General literature

The issue of inflation has always been at the core of theoretical and empirical debates. Scholars have analysed the inflationary tendencies in various countries with different datasets, different determinants, and different econometric methodologies. So far as the impact of fiscal deficit on the inflation is concerned, ^{Hamburger and Zwick (1981}) found the inflationary nature of budget deficit while analysing the United States data for a period 1954-1976. The authors also found a budget deficit more inflationary in the Keynesian regime (1961-1974). However, ^{Dwyer (1982}) failed to document any evidence in favour of influence of debt on the money supply and overall price level in the economy. On the contrary, ^{Ahking and Miller (1985}) reported the evidence of deficit-Inflation relationships only in some specific periods. Similarly, ^{Darrat (1985}) found money growth and fiscal deficit as the significant determinants of increased price levels. Applying a neo-classical framework, ^{King and Plosser (1985}) reported the existence of a weak deficit-inflation relationship. Moreover, King and Plosser (1985) failed to uncover any such relationship while examining a mix of 12 developed and developing countries.

Examining the data from 10 developed countries, ^{Giannaros and Kolluri (1986}) found the absence of any kind of relationship between inflation, money supply, and fiscal deficit. Similarly, using the data over the period 1952-1987, ^{Protopapadakis and Siegel (1987}) also reported the existence of a feeble association between these two variables in another set of 10 developed economies. In addition, inflation is not found respond to debt growth strongly. In another study on 7 industrial economies, ^{Barnhart and Darrat (1988}) found the absence of any unidirectional or feedback Granger causality between this inflation and fiscal deficit.

In the case of 17 developing countries and for a time period from 1961 to 1985, ^{De Haan and Zelhorst (1990}) found the absence of any evidence in favour of the “fiscal dominance hypothesis” and reported that inflation reacts to deficit only during high inflation episodes. ^{Metin (1998}) documented the inflationary impact of fiscal deficit in the case of Turkey. However, analyzing the data on 3 transition economies, ^{Komulainen and Pirttilaä (2002}) reported the neutrality of fiscal deficit in explaining the inflationary tendencies in these economies. Similarly, ^{Loungani and Swagel (2001}) found a puny association between fiscal balance and inflation in the case of 53 developing countries. However, the relationship becomes stronger in the case of economies with higher average inflation. The authors further reported the non-linear influence of fiscal imbalances on inflation and found that the former affects the latter significantly only when the magnitude of the former is above 5%. ^{Domac and Yucel (2005}) while applying pooled probit estimation in the case of 15 emerging economies documented the inflationary role of fiscal deficit. Recently, ^{Nguyen (2015}) also reported the evidence of fiscal inflation in the case of eight selected economies of Asia.

Some scholars were interested to examine the deficit-inflation nexus in the case of a mix of developed and developing countries together in a panel setting and reported diversity of results. For instance, ^{Karras (1994}) found an absence of any impact of the deficit on inflation in the case of a panel of 32 developed and developing countries. Similarly, examining a panel of 90 countries, ^{Click (1998}) reported the absence of any impact of domestic debt on inflation over the period 1971-1990. However, ^{Cottarelli et al. (1998}) found the inflationary nature of deficit along with inflation persistence in the case of a mixed panel of 47 countries. Similarly, ^{Laasch et al. (2002}) found evidence in favour of fiscal inflation by examining a mixed panel of 94 economies. The study further reported the significant role of fiscal deficit in determining the seigniorage and inflation in case of high inflation periods and in case of countries with high average inflation. ^{Catao and Terrones (2005}) examined a data set comprising of 109 countries over a period of 1960-2001 to investigate the dynamic nature of interactions between these two variables. The study documented the evidence of inflationary nature of deficit figures in the case of transition economies and in the economies featured with high episodes of inflation but not in the case of advanced economies and those experiencing lower inflation levels. Similarly, examining the larger data set over the period 1962-2004 for a mixed panel of 71 countries, ^{Kwon et al. (2009}) found the positive and appreciable impact of debt growth on the inflation in case of countries plagued with massive debts but, however, the impact is low in remaining cross-sections of the panel. Likewise, ^{Lin and Chu (2013}) while analysing the data on 91 countries for the period 1960-2006, found the results more or less like that of Catao and Terrones (2005). More recently, ^{Tran (2018}) investigated the asymmetric impact of fiscal balance on the key monetary variables like interest rate, inflation, and exchange rate in the case of four emerging economies like BRIC for the period 1999-2000. The study found the long-run association between fiscal balance and monetary variables in the case of countries like Brazil and India, whereas no such association is found for China and Russia. In addition, the deterioration of fiscal balance is found to have a more powerful and significant impact on the monetary variables than when it improves.

