Barnett, William and Ftiti, Zied and Jawadi, Fredj (2018): The Causal Relationships between Inflation and Inflation Uncertainty.

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Abstract
Since the publication of Friedman’s (1977) Nobel lecture, the relationship between the mean function of the inflation stochastic process and its uncertainty has been the subject of much research. Friedman postulated that high inflation causes increased inflation uncertainty. Ball (1992) produces macroeconomic theory that could justify that causality. But other researchers have found the converse causality, from increased inflation uncertainty to increased mean inflation, and postulated macroeconomic theory that could support their views. In addition, some researchers have found inverse correlation between mean inflation and inflation volatility with causation in either direction. These controversies are important, since they have different implications for economic theory and policy. We conduct a systematic econometric study of the relationship among the first two moments of the inflation stochastic process using state of the art approaches.
We propose a timevarying inflation uncertainty measure based on stochastic volatility to take into account unpredictable shocks. Further, we extend previous related literature by providing a new econometric specification of this relationship using two semiparametric approaches: the frequency evolutionary cospectral approach and the continuous wavelet methodology. We theoretically justify their use through an extension of Ballʼs (1992) model. These frequency approaches have two advantages: they provide the analyses for different frequency horizons and do not impose restriction on the data. While related literature always focused on the US data, our study explores this relationship for five major developed and emerging countries (the US, the UK, the Euro area, South Africa, and China) over the last five decades to investigate robustness of our inferences and investigate sources of prior inconsistencies in inferences among prior studies. This selection of countries permits investigation of the inflation versus inflation uncertainty relationship under different hypotheses, including explicit versus implicit inflation targets, conventional versus unconventional monetary policy, independent versus dependent central banks, and calm versus crisis periods.
Our findings depict a significant relationship between inflation and inflation uncertainty that varies with time and frequency and offer an improved comprehension of the ambiguous inflation versus inflation uncertainty relationship. This relationship seems positive in the short and medium terms during stable periods, confirming the FriedmanBall theory, while it is negative during crisis periods. In addition, our analysis identifies the phases of leading and lagging inflation uncertainty. Our general approach nests within it the earlier approaches, permitting explanation of the prior appearances of ambiguity in the relationship and identifies the conditions associated with the various outcomes.
Item Type:  MPRA Paper 

Original Title:  The Causal Relationships between Inflation and Inflation Uncertainty 
Language:  English 
Keywords:  Inflation, Inflation uncertainty, Frequency approach, Wavelet, Semiparametric approach, Stochastic volatility. 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General E  Macroeconomics and Monetary Economics > E3  Prices, Business Fluctuations, and Cycles > E31  Price Level ; Inflation ; Deflation 
Item ID:  86478 
Depositing User:  William A. Barnett 
Date Deposited:  05 May 2018 10:05 
Last Modified:  27 Sep 2019 07:08 
References:  AguiarConraria, L., Azevedo , N., Soares, M. J., 2008. Using wavelets to decompose the timefrequency effects of monetary policy. Physica A: Statistical Mechanics and its Applications. 387. 28632878. Allégret, J.P., Essaadi, E., 2011. Business cycles synchronization in East Asian economy: Evidences from timevarying coherence study. Economic Modelling. 28, 351–365. Allen, M.R., Smith, L.A., 1996. Monte Carlo SSA: Detecting irregular oscillations in the presence of colored noise, J. Climate. 9, 3373–3404. Artis, M.J., BladenHovell, R., Nachane, D.M., 1992. Instability of the velocity of money: A New approach based on the evolutionary spectrum. CEPR Discussion Paper, n° 735. Baillie, R.T., Chung, C., Tieslau, M.A., 1996. Analysing inflation by the fractionally integrated ARFIMA–GARCH model. J. Appl. Econometrics. 11, 23–40. Ball, L., 1992. Why does high inflation raise inflationuncertainty? J. Monetary Economics. 29, 371–388. Barnett, W. A., Chen, G. (2015). Bifurcation of macroeconometric models and robustness of dynamical inferences. Foundations and Trends in Econometrics. 8 (12), 1144. Barnett, W. A., Serletis, A. and Serletis, D. (2015). "Nonlinear And Complex Dynamics In Economics," Macroeconomic Dynamics, Cambridge University Press, vol. 19(08), pages 17491779. Barnett, W. A., Gallant, A. R., Hinich, J. A., Jungeilges, D. T., Kaplan, D. T., and Jensen, M. J., 1997. A singleblind controlled competition among tests for nonlinearity and chaos, Journal of Econometrics. 