A Global Perspective On Inflation And Propagation Channels

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Journal of Banking and Financial Economics 1(3)2015, 50–76A global perspective on inflation and propagationchannelsLuca Gattini1European Investment Bank, Luxembourgl.gattini@eib.orgHuw PillGoldman Sachs, United Kingdomhuw.pill@gs.comLudger SchuknechtBundesministerium Finanz, Germanyludger.schuknecht@bmf.bund.deReceived: 25 November 2014 / Revised: 30 January 2015 / Accepted: 16 February 2015 / Published online: 25 March 2015ABSTRACTThis paper revisits the evidence on monetary policy transmission. It extends the existing literaturein three dimensions. First, we attempt to internalise potential international channels of transmissionby taking a global perspective. More specifically, we explore global aggregates covering a broaderset of countries (ca. 70% per cent of the world economy) and a longer time span (from 1960 to2013) than previous studies. Second, we broaden the set of transmission channels considered,notably by exploring interactions among monetary variables, inflation and asset prices (includingresidential property prices). Third, we look at the potential role of public debt in driving pricedevelopments, on the grounds underpinned by fiscal theories of the price level. On the basis ofa VAR analysis, we find that: (1) global money demand shocks affect global inflation and globalcommodity prices (which, in turn, impact on inflation); (2) global asset price dynamics respondto financing cost shocks and (very modestly) to shocks to global money demand; and (3) positivehouse price shocks exert a significant influence on inflation. From a global perspective, the studysuggests that an understanding of inflation requires recognition of the externalities that globalcommodity and asset price developments exert over domestic inflation.JEL classification: E31, E51, E62, C32, F42Keywords: VAR, global inflation, global house prices, global money1Corresponding author. European Investment Bank, 98-100, Boulevard Konrad Adenauer, L-2950 Luxembourg; e-mail: l.gattini@eib.org. Faculty of Management University of Warsaw. All rights reserved.DOI: 10.7172/2353-6845.jbfe.2015.1.350

Luca Gattini, Huw Pill, Ludger Schuknecht Journal of Banking and Financial Economics 1(3)2015, 50–761. INTRODUCTION2The low frequency, reduced-form relationship between monetary growth and inflation is oneof the best-established correlations in empirical macroeconomics. However, characterising thestructural nature of this relationship – the transmission mechanism by which monetary expansionsinfluence price developments – remains a source of vigorous, ongoing debate.Recently, Sousa and Zaghini (2007) and Belke et al. (2010a; 2010b) investigated specificstructural characterisations of the relationship between monetary variables and inflationdevelopments in global models. Our paper also takes a global perspective, but encompassesprevious studies by analysing a wider set of macroeconomic variables, enlarging the geographicalcoverage of countries, and extending the time period analysed. We construct new globalmacroeconomic variables according to a ‘representativeness’ rule, which ensures that coveragemeets a minimum threshold on the geographic dimension. In so doing, we ensure our data setincorporates emerging market economies, and thus better captures the new global economy.Aside from greater geographical and temporal coverage, this paper extends the literaturein other dimensions, notably by entertaining three additional potential transmission channelsof monetary policy: (1) via international interactions in an increasingly globalised economy,notably through the evolution of commodity prices; (2) via interactions between monetaryvariables (liquidity and interest rates) and asset prices (specifically residential property prices),a relationship that has achieved growing interest in the literature; and (3) via interactions withbalance sheet variables, in particular the stock of public debt and credit to the private sector. Froma European perspective, the influence of public debt may be of particular interest, given the Euroarea fiscal and sovereign crisis that started in late 2009.Our analysis covers a time period spanning 1960 to 2013 and employs eight quarterly timeseries: money, credit to the private sector, real GDP, the consumer price index, house prices,the stock of public debt, the level of short-term interest rates and global commodity prices. Thecountry coverage in each period t represents at least 68 per cent of world GDP measured in termsof Purchasing Power Parity (PPP).Having established the statistical properties of our time series, we employ a vectorautoregression (VAR) approach to explore interactions among the variables and the relativeimportance of various possible propagation channels. Starting from a benchmark structuralcharacterisation similar to previous studies (embodying short-term interest rates, inflation, GDP,money and commodity prices), we augment our VAR model by adding house prices, privatecredit and public debt. Finally, we conduct a set of robustness checks.To anticipate our conclusions, we establish the following results.First, global monetary shocks affect both global inflation and global commodity prices. Inturn, global commodity price shocks affect global inflation. These findings highlight an additionalglobal transmission mechanism via commodity prices to global inflation. Inflation in individualcountries will be influenced by global commodity price dynamics that are likely to be beyond thecontrol of domestic monetary authorities.Second, at the global level asset/property price dynamics appear to be driven primarily byfinancing cost shocks, rather than being driven by shocks to global money. Moreover, an increasein house prices exerts a positive influence on global inflation.Third, there appears to be a modest, but negative, relationship between public debt andinflation. One interpretation of this empirical relationship is that it reflects Ricardian effects,where higher public debt weighs on private demand, thus reducing inflation. While looking2We are grateful for the helpful comments of Antonello D’Agostino, Andreas Beyer, Björn Fischer, Julian von Landesberger, Philippine CourThimann and Oskar Nelvin, as well as the suggestions of an anonymous referee. Moreover, we also thank the participants in the ECB seminar oninflation transmission channels. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the ECB orthose institutions with which they are currently affiliated. Faculty of Management University of Warsaw. All rights reserved.DOI: 10.7172/2353-6845.jbfe.2015.1.351