Studies specific to India

The deficit inflation interaction has also been examined in the case of the Indian economy. ^{Sarma (1982}), ^{Jhadav (1994}) and Ragrarajan and Mohanty (1998) have found the existence of a perennial interaction between fiscal deficit and inflation in both forward and feedback directions. In addition, these studies have reported the fiscal deficit among the important determinants of inflation in India. The outcome of these studies is relevant to the prevailing conditions of that time. With the permanent blockade of ad hoc treasury bills in 1996-97, market borrowings used to finance deficit ceased as an option and mode of monetization was resorted. However, even after accounting for the monetization period of deficit financing in an extended data set analysis, ^{Ashra et al. (2004}) reported the absence of long-run association between Reserve Bank credit and fiscal deficit and between money and Reserve Bank credit to the government. The study, therefore, recommended the scrapping of fiscal deficit as a stabilization tool. Using a more recent data set and an updated methodology, Khundrakhpam and Goyal (2009) reported the significant contribution of fiscal deficit in the incremental reserve money creation and overall money expansion, which finally leads to inflation in the economy. Similarly, ^{Khundrakpam and Pattanaik (2010}) found the inflationary role of fiscal imbalances. The Reserve Bank of India (2012), also reported evidence in favour of the fiscal inflation in India. Mohanty and John (2015) applied the time-varying structural VAR procedure to analyse the time-varying impact of various determinants of Inflation in India. This study reported the inflationary impact of fiscal deficit. More recently, ^{Ramu and Gayithri (2017}) found the inflationary impact of fiscal deficit in India. Through an SVAR approach, the authors have documented the evidence of three transmission channels like consumption expenditure channel, money supply channel and import channel to portray the traverse of the impact of fiscal deficit on inflation. However, no evidence in favour of the interest rate channel is observed.

The above literature survey highlighted certain important points. On average, deficits are reported to be less inflationary in advanced and low inflation countries characterized by sound and credible monetary authorities and less fiscal dominance. However, in developing countries with higher inflation rates and high inflation episodes, the deficits are found to be inflationary. There is an inconclusiveness documented about the impact of the deficit on inflation in general and India in particular. A limitation of the existing literature is that the analytical framework adopted for the empirical exercise is largely symmetric/linear and the possibility of any asymmetric nature of the relationship is omitted. To fill this void, the present study, therefore, is an attempt to analyse the inflationary impact of fiscal deficit in a dynamic and asymmetric framework. Our study, will, therefore, enrich the existing literature along the asymmetric lines.

Data description

The selection of the variables for the empirical analysis is guided by the prevalent theoretical propositions and the existing empirical evidence. The data set is of annual frequency covering the period 1970 to 2016. The variables include Inflation (INF), Gross fiscal deficit (GFD), Money (NM), Gross domestic product (GDP), real GDP growth rate (GDPFC), Interest rate (CMR), Trade-openness (TO), Exchange rate (EXR) and Oil price Inflation (OPI). The data on all the variables (except OPI) has been taken from Database on Indian Economy (DBIE), Reserve Bank of India website, whereas for OPI we resorted to International Financial Statistics (IFS) from International Monetary Fund. INF is expressed as percentage annual variation in the Wholesale Price Index (WPI). GFD is expressed as a percentage of GDP and following ^{Catao and Terrones (2005}) and ^{Lin and Chu (2013}), GFDD is also scaled by narrow money (NM) for robustness purposes. GDPFC is the annual change in the gross domestic product at constant prices. NM is represented by the narrow money measure. TO is proxied by the sum of exports and imports both expressed as a percentage of GDP . The average of three Oil price measures in the foreign currency is first converted into rupee terms by multiplying the nominal exchange rate (EXR= ₹/$) of India with the respective oil price figures. Finally, OPI is calculated as the percentage annual variation in oil prices expressed in domestic currency. The inclusion of OPI in the analysis will portray the effect of oil price dynamism and exchange rate movements simultaneously. The definition of various variables is given in Table 1.

