in a full-DSGE model.

Appendix

This chapter extends the workhorse model of Berg, Karam, and Laxton (2006b) to better capture Philippine-specific factors:

First, the large and persistent fiscal deficit in the Philippines could be an important source of shocks to the economy. Therefore, we include the fiscal gap in the output gap equation and a fiscal rule to simultaneously capture the possibility of countercyclical fiscal policy and ensure a stable public debt, as in Honjo and Hunt (2006). The specification is:
$F B g a p_{t} = θ_{y g a p} y g a p_{t - 1} - θ_{D g a p} D g a p_{t + 1} + ɛ_{t}^{F B g a p}$
where FB is the fiscal balance gap, and Dgap the deviation from the government’s debt target. The foreign sector does not have an endogenous fiscal policy reaction function. Debt is defined as the cumulative fiscal balance, and the debt target is set equal to zero, which implies the equilibrium fiscal balance is zero and there is no debt accumulation over time. This can be thought of as normalization around a nonzero, but constant, ratio of public debt to GDP.
Second, this chapter extends the model to include macro-financial linkages as in Carabenciov and others (2008). The global financial crisis and great recession have highlighted how financial developments can affect the real economy, particularly through “financial accelerator” effects (Bernanke et al., 1999). Given the dominance of banks in the Philippines, the analysis focuses on bank lending conditions, as in Carabenciov and others 2008). Bank lending (BL) is a function of BL* (defined as the equilibrium level of BL), the real interest rate gap, and banks’ expectation of the economy four quarters ahead. The output gap is explained by the same variables as in equation (1) above as well as by a distributed lag of $ɛ_{t}^{B L}$ The values of the coefficients imposed on the distributed lag of $ɛ_{t}^{B L}$ are intended to react to a pattern in which an increase of $ɛ_{t}^{B L}$ loosening of credit conditions) is expected to positively affect spending by firms and households in a hump-shaped fashion, with an initial buildup and then a gradual rundown of the effects as in Carabenciov and others (2008). The specification in this case is:

B L_{t} = B L^{*} - χ_{R R g a p} R R g a p_{t - 1} + χ_{y g a p} y g a p_{t + 4} + ɛ_{t}^{B L}

where η is the distributed lag of $ɛ_{t}^{B L}$ and is calculated as:

η_{t} = 0.04 ɛ_{t - 1}^{B L T} + 0.08 ɛ_{t - 2}^{B L T} + 0.12 ɛ_{t - 3}^{B L T} + 0.16 ɛ_{t - 4}^{B L T} + 0.20 ɛ_{t - 5}^{B L T} + 0.16 ɛ_{t - 6}^{B L T} + 0.12 ɛ_{t - 7}^{B L T} + 0.08 ɛ_{t - 8}^{B L T} + 0.04 ɛ_{t - 9}^{B L T}

Third, the chapter introduces a pass-through coefficient pt to capture the pass through of international oil price to local fuel prices.

π_{t}^{o} = (π_{t}^{R W o} + 4 * (z_{t} - z_{t - 1})) * p t

where $π_{t}^{R W o}$ is the international oil price inflation.

References

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Prepared by Shanaka J. Peiris (APD).

The BSP’s monetary policy framework provides for exemption clauses to recognize the fact that there are limits to the effectiveness of monetary policy and that there may be occasional breaches owing to factors beyond the control of the central bank (see Guinigundo, 2010).

In conveying to the public the views of the BSP, an assessment of the output gap is presented along with the inflation forecast. The BSP also assesses inflation expectations, evidence of second-round effects, and the yield curve among a host of critical factors (see Guinigundo, 2010).

See McNelis and Bagsic, (2007) for details on the BSP inflation forecasting models and performance.

This contrast with the financial programming approach, which emphasizes the role of monetary aggregates in analyzing the monetary sector (see Berg, Karam, and Laxton, 2006, for a discussion of the different approaches).

The model-based forecast provides a relatively good out-of-sample forecast (compared to a bayesian VAR beyond two lags).

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