On the predictive content of the PPI on CPI inflation: the case of Mexico


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1 On the redictive content of the PPI on inflation: the case of Mexico José Sidaoui, Carlos Caistrán, Daniel Chiquiar and Manuel RamosFrancia 1 1. Introduction It would be natural to exect that shocks to roducer rices, as they sill over through the roduction chain, should eventually have some effect on consumer rices. This should hold true for costush shocks that are exected to aear initially during the first stages of the roduction chain. In this case, it would also be natural, from a statistical oint of view, for roducer rices to cause consumer rices (ie roducer rices should Grangercause consumer rices). Following these considerations, information on roducer rices could therefore be useful for central banks in identifying costush shocks and imroving forecasts of consumer rices inflation. The international exerience, however, seems to suggest that the connection between roducer and consumer rices is not as close as the abovementioned rationale would imly. For examle, emirical studies for the United States, such as those by Clark (1995), and Blomberg and Harris (1995), find that the roducer rice index (PPI) does not have a significant redictive content for the future attern of the consumer rice index (). The lack of robust evidence regarding a close causal link between the PPI and the, along with the fact that most central banks define their inflation targets in terms of a certain measure of consumer rices, has led some central bankers to disregard the PPI as a relevant indicator for assessing inflationary trends. Nevertheless, there are several shortcomings in the literature concerning this issue. Among these, the most relevant are: i) In general, the range of rices included in both roducer and consumer rice indices differs significantly. Indeed, it is common for PPI baskets to include mainly domestically roduced goods, while s include comrehensive sets of goods and services. ii) The revious literature has not given enough relevance to the role layed by the statistical roerties and dynamic interactions of and PPI time series in the analysis. In articular, most revious studies have assessed Grangercausality between these two indices by using VAR models in first differences. However, this rocedure relies on two assumtions: a) rice levels are I(1) series and therefore inflation rates are stationary; and, b) consumer and roducer rices are not cointegrated. Should either of these two assumtions not hold, the estimation of a VAR in differences is thus not the aroriate tool for analysis. In articular, if the ricelevel series are I(2), then the causality analysis should take this roerty into account, which further comlicates the study. Regarding cointegration, it is well known that, if two series are cointegrated, the VAR in first differences suffers from omittedvariable biases, because it does not include the relevant error correction mechanism (ECM) term. These biases can make Grangercausality tests lead to misleading conclusions (an issue ointed out by Granger (1988)). 1 Banco de México. The views and conclusions exressed in this aer are solely those of the authors and do not necessarily reflect those of Banco de México. We would like to thank José Antonio Murillo and Antonio Noriega for their hel and comments and Gabriel LóezMoctezuma for excellent research assistance. BIS Paers No
2 This note readdresses the revious evidence concerning the ossibility of a causal relationshi between the PPI and the, using data of both rice indices in Mexico. We believe this country is an aroriate case for studying the dynamic relationshi between the two indices. Indeed, since 1994, the range of rices in the PPI has included the service sector, and the methodology to comute both indices is homogeneous. Although in this case the and PPI still differ, the analysis should not be as affected as in other countries by issues concerning the range of rices included in such indices. For the uroses of this aer, the statistical and dynamic interactions of both time series are considered to be significantly relevant. Evidence is resented showing that from mid2 onwards, the inflation rates of both the and PPI became stationary. The analysis is therefore restricted to the eriod when consumer and roducer rice inflation rates may be safely assumed to be I(). The biases imlicit in using a VAR in differences are exlicitly avoided. We first show evidence that both PPI and series seem to be indeed cointegrated and, thus, the causality analysis is based on a vector error correction model (VEC), which exlicitly considers the role of the ECM term in the estimates. Figure 1 Annual inflation: vs PPI PPI Source: Bank of Mexico. In contrast with revious studies, the results suggest that, in the case of Mexico, recent information on the PPI seems to be useful for imroving forecasts of inflation. In articular, inflation resonds significantly to disequilibrium errors with resect to the longrun relationshi between consumer and roducer rices (ie whenever roducer rices suffer a shock, inflation increases temorarily until consumer rice levels adjust to their longrun relationshi with roducer rices). Thus, what may have led revious literature to conclude that the PPI is not useful in redicting movements seems to be recisely the omission of this relevant transmission mechanism in the analysis. 