Table 1: Variable description 

Table 2 Descriptive statistics 

Empirical methodology

Causality examination

Time-domain causality

To examine the time domain causality between fiscal deficit and inflation, the study employs the well-known ^{Toda and Yamamoto (1995}) test, henceforth (TY) test. In the usual procedure of the conventional Granger causality test, the lagged coefficients obtained through the underlying VAR model are set equal to zero according to Wald’s principle. However, ^{Lutkepohl and Kratzig (2004}) cautions about nonstandard limiting distributions of Wald’s test statistic due to the co-integration properties of the VAR model. These nonstandard asymptotic properties follow from the singularity of asymptotic distributions. To do away with the singularity nuisance, the TY test, supplemented the original VAR model with the maximum order of integration of variables. Moreover, the test performs better in case the variables are integrated and possibly cointegrated and the data is used in levels rather than in first differences .

The TY test is performed using the specifications 1 and 2. It is to be noted that the standard Granger causality is based on VAR(k) model, wherein k is the appropriate length of lags of various variables reported by different lag selection criteria. However, in TY procedure, VAR (k + d _max) model is estimated. Here d_max denotes the maximum order of integration of the variables suspected in the process. If we set lag length k equal to p and d is reported as the highest order of integration, we proceed as:

M and N constitute the set of variables to be examined for analysis. Here zero restrictions are put to first p parameters in order to test the null of no causality against an alternative one where the causality is supposed to exist. The test statistic is usually referred to modified Wald (MWALD) and follows a 𝜒 2 distribution with a degree of freedom equal to 𝑝 and supposed to be independent of the unit roots and cointegration.

Frequency domain causality

With a view to examining the issue of causality in the short and long-run, the frequency-domain framework is incorporated. Statistically, frequency domain (FD) refers to a domain for the examination of mathematical functions or signals at various frequencies instead of time, wherein a given stationary process is decomposed into a weighted sum of sinusoidal components with a certain frequency (Ω). Though the nature of the definition of causality remains the same for both time and frequency domains, the framework of examination is different. Change of a signal over time is represented by a time-domain graph, whereas the magnitude of a signal within each frequency band over a range of frequencies is connoted by the FD graph. More simply, time denotes the happening of a variation and frequency measures the strength of that variation. Though there are many approaches available in the literature to conduct the Granger causality in frequency domain analysis, we, however, employed the recently developed framework of ^{Breitung and Candelon (2006}), hereafter (BC). This approach has the advantage of being applicable to either a stationary set of variables or integrated but not cointegrated (data used in first differences) or both integrated and cointegrated (data is used in levels). In addition, FD analysis eliminates seasonal variations in case the data series used for analysis is short and the methodology accounts for non-linearities and causality cycles i.e. causality analysis at low and high frequencies. Following the ^{Geweke (1982}) procedure.

^{Breitung and Candelon (2006}) test between the two variables M and N can be performed through a VAR derived framework as :

Let V_t = (M_t, N_t) be a two-dimensional vector of time series obtained at t = 1. . .., T with a finite-order VAR representation given by:

The F statistic related to Equation 4 follows F (2, T-2p) for Ω ∊ 0,𝜋 . It is to be noted that the causality between the two variables at the low frequency denotes the long-run causality and the short-run causality is represented at high frequency. If the variables are cointegrated then causality at zero frequency connotes long-run causality and in case of stationary variables there exists no such thing like long-run causality, instead, the low-frequency causality implies the explanatory variable is able to predict the low-frequency component of dependent variable one period ahead.

Asymmetric cointegration

Primarily the study examines the existence of a long run cointegration relationship between fiscal deficit and inflation in the case of the Indian economy. There are a number of linear tests available in the literature to check for the existence or otherwise of cointegration. The limitation of these tests is that they assume a symmetrical association among the variables and ignore the possibility of any asymmetry in the nature of the relationship. The scholars like ^{Shin et al. (2014}) cautioned about the misleading repercussions of assuming explicitly a linear association among variables and therefore, suggested to take account of possible asymmetries. This development of literature evaluating the issues of non-stationarity and non-linearity ( ^{Psaradakis et al., 2004}; ^{Kapetanios et al., 2006}) clearly highlights the inadequacy of linear models to allow a more precise statistical inference and to produce authentic forecasts in case of situations featured with positive transaction costs and where policy implications are observed in-sample (Shin et al., 2014).

