Draft – Not to be quoted

Asymmetry in Inflation and Output Gap interactions

-Evidence of a Nonlinear Phillips Curve for India

Honey KarunNational Institute of Public Finance and Policy, New Delhi, India. The author can be contacted at honeykarun@gmail.com

Draft- Not to be quoted

Asymmetry in Inflation and Output Gap interactions

-Evidence of a Nonlinear Phillips Curve for India

Honey Karun

Abstract

Most empirical studies on the Phillips curve are confined to ‘linear’ model

specifications. Using quarterly data for the period 1996-2014, the study finds

evidence for a convex shaped Phillips curve when the economy is overheated, i.e.,

when the economy is above its potential output. The results imply that the cost of

deliberating disinflation policy by the central bank is higher than the policy of pre-

empting inflation. Since there is no evidence for a Phillips curve when the economy is

operating below its potential output, inflation targeting through interest rate channels

may not be able to stabilize inflation to its previous levels.

Keywords: Inflation targeting, Phillips Curve, Monetary Policy, New Monetary Framework

JEL Classification: E31, E52, E58.

The author is a project associate at National Institute of Public Finance and Policy. The views here are personal and not of the organization.

1

Inflation targeting is a framework where a monetary authority explicitly adopts

stabilizing inflation as a core objective (with output stability as a secondary objective-

a slightly flexible form of inflation targeting) in medium to long term (Bernanke et.al.

1999; Mishkin and Schmidt-Hebbel 2001). Inflation targeting is argued to be a strong

alternative with transparency of a rule and the flexibility of discretion for the conduct

of monetary policy in recent times (Svensson 1997; Bernanke and Mishkin 1997).

Svensson (2007) and Woodford (2007) showed that flexibility in inflation targeting

allowing for central banks’ judgment and model uncertainty with more transparency

in their operational objectives and communication is the optimal monetary policy.

Many countries have adopted inflation targeting as the prime objective of the conduct

of monetary policy in the past two decades (see Reserve Bank of India 2014,

Appendix Tables II.2A, p.85 and II.3, p.93). The central bank of India (Reserve Bank

of India (RBI)) has adopted the inflation targeting framework in 2014.1

Notwithstanding that, the debate on whether an inflation targeting framework is

suitable for the conduct of monetary policy in India is far from being settled to a

consensus (Gupta and Sengupta 2014; Bhattacharya and Patnaik 2014; Azad and Das

2013).

One of the structural equations in a new Keynesian model of inflation targeting

framework, is the existence of Phillips Curve, which requires that there exists a trade-

off between inflation and output gap and the relationship is linear. This implies, that,

if these two preconditions are not validated then it may raise questions about the

effectiveness of monetary policy. This paper, thus, attempts to test the possibility of

1 The Expert Committee headed by the RBI Deputy Governor Urjit R Patel to Revise and Strengthen

the Monetary Policy Framework was set up in 2013, to recommend what needs to be done to revise and

strengthen the current monetary policy framework with a view to, inter alia, making it transparent and

predictable. In February 2015, the RBI and the Government of India has entered into an agreement on

‘New Monetary Policy Framework emphasizing the need to strengthen inflation targeting in India.

2

non-linearity in the new Keynesian Phillips Curve in Indian context. Testing for the

existence of the curve is significant, as the trade-off between inflation and output gap

is vital in determining the output cost of fighting inflation. Moreover, the output cost

of deliberate disinflation policy by a central bank depends on the shape of Phillips

curve if the economy is operating below its trend level (Filardo 1998).

The paper is organised into IV sections. Section I discusses the analytical framework

of the Phillips Curve specifications and also provides a brief literature review.

Sections II interprets data, provides the model specification and discusses the findings

for non-linear specification of Phillips curve in India. Sections III argues the

implications of the estimated results and attempts to draw some policy implications

for monetary policy in India. Section IV concludes.