2 2 The Bank of Mexico s latest exerience with the PPI in assessing consumer inflationary ressures tends to confirm these conclusions. In some of the recent eisodes in which the trajectory of inflation has changed course, the PPI did in fact rovide an early warning about the inflection oint (see Figure 1). 25 BIS Paers No 49
3 The rest of the document is organized as follows: Section 2 analyzes the statistical roerties of the and the PPI series over time, and in articular, their degree of ersistence. Section 3 describes the methodology used to determine the usefulness of the PPI as a redictor of inflation. Section 4 summarizes the emirical results. Finally, Section 5 resents some final remarks regarding the ossible lessons that may be obtained from the Mexican exerience on the use of oututbased rice indices to assess inflationary ressures. 2. Changes in the ersistence of the and the PPI In order to analyze the change in the ersistence of both the and PPI, the first ste is to identify their basic time series roerties. These roerties constitute a building block for further research. It is of articular relevance to identify the order of integration of the data; that is, to assess whether PPI and inflation rates are stationary I() rocesses or not. As mentioned before, if inflation rates are nonstationary I(1) rocesses, then the rice levels would be I(2) rocesses, and the analysis to identify the assthrough of roducer rice shocks to consumer rices would therefore be more comlicated. Identifying whether inflation rates are stationary or not becomes more difficult when shifts in monetary olicy, among other factors, make inflation rates switch from nonstationary to stationary regimes, or vice versa. However, several tests have recently been develoed to accurately decomose the samle in a time series observed in stationary and nonstationary behavior segments. Regarding the Mexican economy, evidence based on this tye of tests suorts the idea that consumer rice inflation shifted from a nonstationary to a stationary regime around 2 (see Chiquiar, et al (27)). This date nearly coincides with the eriod when the Bank of Mexico formally adoted an inflation targeting regime. The latest develoment in this methodology is based on a test for multile changes in ersistence by Leybourne, Kim and Taylor (27), which also allows for estimating the dates of change in a consistent way. Their test identifies all stationary eriods within the samle, effectively decomosing the data into stationary (or I()) and nonstationary (or I(1)) subsamles. When no I(1) behavior is detected, the series is stationary. The eriods identified as I()/I(1) can then be analyzed in terms of both timing and oerating rules of monetary olicy. Table 1 Test for changes in ersistence Series Samle Starting date for I() eriod inflation 1994:2 28:1 2:5 PPI inflation 1994:2 28:1 2:4 Source: Own calculations with data from Bank of Mexico. The results of the test for monthly inflation data based on and PPI inflation rates in Mexico suggest that, in both cases, inflation shifted from a nonstationary to a stationary regime around mid2. Table 1 summarizes the results. The second column refers to the samle to which the testing rocedure was alied. The following column reorts the date identified by the rocedure as the beginning of the I() subsamle. For instance, for the, the test identifies a single I() eriod from May 2. This means that from 1994:2 to 2:4, inflation seems to have behaved in a nonstationary fashion (ie as a I(1) BIS Paers No
4 rocess), while from 2:5 onwards, the test suggests that this inflation rate behaved as a stationary rocess. Very similar conclusions can be reached regarding PPI inflation. Aarently, from the beginning of the samle to the year 2, the data behaves as a nonstationary rocess, while from mid2 onwards, the inflation indices behave in a stationary way. The level of significance for all changes in ersistence was 1%. These findings are similar to those reorted by Chiquiar et al (27). Figure 2 reresents the results grahically. The grahs lot the two inflation series, together with straight horizontal lines indicating the stationary eriod, as identified by the ersistence change test. For convenience, this line is drawn at the inflation mean during the I() eriod identified by the test. 8 Figure 2 Monthly and PPI inflation (a) (b) PPI Source: Bank of Mexico. 252 BIS Paers No 49
