Skip to main content
SpringerLink
Log in

Measuring core inflation in Italy comparing aggregate vs. disaggregate price data

  • Original Paper
  • Published:
Cliometrica Aims and scope Submit manuscript

Abstract

This paper focuses on the core inflation measurement in Italy using univariate (national-level inflation) vs. multivariate (city-level inflation) models during the period 1970–2006. We derive algebraic expressions that allow comparison between the reduced form parameters of univariate and multivariate local level models in the context of contemporaneous and temporal aggregation. We illustrate the relevance of these theoretical results for the empirical analysis of time series. Using Italian data, we find that multivariate and univariate models extract similar core inflation measures when analyzing the moderate-low inflation period. In contrast, the two competing models yield different trends when modeling the Great Inflation period.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Other approaches have been adopted to analyze the dynamics of inflation in Italy. For example, a common trends model applied to a small-scale macroeconometric system including the inflation rate, a measure of economic activity, oil prices, money, and wage growth rates covering the period 1970–2006 was used by Bagliano and Morana (1999) to estimate core inflation in Italy. Recently, Bacchiocchi (2009) investigated the determinants of inflation in Italy since the beginning of the 1970s until the first years after the launch of the European Monetary Union.

  2. We refer to Mills (2009) for a retrospective analysis on the modeling of trends and cycles in time series.

  3. We use the FOI CPI (“Indice dei Prezzi al Consumo per le Famiglie di Operai e Impiegati”): it is a index for industrial and clerical workers households, which was closely watched in Italy, especially before the EU harmonized consumer price index (HICP) was published. The regional capitals are Aosta, Torino, Milano, Trento, Venezia, Trieste, Genova, Bologna, Firenze, Perugia, Ancona, Roma, L’Aquila, Campobasso, Napoli, Bari, Reggio Calabria, Palermo, Cagliari.

  4. Test results are not reported due to space limitations but are available from the authors upon request.

  5. In fact, as first shown by Wei and Stram (1988), when \(m\rightarrow \infty\), both \(\Uptheta_c\) and \(\Uptheta_a\) converge to a limit of 0.268, which corresponds to the eigenvector associated with the largest eigenvalue of the aggregation matrix (i.e., the matrix that maps the autocovariances of the disaggregate model to the autocovariances of the aggregate one).

References

  • Altissimo F, Ehrmann M, Smets F (2006) Inflation persistence and price-setting behaviour in the Euro area. A Summary of the IPN Evidence, ECB Occasional Paper Series No 46

  • Altissimo F, Mojon B, Zaffaroni P (2009) Can aggregation explain the persistence of inflation? J Monet Econ 56:231–241

    Article  Google Scholar 

  • Andrews DWK (1993) Tests for parameter instability and structural change with unknown change point. Econometrica 61:821–856

    Article  Google Scholar 

  • Andrews DWK, Ploberger W (1994) Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 62:1383–1414

    Article  Google Scholar 

  • Bacchiocchi E (2009) Modelling Italian inflation, 1970–2006. Rivista di Politica Economica Anno 99(1):65–110

  • Bagliano FC, Morana C (1999) Measuring core inflation in Italy. Giornale degli Economisti e Annali di Economia 58:301–328

    Google Scholar 

  • Bryan MF, Cecchetti SG (1994) Measuring core inflation. In: Mankiw SG (ed) Monetary policy. The University of Chicago Press, Chicago

    Google Scholar 

  • Beck GW, Hubrich K, Marcellino M (2006) Regional inflation dynamics within and across Euro area countries and a comparison with the United States. ECB Working Paper Series No 681

  • Busetti F, Fabiani S, Harvey AC (2006) Convergence of prices and rates of inflation. Oxf Bull Econ Stat 68:863–877

    Article  Google Scholar 

  • Busetti F, Forni L, Harvey A, Venditti F (2007) Inflation convergence and divergence within the European monetary union. Int J Cent Bank 3:95–121

    Google Scholar 

  • Cecchetti SG, Mark NC, Sonora RJ (2002) Price index convergence among United States cities. Int Econ Rev 43:1081–1099

    Article  Google Scholar 

  • Chow GC (1960) Tests of equality between sets of coefficients in two linear regressions. Econometrica 28:591–605

    Article  Google Scholar 

  • Doornik JA, Hansen H (1994) An omnibus test for univariate and multivariate normality. Economics Working Papers W4 and 91, Nuffield College, Oxford

  • Engel C, Rogers JH (1996) How wide is the border? Am Econ Rev 86:1112–1125

    Google Scholar 

  • European Central Bank: (2004) The monetary policy of the ECB. Frankfurt, Germany

    Google Scholar 

  • Fratianni M, Spinelli F (1997) A monetary history of Italy (trans: Schwartz AJ). Cambridge University Press, Cambridge

  • Gaiotti E (1999) The transmission of monetary policy shocks in Italy, 1967–1997. Bank of Italy, Temi di Discussione, 363