I Analytical Framework

The functional form of the new Keynesian Phillips Curve (NKPC) (following

Woodford, 2003) can be written as:

π t=β Et (π¿¿ t+1)+k ( x t )+μ t ¿ (1)

where, π t is inflation, Et is the expectation at time period t about inflation at time t+1,

x t is the output gap, and μt is the cost push shocks. This implies that the slope of the

curve is constant and, therefore, independent of the stage of the business cycle and the

speed of the disinflation. The linear specification, here, depends on the following

components: inflation expectations, the extent of resource or capacity utilization i.e.

economic activity in an economy which is captured through output gap, and the

supply side factors which may or may not be exogenous at times.

Thus, in the absence of a supply shock, in the model above, inflation inertia can be

explained through the dynamics of inflation expectations. This implies that if a

3

monetary authority announces a targeted inflation rate, combined with the assumption

that the economic agents form rational expectations; and the expected inflation term

above maintains one to one relation with actual inflation, the monetary authorities can

achieve the target without making any adjustment to the output. Apart from the fact

that the linear models are relatively easy to be estimated empirically (Gordon 1997),

an underlying assumption in linear Phillips curve states that the impact of output gap

on inflation does not vary with the initial levels of inflation, and other indicators.

Thus, the possibility of any asymmetry in the shape of the curve is completely ruled

out (Dupasquier and Ricketts 1998).

The basic functional form of non-linear Phillips curve (Clark et.al 1995) can be as

follows:

π t=π t−1+π t+1e +β ¿ (2)

where, gapt¿ is the output gap and gappost

¿ represents the positive values of output

gap. π t−1+π t+1e , here captures the backward and forward looking expectations of the

inflation. The above equation can be estimated as a piecewise linear specification with

a possible kink at the point where the output gap starts exerting an upward pressure on

inflation.

Filardo (1998a) extended this specification and argues that the relationship can be

reviewed under three different situations or regimes i.e. when the output gap is well

below its trend (weak) or negative output gap, well above trend (overheated) or

positive output gap, and more importantly a third regime where output is moving

around its trend value (balanced). To define such regimes, one needs a threshold

parameter say α which splits or classifies the output gap into the above mentioned

regimes. In other words, the regime will be a weak one if output is more than α

4

percent below its trend, balanced when output is within α percent of trend on both

sides, and finally, overheated when the output is more than α percent above its trend.

The equation, thus, can be rewritten as:

π t=π te+βweak∗outputgap+βbalanced∗outputgap+β¿heated∗outputgap+εt

(3)

In the above specification, the Phillips curve can have shapes as illustrated below

(Figure1).

Figure 1. Possible empirical shapes of Nonlinear Phillips Curve

It can be inferred from Figure1 above that such form of non-linear curve is

conditional on two components; one the slope coefficients i.e. beta and more

importantly the size of regime which is dependent on the threshold parameter α. The

slopes in different regimes measure the degree of sensitivity of inflation-output

relation under the three regimes. Filardo (1998b) argued that the Phillips curve need

not be necessarily linear and depend on the sensitivity of inflation with economic

activity. Hence, there could be different possibilities where a Phillips curve is

concave, convex or flat. Further, there is a possibility that an economy faces a curve

which could be a combination of either of the above shapes.

5

βoverheated

-α

π−πe

Output Gap

βoverheated

Piecewise convex

-α

Piecewise concave

π−πe

αOutput

Gap

βbalanced

βweak

βweak

βbalanced

α

Azad and Das (2013a) derived the Phillips curve from a representative price setting

firm’s behaviour2 and argued that the profit margin of a firm depends on the market

structure. In such case, the profit margin of a firm may be a nonlinear function of

capacity utilisation. Hence, the shape of the curve would vary depending upon the

profit margin arising out of the market structure.

I.1 Empirical Evidence on Nonlinear Phillips Curve

Despite the theoretical discussions on the possibility of a nonlinear Phillips curve

relationship, the empirical importance to the nonlinearity of the curve has been

limited in the literature. Ball (1994) argued that though the limitations of linearity

were potentially empirically important, it did not account for asymmetries in wage-

price flexibility, credibility, and incomes policies which cause the sensitivity of

inflation to output to depend on whether output is above or below its trend levels.