Doing away with linearities and taking cognizance of possible asymmetries, we applied an asymmetric NARDL model by ^{Shin et al. (2014}) for empirical analysis of long-run and short-run asymmetries among variables of interest. It is a convenient approach as it provides a dynamic error correction specification combined with the asymmetric long-run cointegration regression by decomposing a given time series 𝑁 𝑡 into its oppositely signed partial sums (N_t ⁺ and N_t ^-) in order to take account of possible asymmetries. Second, it provides Bounds based test statistic, used to check for the existence of stable long-run association among variables of interest. Third, the method provides graphs of cumulative dynamic multipliers used to trace out the adjustment patterns following the positive and negative shocks to explanatory variables. The model is simple and comprehensive enough to permit any asymmetry switching from the short-run to the long-run or vice versa . Such is the flexibility of this framework that accommodates different specifications for the various possibilities of short-run and long-run asymmetry. Additionally, the NARDL model allows to testing for the hidden cointegration, so that the omitting any relationships, not apparent in a linear framework, is avoided .

The basic version of the model involves the following specification(13 y 14):

The study finally employs the non-linear cumulative dynamic multipliers to portray the route between disequilibrium position of short period and new long-run equilibrium of the system. The multipliers permit us to find out the asymmetric adjustment patterns following positive and negative shocks to explanatory variables. The diagrams have the important theoretical connotation of providing a way to illustrate the traverse to a new stable equilibrium position following any short-run disturbance from the long-run relationships (^{Shin et al., 2014}). The cumulative dynamic multipliers of 𝑁 𝑡 + and 𝑁 𝑡 − on 𝑀 𝑡 can be evaluated as follows(15):

components around a zero threshold value to portray the behaviour of the dependent variable to these components.

So far as the variables involved in the empirical analysis are concerned, the following nonlinear error correction models based on the error correction specification (14) are estimated.

The inclusion of various variables is strictly in accordance with the existing theory and empirical evidence. While the GFD or GFDD is included according to the various theoretical arguments discussed above. The fiscal deficit is largely considered to be inflationary, although the channels of transmission may assume various routes like the consumption expenditure channel, money supply channel, import channel and the interest rate channel . As a proxy for monetary policy actions, interest rate variable (CMR) is included and inflation is expected to respond negatively to interest rate through the traditional aggregate demand channel. To take account of the possible influence of structural factors , we include real GDP growth rate to act as a surrogate for output fluctuations in the economy.

The increased output growth is expected to lower the inflation by increasing the domestic availability of goods and services whereas a decrease in it escalates the inflationary pressures to denote the dearth of goods and services domestically. Due to the increasing integration of the Indian economy with the rest of the world, trade-openness is also included in the inflation determination equation to test the validity of ^{Romer (1993}) hypothesis on India. The hypothesis argues for a negative association between openness and inflation due to the time inconsistency of monetary policy in case of more open economies. Lastly, increasing dependence on oil imports makes it imperative to include the oil price dynamics and exchange rate fluctuations into the domain of empirical analysis. The increased oil prices are expected to trigger inflationary pressures and a fall is expected to lower them, of course depending upon the extent of pass-through. Thus, a combination of factors related to monetary policy, fiscal policy, supply shocks, and external factors are included in empirical exercise for a comprehensive analysis.

Unit Root Analysis

Using the appropriate specifications for each variable in each of the unit root tests mentioned, the results shown in Table 2 report a mixture of stationary and non-stationary variables. INF, CMR, GDPFC, and OPI are found I(0) and GFD, GFDD and TO are found to be I(1) at the level and I(0) at first differences.

Cointegration and causality analysis

Prior to the causality evaluation, we first check whether the variables of prime interest i.e. INF & GFD and INF & GFDD have any cointegration relationship or not. Since we have a mixture of I(0) and I(1) variables, the autoregressive distributed lag model (ARDL) or Bounds F-test as given by ^{Pesaran and Shin (1998}) and Pesaran et al. (2001), would be an appropriate method to apply. As reported in panel A of Table 3, the null of no cointegration is rejected between INF and GFD both expressed as a percentage of GDP and expressed as a percentage of NM. With the confirmation of the long-run association between the two variables, causality analysis in time domain framework is examined through the TY test.