Luca Gattini, Huw Pill, Ludger Schuknecht Journal of Banking and Financial Economics 1(3)2015, 50–76forward one might entertain a positive link between public debt and global inflation stemmingfrom concerns about potential fiscal dominance over the price level (Sims, 1994) and given thesubstantially higher level of global public indebtedness after the financial crisis, there is littleevidence of such an interaction in our data set.The remainder of our paper is organised as follows. After reviewing the literature in Section 2,we outline the construction of the data and summarise its time series properties in Section 3.Section 4 describes our empirical analysis, including the methodology employed, and presents theresults and robustness checks. Section 5 concludes.2. LITERATURE REVIEWMany previous studies have explored the relationship between monetary growth and inflation.In a seminal article, Lucas (1980) applies statistical filters to characterise the relationship betweenM1 and consumer prices in U.S. data. He finds that the relationship becomes more regular, witha coefficient closer to one, as the filtering process focuses on the low frequencies in the twotime series (i.e. the long-run relationship). Lucas (1980, p. 1005) claims that the low-frequencyrelationship he finds represents “one way in which the quantity-theoretic relationships can beuncovered via a-theoretical methods from time-series which are subject to a variety of otherforces.”McCandless and Weber (2005) found a high (almost unit) correlation between the rate ofmonetary growth and the rate of inflation in a cross-country panel. This result is robust acrossdifferent definitions of money and across various sub-samples of countries. Benati (2009) hasshown that, over the last two centuries, the fraction of long-run variation in inflation explainedby long-run money growth has been very high and relatively stable. Moreover, he sheds lighton the unit relation associated with the quantity theory of money. He shows that infrequent butsignificant inflationary bursts underpin the one-for-one correlation between money growth andinflation.Over the past decade, the global dimension of the money-inflation relationship has receivedincreasing attention. From a forecasting perspective, Ciccarelli and Mojon (2010) offer evidencethat a common international component accounts for 70 per cent of the variance in domesticinflation in industrialized economies. D’Agostino and Surico (2009) stress that global liquidityproduces forecasts of US inflation that are significantly more accurate than the forecasts based onUS money growth and country-specific components alone. Such analyses build on the insight ofMcKinnon (1984), offered more than a quarter of a century ago.With these stylised facts in mind, a growing number of studies have analysed potentialtransmission channels from money to global inflation via vector autoregression models, in bothreduced form (VAR) and structural form (SVAR), with aggregated global data.Sousa and Zaghini (2007) constructed a global aggregate for the G5 economies witha starting date in the early 1980s and apply a SVAR approach to the data. They find that pricesrespond significantly and positively to global liquidity shocks. Their result is robust to variouscompositions of the aggregate. Consequently, they argue that cross-country monetary flows – e.g.,capital flows – may make it more difficult to disentangle the relation between money, inflationand output at the regional or national level. Rueffer and Stracca (2007) analysed a similar groupof countries over the sample 1980–2004 and used a similar set of variables. They found supportfor the conjecture that monetary aggregates may convey some useful information on variablesthat matter for inflation, concluding that liquidity is a useful indicator of inflationary pressures atthe global level. They also studied the impact of global phenomena on domestic variables. In thiscase, they found that the channels through which liquidity can be transmitted cross-borders aremore elusive and ambiguous. Additionally they also augmented the global model with property Faculty of Management University of Warsaw. All rights reserved.DOI: 10.7172/2353-6845.jbfe.2015.1.352