Clark and Laxton (1997) wrote, “….the question of whether the Phillips relationship

was a straight line or curve was eclipsed in part because in the 1960s and 1970s the

dominant issue was the extent to which the relationship was stable…..the attention

shifted to the expectations augmented Phillips curve and the determinants of the

NAIRU, as well was the factors generating the apparent rise in the NAIRU during the

1970s and 1980s in many industrial countries…”.

In our attempt to search for relevant literature, most of the studies on the nonlinear

Phillips curve are limited to developed economies (Clark et.al 1996; Eisner 1997;

Stiglitz 1997; Dupasquier and Ricketts 1998b; and Filardo 1998c) only. In Indian

context, Table.1 details some of the literature on Phillips curve estimation in India.

2 Dupasquier and Ricketts (1998a) provided a brief theoretical survey of models based on price setting behaviour which determines the nonlinear shapes of the Phillips curve.

6

Table 1. Recent Literature on Phillips curve estimation in IndiaAuthor and Year Framework Findings

Kapur (2013) Linear Existence of the Phillips curve for the period

1996–2012

Patra and Kapur

(2012)

Linear Existence of the Phillips curve for the period

1996–2009

Mazumder (2011) Linear Existence of the Phillips curve for the period

1970–2008

Singh et.al (2011) Linear Existence of Phillips Curve in India between

the first quarter of 2004 and the first quarter

of 2009 only after controlling for supply

shocks

Patra and Ray

(2010)

Linear Existence of Phillips Curve in India for the

period 1997–2008

Paul (2009) Linear Existence of the Phillips curve for the

industrial sector for the period 1956–2007

Dua and Gaur

(2009)

Linear Existence of the Phillips curve for the period

1996–2005

Srinivasan et al.

(2006)

Linear No evidence for Phillips curve for the period

1994-2005

Azad and Das

(2013b)

Nonlinear Evidence for Phillips curve for the period

1961–2008

As it is evident from the Table.1, the literature in India is primarily focused on

existence of a linear curve. Azad and Das (2013c) is the only study which discussed

7

the nonlinear nature of the Phillips curve in the context of developing countries and

argue for a horizontal Phillips curve in such countries.

II Data and Model Specification

The data is organised from national and international databases3. The equations are

estimated using quarterly data for the period 1996–20144 as the quarterly data for

GDP in India is available from 1996 only. This study chooses to use Wholesale Price

Index (WPI) based inflation as key variable for the analysis. The implicit assumption

is that it would be useful to analyse the monetary policy stance of RBI keeping the

measure of inflation that has been used till recently as headline inflation index for

monetary policy making process. Thus, the variables considered for this study are:

WPI (year-on-year, y-o-y) based on headline wholesale price index; output gap

(constructed as deviation of seasonally adjusted actual real GDP from its potential

(trend) real GDP using Hodrick–Prescott (HP) filter). Following some of the recent

studies(Kapur 2013a; Kotia 2013; Mazumder 2011a; and Dholakia and Sapre 2012)

the supply shock variables chosen for this study are: variation (y-o-y) in global non-

fuel commodity price index; variation (y-o-y) in global all commodities price index;

variation (y-o-y) in international crude oil prices5; REER is variation (y-o-y) in the

36-currency trade-weighted nominal effective exchange rate index of the Indian

rupee; RAIN is deviation of actual rainfall during July from its normal level during

3 Central Statistical Organization, Ministry of Statistics and Programme Implementation; Database on Indian Economy (Reserve Bank of India); Office of the Economic Adviser to the Government of India; Indian Meteorological Department and International Monetary Fund.