Literature has highlighted that the direction of causality is not only from fiscal deficit to inflation through the various transmission channels, but a feedback causality is also possible. Inflation can both increase and decrease the fiscal deficit. According to ^{Aghevli and Khan (1978}) Hypothesis, an increase in inflation reduces the real value of government income and thereby necessitates it to borrow more in order to meet the expenditure requirements. With the resulting deficit rises and is known as the inflation-induced fiscal deficit (^{Heller 1980}). Similarly, excessive inflationary pressures mandate a contractionary policy stance on part of monetary authorities to control it. Higher nominal interest rates (Fisher effect) increase the debt interest payments of the already accumulated debt of the government and thereby trigger further the deficit level. Similarly, according to Olivera-Tanzi effect, higher inflation pressures can also lead to a decline in the volume of tax collection and deterioration of real tax proceeds being collected by the government due to time elapsed between the taxable event occurs and the collection of the tax becomes effective (collection lags) (^{Olivera, 1967}; ^{Tanzi, 1977}) . Thus, due to the deteriorated revenue position, deficit increases. Besides the positive association, the inflation and budget deficit also move in reverse direction under certain circumstances. Inflation tax would be a type of tax revenue that leads to a fall in deficit. Similarly, if the borrowing is not indexed to the inflation, any hike in the later will lower the real value of the former through borrowing shocks.

Table 2: Unit Root Tests 

The lower panel of Table 3 indicates that fiscal deficit granger causes inflation in India, but feedback causality is absent. The presence of unidirectional causality and long-run cointegration relationship highlights the inflationary influence of deficits in India. The establishment of the unidirectional causality from fiscal deficit to inflation is in accordance with the results of ^{Ramu and Gayithri (2017}).

Table 3: Cointegration and time domain causality 

The four parts of Figure 1 (a, b, c and d) portray the causality analysis in a frequency domain framework using the BC test. Part “a” of the figure shows that fiscal deficit causes inflation only at lower frequencies or higher time periods. At the higher frequencies (short run), the deficit is not found to cause inflation in India. Part “b” again provides an illustration of the absence of feedback causality from inflation to fiscal deficit in India at all frequencies. Thus, BC test documents that fiscal deficit need not be inflationary in the short-run since the government can temporarily resort to other sources of financing the deficit instead of excess money creation in the short-run (^{Sargent and Wallace, 1981}; ^{Lin and Chu, 2013}). The outcome of this result also validates the intrinsically dynamic nature of the relationship between these two variables. The parts “c” and “d” of the figure provides the results for robustness.

Figure 3: Frequency domain causality (Breitung-Candelon test) 

Asymmetry analysis

With a view to providing some insights about the possible asymmetry, if any, exists between the variables, the study applies NARDL model developed by ^{Shin et al. (2014}). We include INF, GFD, CMR, GDPFC, TO and OPI variables into the NARDL error correction specification (8). Since none of the variables is I(2), the application of NARDL is justified. In addition, the adopted methodology would also take note of the dynamic nature of the relationship since the specification permits the lags of both dependent and independent variables to influence the dependent variable and also allows for intrinsic dynamic adjustment .

We followed a general to a specific approach for the estimation of the asymmetric ARDL error correction model. We started with max p = q = 2 as decided by AIC and SBC criteria’s, and zero restrictions are assigned to most of the insignificant lags to ensure precision and avoidance of noise into the dynamic multipliers. In this case, as well, the null of no cointegration is rejected like in the case of linear Bounds F-test, since both the t_BDM and F_pss tests, as reported at part B of Table 4, are found to be statistically significant at 5% significance level.

Part C of Table 4 reports the necessary diagnostic tests of the estimated model to ensure its stability. The absence of serial correlation is accepted in the case of the Portmanteau serial correlation test 𝜒 𝑆𝑐 2 , heteroskedasticity in the case of Breusch-Pagan-Godfrey test 𝜒 𝐵𝑃𝐺 2 , Ramsey RESET test (Ramsey F-test) validates the structural specification of the model and finally, presence of normality is accepted in the Jarque-Bera test of normality 𝜒 𝑁𝑂𝑅 2 . With the detection of long-run association, we proceed to examine whether it is symmetrical, or some asymmetry is involved. Wald’s test with a null of symmetrical association between the variables is tested against an alternative of asymmetrical one. The (W_LR) and (W_SR) test statistics are shown in part A of Table 4. The results document that except between INF and OPI, presence of an asymmetrical long-run association is established between INF and all other explanatory variables. The short-run asymmetry is also reported among the same pairs of variables and the null of symmetric association cannot be rejected again in case of oil price inflation.