Luca Gattini, Huw Pill, Ludger Schuknecht Journal of Banking and Financial Economics 1(3)2015, 50–76and equity prices and they did not find significant evidence of excess liquidity impacting on assetvalues at a global level.Belke et al. (2010a) expand on this previous literature. They constructed aggregate timeseries for the major OECD economies and, by the means of a cointegrated VAR, showed thatthe inclusion of commodity prices helps to identify a relevant transmission mechanism fromglobal liquidity to other macroeconomic variables. In other words, global liquidity containsimportant information for the evolution of commodity prices, which in turn are an importantdriver of aggregate demand and inflation. This supports the view that commodity movements are,to some extent, an outcome of a monetary phenomenon, with causality running from monetaryvariables to commodity prices. Belke et al. (2010b) develop this analysis by introducing houseprices in place of commodity prices and find a significant response of house prices to globalliquidity (in contrast with Ruffer and Stracca (2007)). They also find a significant and positiverelationship between global and regional liquidity (either credit or money), on the one hand, andcountry specific developments of asset prices, on the other, corroborating the work of Alessi andDetken (2009) and Agnello and Schuknecht (2009). Interestingly, Belke et al. (2010b) also findsubsequent spillovers from asset prices to consumer prices at the global level. Moreover, they finda positive impact of house prices on global liquidity, which is interpreted as an effect of increaseddemand for credit. Accordingly they find house prices are an important component of globalinflation dynamics and this of their model.As regards monetary and fiscal policy interactions, the potential effects of public debton inflation have not been studied at the global level thus far. In general, the so-called fiscaltheory of the price level is based on fiscal and monetary policy rules such that the price level isdetermined by government debt and fiscal policy alone, rather than by monetary policy settings.In a theoretical framework, Sims (1994) stresses that in a fiat-money economy, the value of fiatmoney depends on public beliefs about fiscal policy under circumstances that are never observedin equilibrium: in this context, inflation is a fiscal phenomenon. Little empirical evidence haslooked into the usefulness of a fiscal rule in accounting for the evolution of prices. Empiricalanalysis conducted in Canzonieri et al. (2001) support the evidence of Ricardian regimes. This inturn suggests that prices are determined in a conventional way, say by money supply and demand.Public debt could then have an inflation increasing effect indirectly via more aggregate demand.3. DATA3.1. Time series constructionOur analysis covers the period spanning 1960 to 2010. We employ eight time series, eachwith quarterly frequency. Seven of these eight variables are constructed by aggregating nationaldata: money (M), credit to the private sector (CRP), real GDP (Y), the consumer price index(CPI), house prices (RPP), the stock of public debt (D) and the level of short-term interest rates(INT). We also use a global commodity price index (COM), which has been constructed by TheEconomist newspaper.Two selection criteria have been applied to the aggregating algorithm. First, a country i isincluded at time t when the eight series jointly are available at time t for that country i. Thisnecessary condition facilitates the comparison across the aggregated variable since the samebasket of countries is considered in each period t. Second, the country coverage in each period trepresents at least 68 per cent of world GDP measured in terms of Purchasing Power Parity(PPPs) and an overall average coverage of at least 70 per cent up to period t. Faculty of Management University of Warsaw. All rights reserved.DOI: 10.7172/2353-6845.jbfe.2015.1.353