4 The data has been limited to year 2014 as the Ministry of Statistics & Programme Implementation released the new series of national accounts, revising the base year from 2004-05 to 2011-12 in January ,2015. Further, the Ministry has stated that the improvements in methodology for estimation has considerable effects on the quarterly estimates of GDP. This makes it difficult to create a long time series with the new base year. Therefore, the data for GDP in this study is at 2004-05 prices only and the latest quarterly estimates were available till Q4’2014 only. 5 The oil prices in India have been deregulated and integrated to global price movements in FY2012-13. Thus, though this variable is a significant shock variable, it may not reveal significant impact on the domestic inflation empirically.

8

July. FE refers to forecast error in observed inflation and expected inflation which is

captured through lags of inflation (Gordon 1998; Kapur 2013b).

The non-linear model used in the study can be specified as:

π t=π te+δ (last perio d ' sforecast error )+βweak∗outputgap+βbalanced∗outputgap+ β¿h eated∗outputgap+ε t

(4)

where, the first two variables on the right hand side captures the persistence of the

past inflation, also known as inflation inertia.

The output gap here is defined as,

gap¿¿

ot h erwise=0 (5)

gap¿¿

ot h erwise=0 (6)

gap¿¿

ot h erwise=0 (7)

In the above specification, the coefficient estimates are defined such that,

βweak=βbalanced+β¬¿ ¿ (8)

β¿heated=βbalanced+β pos (9)

where,

β¬¿=lagged ouput gap∗I¬¿¿ ¿ and; (10)

β pos=lagged ouput gap∗I pos (11)

In the above equations, the indicator functions associate with the particular regime

(weak and overheated) and take on a value of 1 if the output gap data come from their

respective regimes. The use of such indicator functions allows the three regimes to be

connected at common knots which are defined by α here. A critical question here

arises is what should the threshold parameter α be. This parameter is usually

9

estimated by searching over a grid (similar to threshold autoregressive estimations)

through solving the following:

argmax∝ g(∝;ot h er parameters)

(12)

subject to constraints that the slope coefficients are not statistically negative.

Filardo (1998d) estimated the value of α to be 0.9. For this study, we used this value

as given. As it is clear from the Figure2, the threshold value of 0.9, fairly divides the

sample with maximum size for the balanced regime.

1996

-97:

Q1

1996

-97:

Q4

1997

-98:

Q3

1998

-99:

Q2

1999

-00:

Q1

1999

-00:

Q4

2000

-01:

Q3

2001

-02:

Q2

2002

-03:

Q1

2002

-03:

Q4

2003

-04:

Q3

2004

-05:

Q2

2005

-06:

Q1

2005

-06:

Q4

2006

-07:

Q3

2007

-08:

Q2

2008

-09:

Q1

2008

-09:

Q4

2009

-10:

Q3

2010

-11:

Q2

2011

-12:

Q1

2011

-12:

Q4

2012

-13:

Q3

2013

-14:

Q2

-4

-3

-2

-1

0

1

2

3

Year

Out

put G

ap

Figure 2. Output gap regimes

This implies, that the Indian economy is considered overheated and weak (in this

paper) only if the deviation of trend output is significantly large (i.e. at least or more

than 1%).

II.1 Interpreting Results

The estimated results based on above specification are presented in the Table 2 and 3.

Column 2 reports the pure nonlinear Phillips curve or the base line model without any

supply shocks. The subsequent columns report the results for different supply shocks

introduced to the model. The results of the baseline model were in sharp contrast to

10

Overheated Regime

Weak Regime

Balanced Regime

the conventional arguments of existence of a linear Phillips curve in India. The results

indicated that the relationship holds true only if the economy was overheated. Thus, a

1% change in output in an overheated economy led to 67 basis points change in

inflation with a lag of one quarter. The relationship was statistically insignificant for

both weak and balanced regimes. This has strong implications to question the ability

of monetary policy stance of the RBI if it believed the relationship (linear) to hold

true for India. The direction of the coefficients for weak and balanced regimes were

on expected lines but as can be noted from the table, both were statistically

insignificant. Further, the inflation was persistent and took into account its past. The

lagged inflation impacted the inflation by 51 basis points. The forecast error had a

strong impact of almost 325 basis points which indicated that the expectations were

highly responsive to current economic conditions and were incorporated into actual

inflation contemporaneously. Thus, the inflation inertia played a critical role in

explaining the inflation dynamics in India.