Table 4: Long and short-run asymmetry results (Model 8) 

The long-run coefficients of the inhabited relationship of various variables with the inflation are reported in panel A of Table 4. The establishment of a long-run direct association between the fiscal deficit and inflation provides empirical support to the projections of ^{Khundrakpam and Pattanaik (2010}) and is in line with the findings of ^{Rangarajan et al. (1989}); ^{Ramu and Gayithri (2017}); ^{Tran (2018}). The surge in money supply (due to increased fiscal deficit and capital inflows) and the narrowing down of negative demand gaps in the economy provide a manifestation of inflationary role of fiscal deficit in India (Khundrakpam and Pattanaik, 2010). The positive impact of fiscal deficit on the inflation of India can be attributed to the transmission channels like the consumption expenditure channel, money supply channel, import channel and the interest rate channel and according to the theoretical arguments of Keynesians, Neo-Classicals and Monetarists. In fact, Ramu and Gayithri (2017) have exclusively tracked the first three of these transmission channels in the case of India. Emerging countries like India featured with high fiscal deficit and public debt stocks are more exposed to inflationary pressures (Tran, 2018). Therefore, owing to their less fiscal space as may be needed for sustainability, these economies are vulnerable to higher default risks.

The signs of positive and negative coefficients are according to theory and both are statistically significant. However, an increase in fiscal deficit is found to be more inflationary and the decrease in it affects inflation with a lower magnitude. In other words, the impact of fiscal position deterioration is more pronouncing and that of any fiscal improvement is milder or sluggish. Though it is not within the scope of the present study to provide the underlying reasons for the asymmetric response of inflation to positive and negative changes in the fiscal deficit, however, the relevant theoretical underpinnings are worth mentioning. The possible asymmetry of fiscal deficits on inflation in the case of India can be explained through the existence of liquidity constraints, consumption-investment downward inflexibility, and the downward price stickiness. Due to the existence of liquidity constraints as reflected through less developed credit markets and a large portion of the population without adequate purchasing power in India, any increase in fiscal deficit would lead to an exacerbated increase in aggregate demand because of additional purchasing power with the individuals with limited liquidity hitherto and also enable individuals without constraints to feel wealthier. Hence, the impact on inflation would be relatively higher. In case the deficit is reduced, people find it difficult to lower the consumption levels (Ratchet effect) drastically along with investment irreversibility, leading only to a marginal decline in demand conditions and hence a lower effect on inflation is observed. The lower impact of reduced fiscal deficit on inflation can also be explained through downward price stickiness. Producers are usually hesitant to price and wage reductions in order to avoid worker-employ conflicts, secure the worker morale and secure some degree of price-setting power even in a supply-constrained economy (^{Mohanty and John, 2015}; ^{Barnichon et al., 2017}). Thus, the effect of contractionary fiscal action in-terms of a decline in deficit spending has a lower effect on inflation.

Coming to the impact of monetary policy shocks as surrogated through call money rate of interest, we found that the inflation and interest rate are negatively related wherein a contractionary policy shock lowers it and an expansionary one raises it. The established negative association can operate through the conventional aggregate demand channel. However, the contractionary policy stance is found to be more effective than the expansionary one, signifying the asymmetric influence of monetary policy actions on the inflation of India. The reason for this asymmetry can be ascribed to the behaviour of demand conditions due to the changing outlook of firms and consumers, the binding liquidity constraints and the downward price stickiness under alternative monetary policy stances (^{Morgan, 1993}; ^{Barnichon et al., 2017}). The changing outlook of firms and consumers could result in the asymmetric impacts, if the magnitude of pessimism is relatively high during economic downturns than they are optimistic during expansions, or if the business and consumer confidence matters more during recessionary conditions . As related to the credit constraints, tight monetary policy would be more effective than an easy one if the former makes banks less willing to lend to some riskier (subprime) borrowers. An increase in interest rate by the central bank leads to an upsurge in lending rates of commercial banks due to the shifting of increased costs to the borrowers. The high lending rates could, in turn, increase the chances of borrower’s default and as a result, banks become more risk-averse and very choosy in the credit supply and hence make them liquidity constrained. This leads to a lower level of investment, a reduction in household consumption i.e. a reduction in demand conditions and hence an appreciable fall in the price level (Barnichon et al., 2017). On the contrary, a cut in the interest rates through an easy policy action eliminate the credit constraints. However, if the demand for credit is lacking during an economic downturn, slacking the liquidity position may not necessarily augment the borrowings and higher spending. Therefore, the impact on inflation would be feeble. The substantial reduction of inflation due to the interest rate hike can also be explicated through the money supply channel. An increase in interest rate by the central bank leads to a rise in prime lending rate, a decline in credit supply, a fall in money supply via money multiplier process and finally to a lower level of the price level. On the other hand, a decrease in the interest rate during the retarded economic conditions will not necessarily lead to borrowing and spending augmentation of all economic agents in the economy, the effect of expansionary policy stance on inflation will, therefore, be relatively lower (Barnichon et al., 2017).