5 To conclude, the two inflation measures analyzed aarently switched from nonstationary to stationary behavior during 2. Considering that inflation is the difference between the (log) rice indices, from 21 onwards, both rice indices can be treated as I(1) variables. Given the latter, for the rest of the aer, the analysis will be conducted by restricting the samle to the eriod from January 21 to the last observation available (October 28), in order to ensure that the variables are stationary in differences (I(1) in levels) and, thus, that the conventional cointegration analysis is alicable Methodology to evaluate the redictive content of the PPI for the In this aer, the methodology roosed by Granger (1969) and later oularized by Sims (1972) is used to analyze if the PPI can hel forecast the (ie if PPI Grangercauses ). The most commonly used test of Granger causality, otherwise known in econometric textbooks and software as Granger test, is erformed under a bivariate vector autoregression (VAR), where a joint exclusion test is used. In order to investigate the redictive ability of PPI inflation for inflation, the relevant equation from the VAR would be: t PPI j t j j t j t, (1) j 1 j 1 where t is considered as white noise. The VAR is tyically estimated by ordinary least squares (OLS), and the number of lags,, is usually determined by using an information criterion such as the Bayesian information criterion (BIC), or Schwarz Criterion. Then, a test of the null hyothesis H : , (2) is conducted, either with the usual Ftest, or with the Wald variant. 4 If the null hyothesis is rejected, then it can be concluded that PPI inflation does Grangercause inflation. These tyes of tests have been used in the literature to investigate the relation between PPI and inflations (eg Clark (1995)). Engle and Granger (1987), however, show that if the variables under investigation are I(1), and a linear combination of them is I(), that is, if the variables are cointegrated, then the series will be generated by an errorcorrection model. Considering the natural logarithm of the rice indices ( = ln() and PPI = ln(ppi)), their first difference will be the (monthly) inflation rate. The first equation of the VEC reresentation would thus be: 3 4 Augmented DickeyFuller tests (Dickey and Fuller (1979)) for this eriod cannot reject the hyothesis of a unit root in each rice index at the 1% level. The tests were erformed using a constant and a linear trend, and the number of lags was selected using the BIC criterion, starting with 18 lags. The Ftest alies if t is assumed to be Gaussian. However, even in such a case, the Fdistribution would aly only asymtotically because the lagged deendent variables that aear as regressors make the assumtion of fixed regressors untenable. BIS Paers No
6 z t t 1 PPI 1 zt 1 j t j j t j t, j 1 j 1 t 1 1 PPI t 1 (3) where t is considered as white noise, z t1 is the error correction term, and can be interreted as the degree to which the system is out of equilibrium from the longrun relationshi between the series, 1 is the seed of adjustment, and 1 is the cointegration coefficient. After comaring equations (1) and (3) it is clear that if the rice indices are cointegrated, then equation (1) is missing the error correction term, and hence, is missecified. Indeed, Granger (1988) shows that a consequence of the errorcorrection model is that at least one of the variables in the system must be caused by z t1, (which is a function of the lagged rice levels). Therefore, if two variables are cointegrated, (Granger) causation must follow at least in one direction. Granger and Lin (1995) define clearly the existence of two imortant sources of causation in the errorcorrection model (3). One originates from the effect of the error correction term (ie from the longrun relationshi), if 1 is different from zero, and the other, from the lags of the PPI inflation rate (ie from the shortrun dynamics), if s are different from zero. Accordingly, the former is called longrun Granger causality while the latter is shortrun Granger causality. If the and the PPI are cointegrated, then there can be shortrun causation from the PPI to the, longrun causation, or both. No causation from the PPI to the can also occur, although this would imly some tye of causation from the to the PPI. Since the results in the revious section suggest that both rice indices under study are I(1) variables in the samle since 21, it is imortant to emhasize that, if the two series are shown to be cointegrated, the model in equation (1) would be missecified if z t1 is not used exlicitly. In this case, if equation (1) is used, the ossible relevance (in levels of significance) of the PPI as a redictor of the could be missed. In extreme cases, this missecification could invalidate comletely the ossible use of the PPI in forecasting the (eg if both variables are cointegrated and there is only longrun causality from the PPI to the ). 