11

Table 2. Estimation Results of Nonlinear Phillips CurveVariables Reg.1 Reg.2 Reg.3 Reg.4 Reg.5 Reg.6

Constant 2.410* 1.740* 2.011* 1.702* -3.975 2.595*

(-5.16) (3.95) (4.88) (3.33) (-1.11) (5.55)

WPI(-1) 0.515* 0.604* 0.552* 0.605* 0.479* 0.497*

(-6.64) (8.44) (8.18) (7.51) (6.09) (6.51)

Forecast error -

3.250*

-

2.086*

-

2.185*

-

2.664*

-

3.284*

-3.503*

(-5.68) (-3.64) (-4.03) (-4.56) (-5.83) (-6.08)

Output Gap (in weak

regime)

-0.131 -

0.1940

-0.226 -0.232 -0.153 -0.090

(-0.64) (-1.06) (-1.26) (-1.17) (-0.76) (-0.45)

Output Gap (in

balanced regime)

0.247 0.156 0.3112 0.129 0.262 0.074

(-0.58) (0.41) (0.84) (0.31) (0.42) (0.17)

Output Gap (in

overheated regime)

0.679* 0.383 0.676* 0.380 0.696* 0.695*

(2.51) (1.54) (2.89) (1.36) (2.61) (2.62)

Global inflation (all

commodities)

- 0.031* - - - -

(4.35)

Global inflation (non-

fuel commodities)

- 0.046* - - -

(4.77)

Change in oil prices - 0.014* - -

(0.78)

REER - - 0.065*

*

-

(1.8)

Rain (-1) - - - 0.037*

(1.93)

R squared 0.73 0.79 0.80 0.76 0.75 0.75

Adjusted R squared 0.71 0.78 0.78 0.74 0.72 0.72

dw stat 1.77 1.74 1.73 1.71 1.86 1.76

White test 0.52 0.14 0.55 0.33 0.54 0.46

(*) denotes 5 % level of significance. (**) denotes 10 % level of significance

12

Table 3. Estimation Results of Nonlinear Phillips CurveVariables Reg.7 Reg.8 Reg.9 Reg.10 Reg.11 Reg.12

Constant 1.934* 4.966 5.557 1.746* 4.559 4.685

(4.02) (1.2) (1.35) (3.77) (1.21) (1.2)

WPI(-1) 0.580* 0.598* 0.588* 0.587* 0.606* 0.605*

(7.71) (7.53) (7.48) (8.04) (7.82) (7.57)

Forecast error

-2.043*

-

1.883*

-

2.110* -2.050*

-

1.922*

-

1.943*

(-3.56) (-3.06) (-3.39) (-3.72) (-3.32) (-3.26)

Output Gap (in weak

regime)

-0.155 -0.138 -0.107 -0.259 -0.258 -0.249

(-0.83) (-0.73) (-0.57) (-1.44) (-1.43) (-1.26)

Output Gap (in

balanced regime)

0.195 0.192 0.054 0.251 0.253 0.300

(0.51) (0.5) (0.14) (0.68) (0.68) (0.76)

Output Gap (in

overheated regime)0.461**

0.448*

*

0.458*

*0.545* 0.541* 0.533*

(1.77) (1.71) (1.77) (2.13) (2.1) (2.0)

Global inflation (all

commodities)

0.042* 0.049* 0.047* - - -

(3.31) (3.06) (2.97)

Global inflation (non-

fuel commodities)

- - - 0.041* 0.046* 0.046*

(3.87) (3.65) (3.46)

Change in oil prices 0.008 0.011 0.010 0.006 0.006 0.006

(1.01) (1.23) (1.13) (1.23) (1.2) (1.17)

REER-

0.031*

*0.035 -

0.029*

*

0.030*

*

(1.74) (0.86) (1.75) (1.76)

Rain (-1)-

0.028*

*- - 0.021

(1.65) (1.05)