Luca Gattini, Huw Pill, Ludger Schuknecht Journal of Banking and Financial Economics 1(3)2015, 50–76The PPPs and current GDP levels are taken from the Penn World Table Version 8.0.3 Thisinformation is used to construct the country weights applied in the aggregation procedure. Toobtain global aggregated time series, we follow a similar approach to Belke et al. (2010) (itselfbased on Beyer et al. (2001)). For a detailed description of the aggregation procedure and theweights employed, refer to Annex A.Table 1 reports the detailed country coverage and representativeness of the aggregated series.The initial coverage in 1960 included seven economies that accounted for more than 70 per centof world GDP. The table lists countries depending on the year of entry into the time series. Thefull coverage incorporates twenty-eight countries.4 Since the necessary condition previouslydescribed holds at that time only and, more generally, the main constraint in the aggregation isthe availability of a house price index, countries enter the aggregate at different times (and somere-enter).Table 1Country coverage and GDP-PPPs weight in the world economyYearCountry coverage% of world GDP-PPPs1960Australia, Canada, France, United Kingdom, Japan, Netherlandsand United States72.31962Germany73.01965Italy72.71966South Africa72.91970Denmark, Finland, Ireland, New Zealand and Switzerland71.41971Spain73.51976Belgium and ia68.81988Portugal69.61990Korea68.41993Hong Kong68.11995Thailand and Greece70.91998China73.01999Malaysia72.3Source: IMF.The sources for the entire set of variables are primarily IMF, OECD, ECB, Haver Analytics,the Global Financial Database and The Economist newspaper. (For more details on data sources,see Annex B.) The aggregated series have been seasonally adjusted with X-12 methodology,applying an additive seasonal adjustment.53Feenstra, R.C., R. Inklaar and M.P. Timmer (2013). “The next generation of the Penn World Table,” available for download at www.ggdc.net/pwt.4viz., Australia, Austria, Belgium, Canada, Switzerland, People Republic of China, Denmark, Spain, Finland, France, United Kingdom,Germany, Greece, Honk Kong, Ireland, Italy, Japan, South Korea, Malaysia, Netherlands, Norway, New Zealand, Portugal, Singapore, Sweden,Thailand, United States and South Africa.5The estimation results reported in the next section have been cross checked with series non-seasonally adjusted and no significant differenceshave been detected. Faculty of Management University of Warsaw. All rights reserved.DOI: 10.7172/2353-6845.jbfe.2015.1.354

Luca Gattini, Huw Pill, Ludger Schuknecht Journal of Banking and Financial Economics 1(3)2015, 50–763.2. Data inspectionWe investigate the statistical properties of the newly constructed series. An initial inspectionof the time series can help to gauge the main cycles and potential co-movements across thevariables. Chart 1 reports inflation developments against the other seven variables enteringour analysis. All variables are reported as a twelve window moving average of the quarter onquarter growth rates. This is done to smooth out the volatility and still capture the major trendsfor presentational purposes, whereas in our empirical analysis we employ higher (i.e., quarterly)frequency fluctuations.Chart 1Inflation against the other variables (twelve period moving average of quarter on quarter growth rates)4%a.a.c.5% 5%4%c.c.4%4%3%3% 3%2%2% 2%1%1% 1%0%0% 0%-1%-1%-1%5%c.c.c. c.e.4%4% 4%6%6% b.6%6%b.6%5%6%6%5%b.5%5%5%4% 5%4%4% 5% 4%4%3%4%4%3%3%3%3%2% 3%2%2% 3% 2%2%1% 2%1%1% 2% 1%1%0% 1%0%0% 1% 0%0% 0%0%-1%-1%-1%-1%-1%-2% -1%-2%-2%-1% -2%-2%-2%-2%e.e. e.4%4%d.4%d.4%4% 4%4% d.3%3%3%3%3% 3%3%2%2%2%2%2% 2%2%1%1%1%1%1% 1%1%0%0%0%0%0% PI CreditCPICreditCPICredit CPICPICPI CPI3%3% 3%2%2% 2%1%1% 1%Public DebtPublic DebtPublic DebtPublic DebtPublicCPIDebtCPI DebtPublicDebtCPI PublicCPICPICPI CPI2%2%2%2% 2%2%2%0%0%0%0% 0%0%0%GDPGDPGDPGDPCPI GDPCPIGDPGDPCPICPICPICPI CPICPICPICPI14%14%CPIInterestCPIrateInterest(RHS) rate (RHS)CPI14%CPI rate InterestInterest(RHS) rate (RHS)14%Interestrate (RHS) 14% 14%Interestrate(RHS)14%Interest rate (RHS)f.f.f. f.CommoditiesCommoditiesCommoditiesCommoditiesCPI CommoditiesCPICommoditiesCommoditiesCPICPICPICPI CPI9%9%9%9%9%9% 9%4%4%4%4%4%4% ce:See SeeAnnexB. urce: See Annex Q11960Q11960Q1-2%-2%-2%-2%-2%-2%-2%d.d. 07Q22002Q11996Q41996Q4

a VAR analysis, we find that: (1) global money demand shocks affect global inflation and global commodity prices (which, in turn, impact on inflation); (2) global asset price dynamics respond to financing cost shocks and (very modestly) to shocks to global money demand; and (3) positive house price shocks exe

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