4. Granger causality from the PPI to the : emirical results In this section, the errorcorrection model (3) is used to investigate the causal relation between the PPI and the, in both the long and short runs. First, the series must be tested for cointegration. Once evidence of cointegration is rovided, equation (3) is estimated. As a final ste, significance tests on 1 and on s are erformed to assess causality from the PPI to the. All estimations consider the eriod from January 21 to October 28, a subsamle characterized by the stationarity of both and PPI monthly variations (see Section 2). To test for cointegration, we emloy the methodology roosed by Engle and Granger (1987). A regression of the log was run on a constant, the log PPI, and 11 (centered) seasonal dummies. Then, an augmented DickeyFuller test was alied to the residuals of that regression (see Table 2 for test results). The null hyothesis of a unit root in the residuals can be rejected at the 5% significance level. Thus, at the same significance level, the hyothesis that both and PPI are not cointegrated is rejected. 254 BIS Paers No 49
7 Table 2 Cointegration test Variables ADF tstat a PPI 3.53** a EngleGranger (1987) test. Critical Values: 1%: 3.96, 5%: 3.41, 1%: 3.12 (following Hansen (1992)). Model with constant and 11 (centered) seasonal dummies. Source: Own calculations with data from Bank of Mexico. Given these results, the cointegration coefficient, 1, is then estimated using the dynamic ordinary least squares estimator roosed by Stock and Watson (1993). This is a simle rocedure that roduces asymtotically standard normal distributed tvalues, so that inference on 1 can be erformed in the usual manner. 5 The oint estimate is.817 with a standard error of.43. Therefore, the null hyothesis that the cointegration coefficient is.8 cannot be rejected at the 1% level, while the hyothesis that this coefficient is 1 can be rejected at the same level. A cointegration coefficient below one imlies that, in the long run, the assthrough from roducer rices to consumer rices is not comlete, although some considerable assthrough in equilibrium exists. This scenario could arise, for examle, in a situation of monoolistic cometition with nonnegligible fixed costs. Since we do not reject the hyothesis that rice indices are cointegrated, it is more aroriate to estimate equation (3) rather than equation (1). The results of the estimation of the corresonding bivariate VEC (where equation (3) is the first equation of the VEC) are reorted in Table 3. The cointegration coefficient is again estimated to be around.8. The estimates of interest corresond to equation (3), which, in the VEC reorted in Table 3, corresonds to the first column, and the behavior of inflation. As may be noted, the error correction term is significantly different from zero at the 5% level in the inflation equation. 6 Hence, there is evidence of longrun Granger causality from the PPI to the. The seed of adjustment is.114, which means that a shock to the equilibrium relationshi is corrected by around 1% each month, so that the total effect vanishes in less than a year. It is relevant to note that a Wald test erformed on the null hyothesis that the coefficients associated with the two lagged values of PPI inflation are not significant is not rejected (the value is.8774). Therefore, no shortrun (Granger) causation from the PPI to the is found. This result suggests that if we had estimated a VAR in first differences without including the ECM term, we might have erroneously concluded that the PPI does not cause inflation. Finally, the adjusted Rsquared from this regression is slightly below.6, which imlies that this model exlains slightly less than 6% of the total variation of monthly inflation. To conclude, the results of the VEC estimates and its corresonding Granger causality tests suggest that roducer rices are useful for redicting inflation in Mexico. In articular, even though Granger causality tests summarized in Table 3 suggest that roducer rice inflation is not significant for redicting consumer rice inflation in the short run, the latter resonds significantly to disequilibrium errors with resect to the longrun relationshi between consumer and roducer rices. This means that, whenever roducer rices suffer a 5 6 The rocedure roosed by Stock and Watson is to augment the equation in levels used in the EngleGranger tests with leads, lags, and the contemoraneous value of the difference of the (log) PPI. In this case, 4 leads and equal number of lags were used, chosen according to the BIC, from a maximum of six lags (or leads). Inference in the VEC can be erformed as usual given that all variables entered in the equation are stationary. BIS Paers No