R squared 0.80 0.80 0.81 0.81 0.81 0.81

Adjusted R squared 0.78 0.77 0.78 0.79 0.79 0.78

dw stat 1.71 1.67 1.65 1.71 1.67 1.66

White test 0.14 0.08 0.09 0.41 0.40 0.42

(*) denotes 5 % level of significance. (**) denotes 10 % level of significance

13

Overheated Regimes (Marked between vertical lines)

-3-2

-10

12

34

56

78

91 0

1996

q1 19

97q

3 2000

q3 20

02q

1 2003

q3 20

05q

1 2006

q3 20

08q

1 2009

q3 20

11q

1 2012

q3 20

14q

11999

q1 19

99q

4 2007

q3

2008

q2

2010

q2

2012

q1

Year

Wholesale price Index (WPI) Fitted values_WPIOutput gap

Figure 3. Fitted values from Pure Nonlinear Phillips curve

The actual versus fitted values plot shows the robustness of model fit here. The

Figure.3 highlights few important inferences. First, the regimes defined in our model

and estimated coefficients explained the relationship well. For instance, if the output

gap was in an overheated regime (i.e. more than 0.9% threshold), it was associated

with rise in inflation. The gap in the actual and fitted values from 2010q3 onwards

reflected that model had not captured the movements from other factors here. Thus,

the model had to take into account the role of supply shocks and prolonged recovery

from the post 2008 crisis. Hence, the supply shocks were introduced one by one and

finally all shocks considered in study were introduced simultaneously.

The global commodity prices had a quick impact on domestic inflation in the same

quarter. The same direction and impact was visible even if all commodity prices were

replaced with global non-fuel commodity prices. Thus, a 10% increase in global

prices led to rise in domestic inflation by 30-50 basis points. The change in oil prices

had no impact, the coefficient was positive but statistically insignificant. This could

be explained through the administered oil prices policy that India had followed till

recently. However, since the integration of domestic oil prices with international

14

prices was a recent phenomenon, the exact impact would be visible only in the future.

The coefficient of real effective exchange rate (REER) showed that a 10% increase in

REER led to fall in inflation by 60 basis points. Finally, 10% rainfall shortage in the

July quarter led to rise in inflation by 20-30 basis points with a lag of one quarter.

Finally, we reported the results of regressions with all supply shocks introduced

simultaneously (Regression 12) in the Table3. The Figure4 shows the possible

asymmetric Phillips curve for India based on the estimation results of the baseline

model (without any supply shocks). The curve was essentially a convex curve in the

overheated regime. The coefficients of weak, balanced and overheated regime were -

0.13, 0.24 and 0.68 respectively which is in alignment of the slope requirements of the

convexity of the curve i.e. in case of a convex Phillips curve (βweak<βbalanced<β¿h eated).

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3

-6-5-4-3-2-10123456

Nonlinear Phillips curve

π-πe

Out

put G

ap

βoverheated =0.68

βweak=-0.13βbalanced=0.24

Figure 4. The Nonlinear Phillips curve for India

III Implications for monetary policyThe above results have significant implications for monetary policy in India for the

following reasons. The convex shape of a Phillips curve implies higher sensitivity of

inflation towards output gap as a given change in inflation needs proportionately

15

smaller output adjustment. Filardo (1998e) argued that a convex Phillips curve is

consistent for economies with capacity constraints. In such economies, as the

economy grow, the capacity constraints restrict firms from expanding their output,

thus, an increase in demand would lead to higher inflation and not in rise in output.

However, the convexity of the curve may also be possible in case an economy is weak

and the firms are facing less capacity constraints. Azad and Saratchand (2013) argued

that a Phillips curve with a horizontal segment in the manufacturing sector would not

allow a monetary policy to effectively control overall inflation. Moreover, in an

economy where speculative activities or exports of primary commodities or inflation

in imported commodities like oil significantly impact domestic inflation, the limited

role the monetary policy can play is through an indirect decline in the level of

inflation of primary commodities by compressing the demand for these commodities.