  • Giavazzi F, Spaventa L (1989) Italy: the real effects of inflation and disinflation. Econ Policy 4:133–172

    Article  Google Scholar 

  • Gressani D, Guiso L, Visco I (1988) Disinflation in Italy: an analysis with the econometric model of the Bank of Italy. J Policy Model 10:163–203

    Article  Google Scholar 

  • Guiso L, Magnani M (1985) 1981–1984 Perché l'inflazione è calata? Bank of Italy, Lavoro Preparatorio per la Relazione Annuale sul 1984, Rome, Italy

  • Hansen BE (1997) Approximate asymptotic p values for structural-change tests. J Bus Econ Stat 15:60–67

    Article  Google Scholar 

  • Hansen BE (2001) The new econometrics of structural change: dating breaks in U.S. labor productivity. J Econ Perspect 15:117–128

    Article  Google Scholar 

  • Harvey AC (1989) Forecasting structural time series and the Kalman filter. Cambridge University Press, Cambridge

    Google Scholar 

  • Hotta LK, Vasconcellos KL (1999) Aggregation and disaggregation of structural time series models. J Time Ser Anal 20:155–171

    Article  Google Scholar 

  • Hubrich K (2005) Forecasting Euro area inflation: does aggregating forecasts by HICP component improve forecast accuracy? Int J Forecast 21:119–136

    Article  Google Scholar 

  • Koopman SJ, Harvey AC, Doornik JA, Shephard N (2007) STAMP 8.2 structural time seires analyser, modeller and predictor. Timberlake Consultants Ltd, London

    Google Scholar 

  • Lütkepohl H (2010) Forecasting nonlinear aggregates and aggregates with time-varying weights. CESifo Working Paper No. 3031

  • Marcellino M, Stock JH, Watson MW (2003) Macroeconomic forecasting in the Euro area: country specific versus area-wide information. Eur Econ Rev 47:1–18

    Article  Google Scholar 

  • Mills TC (2009) Modelling trends and cycles in economic time series: historical perspective and future developments. Cliometrica 3:221–244

    Article  Google Scholar 

  • Morley JC, Nelson CR, Zivot E (2003) Why are the Beveridge-Nelson and unobserved components decompositions of GDP so different? Rev Econ Stat 85:235–243

    Article  Google Scholar 

  • Paya I, Duarte A, Holden K (2007) On the relationship between inflation persistence and temporal aggregation. J Money Credit Bank 39:1521–1531

    Article  Google Scholar 

  • Proietti T (2007) Measuring core inflation by multivariate structural time series models. In: Kontoghiorghes EJ, Gatu C (eds) Optimisation, econometric and financial analysis, book series on “Advances in computational management science”, vol 9, Part II, pp 205–223

  • Quah D, Vahey SP (1995) Measuring core inflation. Econ J 105:1130–1144

    Article  Google Scholar 

  • Silvestrini A, Veredas D (2008) Temporal aggregation of univariate and multivariate time series models: a survey. J Econ Surv 22:458–497

    Article  Google Scholar 

  • Stock JH, Watson MW (2007) Why has inflation become harder to forecast? J Money Credit Bank 39:3–34

    Article  Google Scholar 

  • Vaona A, Ascari G (2007) Regional inflation persistence: evidence from Italy. Quaderno N. 08-07, Institute for Economic Research, University of Lugano, Switzerland

  • Wei WWS, Stram DO (1988) An eigenvalue approach to the limiting behaviour of time series aggregates. Ann Inst Stat Math 40:101–110

    Article  Google Scholar 

  • Wynne MA (1999) Core inflation: a review of some conceptual issues. ECB Working Paper Series No 5

Download references

Acknowledgments

While assuming the scientific responsibility for any error in the paper, the authors thank the Editor and two anonymous referees for their helpful suggestions. The authors are also grateful to Marco Magnani, Matteo Piazza, Brooke Rutherford, and Fabrizio Venditti for useful comments. The views expressed in this paper are those of the authors and do not necessarily reflect the positions or policies of the Bank of Italy.

Author information

Authors and Affiliations

  1. Université de Strasbourg, Bureau d’Économie Théorique et Appliquée (BETA), 61 Avenue de la Forêt Noire, 67085, Strasbourg Cedex, France

    Giacomo Sbrana

  2. Economics, Research and International Relations, Economic and Financial Statistics Department, Bank of Italy, Via Nazionale, 91, 00184, Roma, Italy

    Andrea Silvestrini

Authors
  1. Giacomo Sbrana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Silvestrini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Silvestrini.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sbrana, G., Silvestrini, A. Measuring core inflation in Italy comparing aggregate vs. disaggregate price data. Cliometrica 5, 239–258 (2011). https://doi.org/10.1007/s11698-010-0059-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11698-010-0059-7

Keywords

JEL classification

Search

Navigation