The impact of output growth and decline as represented by the positive and negative-sum components of GDPFC is well according to the theory, but asymmetry is reported. Negative growth in output is found to increase the inflation relatively by a higher magnitude than the positive growth is found to reduce it. A decline in the output indicates the presence of supply constraints and given the demand conditions (if not increasing) the impact on price level will be higher. On the contrary, an increase in output growth cannot decrease the prices drastically due to the presence of producer’s price-setting power to some extent. Thus, in a supply-constrained economy, downward price rigidity leads to the asymmetric impact of output changes on inflation.

As regards the trade-openness, although an asymmetry is reported, the signs refute the validation of ^{Romer (1993}) hypothesis in the case of India. Specifically, the coefficient of TO⁺ is -0.389 and that of TO^- is -0.723. The former is statistically insignificant, and the latter is significant. The insignificance of TO⁺ may be due to its ex-post measurement of openness only and lacks the ex-ante measurement. The negative component is significant, however, and the sign establishes a direct association between these two variables. The direct link in the case of India is in line with the inflationary impact of outward orientation for developing countries (^{Jalil et al., 2014}, ^{Ajaz et al., 2016}).

Finally, as related to the impact of oil price inflation, though signs are theoretically correct, yet both the positive and negative coefficients are weak and statistically insignificant. The possible explanation for such insignificance at the segregated asymmetry level is that the global oil prices have been converted to domestic oil prices by multiplying the former with the nominal exchange rate of the country concerned. The movements of the two variables, global oil prices, and exchange rates may not always be reinforcing but some counter-movements are also possible. There may be a fall in global oil prices, but due to the increased demand from the country at a lower global price, its exchange rate depreciates and the initial effect of declining oil prices will now get negated through increasing exchange rates and thus the overall influence may be insignificant. In the case of India, the depreciation of rupee more than offsets the favourable effect of the marginal decline in global commodity prices on domestic inflation (Reserve Bank of India, 2013).

Cumulative dynamic multipliers

Figure 2 plots the dynamic effects of positive and negative changes in the fiscal deficit, interest rate, output growth, trade openness and oil price inflation on the inflation of India. The blue line indicates the line of asymmetry and the value of this line at any given point measures the extent of asymmetry at that point. Figure 2 replicates all the results reported in Table 4, thus confirming the validity of the above discussions. In the case of fiscal deficit, asymmetry is stronger from the positive change and asymmetry persistence is observed even in the long-run. As related to the interest rate shocks, contractionary policy shock is more influential than the expansionary one and the asymmetry is observed in both the short and long-run. Similarly, expansion in output growth declines inflation relatively by a lower magnitude than the increase in inflation is caused by output decline. Finally, in the case of oil price inflation and trade openness, we found the same phenomenon as reported in Table 4. However, the coefficients associated with a positive change in trade openness and with both positive and negative changes in case of oil price inflation are statistically insignificant. Moreover, it takes around more than a year on average to reach to new equilibrium position following any short-run disturbance.

Figure 4: Cumulative dynamic multipliers for (Model 8) 

Extensions

The robustness of results is provided by using the fiscal deficit as a percentage of narrow money in an economy as the key variable of analysis. Table 5 and Figure 3 reflect almost a mirror image of Table 4 and Figure 2 and therefore prove the robustness of results .

Figure 5: Cumulative dynamic multipliers for (Model 9) 

Although, the study has attempted to explore the asymmetric nature of the relationship among selected variables, however, certain caveats to this investigation are warranted and introspection into these caveats may provide scope for further research in the area. To start with, a more structured analysis with robust theoretical underpinnings may enhance the understanding and findings of this study. Second, on the whole, the unavailability of data for sufficiently longer periods of time acted both a challenge and hindrance for the exploration of a more comprehensive analysis. Although, the asymmetries have been observed in the empirical exercise with respect to the response of inflation to various macroeconomic variables, and the probable theoretical reasons were advocated, however, identification of exact transmission channels of asymmetry would enrich the analysis.