Azad and Das (2013d) also argued that one of the critical sources of inflation in the

case of developing countries like India are cost-push inflation which is driven by

either the supply shocks from international markets (for instance, oil prices), or the

primary goods producing sectors within the economy.

In Indian context, the monetary policy actions of RBI can be gauged from the Table.4.

16

Table 4. Frequency of Changes in Key monetary instruments in India Year\No. of

Times

CRR Bank Rate Repo Reverse Repo

2007-08 4 0 0 0

2008-09 10 0 8 3

2009-10 2 0 2 2

2010-11 1 0 7 7

2011-12 2 1 5 5

2012-13 3 3 3 3

2013-14 0 6 4 4

Source: Reserve Bank of India, Handbook of Statistics on Indian Economy, various

issues.

Since 2008-09, RBI has used active monetary policy stance using interest rates (repo

rate) as monetary policy transmission channel. Despite, the actions taken by RBI,

inflation has remained high as the structural drivers of inflation have been cost push

inflation in primary articles and other supply shocks. At the same time, there has been

significant decline in Index of Industrial Production in India, which signifies the

slowdown in the manufacturing sector. Combining, the two, it can be easily correlated

to the theoretical arguments put forwarded above. This provides a strong explanation

of why RBI despite its active disinflationary policy in recent years could not control

the inflation in India. One of the outcomes from the above theoretical arguments is a

prolonged stagflation episode in an economy which is what India is experiencing for

many years, where we have low growth in GDP, rising inflation and a significant

reduction in manufacturing activity or output.

IV ConclusionOur empirical results are in sharp contrast to many studies which have argued for two

things essentially: first, that there exists a positive relationship between inflation and

output gap, and second, the relationship is linear for India. This study contradicts such

17

findings and argues for possibility of nonlinearity in the relationship and that

relationship holds true only if the Indian economy is overheated. This may imply that

if the output would temporarily grow above its potential output then inflation would

also rise. If the economic policies are able to maintain the output gap around its trend

in a balanced manner, then the possibilities of inflation remaining stable are also high.

Since there is no trade-off between inflation and output gap in a regime when an

economy is operating below its trend levels, inflation targeting through interest rate

channels by a monetary authority may not be able to stabilize inflation to its previous

levels. Further, it is evident that supply shocks and inflation inertia are critical in

determining the inflation in Indian economy. Our results indicate that with increasing

global integration, the impact of such supply shocks cannot be eliminated completely

In such a situation, any cost push inflation would shift the Phillips curve upwards and

for a given loss function of a central bank, it would signal the central bank to increase

the interest rates till inflation comes down. Thus, any monetary policy stance which

takes active deflationary policy decision to curb inflation via demand deflation, will

not be able to exert any controls over inflation till the supply shocks themselves start

easing out. In such a situation, the possible outcome would be stagflation in the

economy. The convex curve also implies that the cost of deliberate disinflation policy

by a central bank is higher than a policy of pre-emptively resisting rising inflation. . In

such a scenario, it becomes more important for a monetary authority to estimate the

costs of fighting inflation.

18

Appendix-I

Data sources

Variables Source

Gross Domestic Product at Constant Prices

Central Statically Organization, Ministry of Statistics and Programme Implementation, Government of India.http://mospi.nic.in/Mospi_New/site/home.aspx

Real Effective Exchange Rate

Handbook of Statistics in the Indian Economy: Database on Indian Economy (Reserve Bank of India).http://dbie.rbi.org.in/DBIE/dbie.rbi?site=home

Wholesale Price Index

Office of the Economic Adviser to the Government of India, Ministry of Commerce and Industry.http://www.eaindustry.nic.in/

Rainfall data Indian Meteorological Departmenthttp://www.imd.gov.in/section/nhac/dynamic/Monsoon_frame.htm

Global commodityinflation

International Monetary Fundhttp://www.imf.org/external/np/res/commod/index.aspx

Oil Prices International Monetary Fundhttp://www.imf.org/external/np/res/commod/index.aspx

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