8 shock (ie a costush shock), consumer rice inflation increases temorarily until consumer rice levels adjust comletely to their longrun relationshi with roducer rices. Indeed, as can be seen in the results summarized in Table 3, the errorcorrection mechanism aears significantly in the consumer rice inflation equations while its coefficient in the roducer rice inflation equation is insignificant. This suggests that, in the long run, it is consumer rices that resond to roduce rice shocks, and not vice versa. In turn, this means that knowledge of shocks that affect roducer rices is useful to redict future changes in consumer rice inflation. Table 3 Vector error correction estimates a Samle (adjusted): 21M4 28M1 endogenous variables: PPI Cointegrating equation Cointegrating Eq: CointEq1 L( 1) 1 LPPI( 1).7944 [ ] C 1.81 Error correction: D(L) D(LPPI) CointEq [ ] [1.3369] D(L( 1)) [1.9134] [ 2.822] D(L( 2)) [.6787] [.3541] D(LPPI( 1)) [.3184] [3.8159] D(LPPI( 2)) [.516] [.735] C [5.3416] [4.7436] Adj. R squared Granger causality (val).8774 Schwarz Criterion a tstatistics in brackets. Eleven seasonal dummies (centered) were also included in each equation. Source: Own calculations with data from Bank of Mexico. 256 BIS Paers No 49
9 5. Final remarks This note resents evidence from Mexico suggesting that the PPI may have a significant redictive content for the subsequent develoment of inflation. The causality relation from the PPI to the identified in this aer is not driven by coefficients associated with shortrun dynamics, but by the longrun resonse of consumer rices to shocks to roducer rices, which leads to a temorarily higher inflation rate until the longrun equilibrium relationshi between these two indices is satisfied again. Thus, in other countries that may have ricesetting characteristics similar to Mexico, finding a relevant causal relationshi from the PPI to the may also require the secification of a statistical model for these two series that adds a longrun cointegration relationshi to the shortrun dynamics of these two series. The Mexican exerience described in this aer could thus be useful for other central banks seeking to uncover the dynamic relationshi between roducer and consumer rices. It is relevant to mention, however, that the model estimated in this aer is fundamentally a reduced form. In articular, although we have found what seems to be a significant transmission channel from roducer to consumer rices, which could otentially imrove the forecasting ability of the latter, we do not claim that the model resented here is the most efficient for roducing inflation forecasts. Indeed, the information concerning the develoment of roducer rices must be combined with other relevant inflation redictors to roduce efficient forecasts. What the aroach taken in this aer suggests is simly that, within the full set of indicators that could be used, the PPI seems to be a valuable iece of information for assessing inflationary ressures. References Blomberg, S B and E S Harris (1995): The commodityconsumer rice connection: fact or fable? Federal Reserve Bank of New York Economic Policy Review, vol 1 no 3. Chiquiar, D, A E Noriega and M RamosFrancia (27): A time series aroach to test a change in inflation ersistence: the Mexican exerience, Bank of Mexico Working Paers 271, forthcoming in Alied Economics. Clark, T (1995): Do roducer rices lead consumer rices? Federal Reserve Bank of Kansas City Economic Review, Third Quarter. Dickey, D A and W A Fuller (1979): Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association, 74, Engle, R F and C W J Granger (1987): Cointegration and error correction: reresentation, estimation, and testing, Econometrica, 55, Granger, C W J (1969): Investigating causal relations by econometric methods and cross sectral methods, Econometrica, 37, Granger, C W J (1988): Some recent develoments in a concet of causality, Journal of Econometrics, 39, Granger, C W J and J Lin (1995): Causality in the long run, Econometric Theory, 11, Hansen, B E (1992): Efficient estimation and testing of cointegrating vectors in the resence of deterministic trends, Journal of Econometrics, 53, Leybourne, S, T Kim and A M Taylor (27): Detecting multile changes in ersistence, Studies in Nonlinear Dynamics and Econometrics, 11(3), Sims, C (1972): Money, income and causality, American Economic Review, 62, Stock, J H and M W Watson (1993): A simle estimator of cointegrating vectors in higher order integrated systems, Econometrica, 61, BIS Paers No
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