82, 157192. Barro, R.J., Gordon, D.B., 1983. Rules, discretion and reputation in a model of monetary policy. J. Monetary Economics. 12, 101–121. Ben Nasr, A., Balcilar, M., Ajmi, A.N., Aye, G.C., Gupta, R., Eyden, R., 2015. Causality between inflation and inflation uncertainty in South Africa: Evidence from a Markovswitching vector autoregressive model. Emerg. Markets Rev. 24, 46–68. Bernanke, B.S., Mishkin, F.S., 1992. Central bank behavior and the strategy of monetary policy: Observations from six industrialized countries. NBER Working Paper No. 4082. Berument, H., Yalcin, Y., Yildirim, J., 2009. The effect of inflation uncertainty on inflation: Stochastic volatility in mean model within a dynamic framework. Economic Modelling. 26, 1201–1207. Bloomfield, D.S., McAteer, R.T.J., Lites, B.W., Judge, P.G., Mathioudakis, M., Keenan, F.P., 2004. Wavelet phase coherence analysis: Application to a quietsun magnetic element. The Astrophysical J. 617, 623–632. Bollerslev, T., Wooldridge, J., 1992. Quasimaximum likelihood estimation and inference in dynamic models with timevarying covariance. Econometric Rev. 11, 143–172. Bouoiyour, J. and Selmi, R. 2014. Nonlinearities and the nexus between inflation and inflation uncertainty in Egypt: New evidence from wavelet transform framework. MPRA, No. 55721. Canzoneri, M., 1985. Monetary policy games and the role of private information. American Economic Review. 75. 10561070. Caporale, G.M., Kontonikas, A., 2009. The Euro and inflation uncertainty in the European Monetary Union. J. International Money and Finance 28(6): 954971. Carnero, M. A., Pena, D., and Ruiz, E. 2004. Persistence and kurtosis in GARCH and stochastic volatility models. Journal of Financial Econometrics 2:3, 19–342. Chan, J. and Grant, A. 2016. Modeling energy price dynamics: GARCH versus stochastic volatility. Energy Economics 54, 182189. Chan J.C.C., Hsiao, C.Y.L., 2014. Estimation of stochastic volatility models with heavy tails and serial dependence, in: Jeliazkov, I. Yang, X.S. (Eds.), Bayesian Inference in the Social Sciences. John Wiley & Sons, Hoboken. Chan, J.C.C., 2013. Moving average stochastic volatility models with application to inflation forecast. J. Econometrics. 176 (2), 162–172. Chan, J.C.C., 2015. The stochastic volatility in mean model with timevarying parameters: An application to inflation modeling. J. Bus. Economic Statistics. Forthcoming. Chang, K., 2012.The impacts of regimeswitching structures and fattailed characteristics on the relationship between inflation and inflation uncertainty. J. Macroeconomics 34,523–536. Conrad, C., Karanasos, M., 2005. On the inflationuncertainty hypothesis in the USA, Japan and the UK: A dual long memory approach. Jpn. World Economy. 17, 327–343. Creal, D.D., Wu, J.C., 2014. Interest rate uncertainty and economic fluctuations, Working paper n°1432 4. The University of Chicago Booth School of Business. Cukierman, A., Meltzer, A.H., 1986. A theory of ambiguity, credibility, and inflation under discretion and asymmetric information. Econometrica. 54, 1099–1128. Cukierman, A., 1992. Central Bank Strategy, Credibility, and Independence: Theory and evidence, MIT Press, Cambridge. Goupillaud, P., Grossman, A., Morlet, J., 1984. Cycleoctave and related transforms in seismic signal analysis. Geoexplor. 23, 85–102. Darné, O., 2004. Les méthodes et logiciels de désaisonnalisation des séries économiques: une revue de la littérature, Journal de la Revue Française de la Statistiques. 145, 79–102. Emery, K.M., 1993. Inflation and its variability: An alternative specification. Appl. Economics 25, 43–46. Evans, M., 1991. Discovering the link between inflation rates and inflation uncertainty. J. Money, Credit Bank. 23 (2), 169–184. Evans, M., Wachtel, P. 1993. Inflation regimes and the sources of inflation uncertainty. J. Money Credit Bank. 25, 475–511. Ferreira, D. and Palma, A. 2016. Inflation and inflation uncertainty in Latin America: A timevarying stochastic volatility in mean approach. Journal of Economic Studies 44(4) 506517. Fischer, S., Modigliani, F., 1978. Toward and understanding of the real effects and costs of inflation. Weltwirtschaftliches Archiv. 114, 810833. Friedman, M., 1977. Nobel lecture: Inflation and unemployment. J. Political Economy. 85, 451–472. Ftiti, Z., 2010. The macroeconomic performance of the inflation targeting policy: An approach based on the evolutionary cospectral analysis. Economic Modelling. 27, 468–476. Ftiti, Z. and Jawadi, F. (2018), Forecasting inflation uncertainty in the United States and Euro Area. Computational Economics, forthcoming. Gallegati, M., Ramsey, J.B., Semmler, W., 2014. Interest rate spreads and output: A time scale decomposition analysis using wavelets. Computational Statistics and Data Analysis. 76, 283–290. Gencay, R., Selçuk, F., and Witcher, B. (2001), An Introduction to Wavelets and Other Filtering Methods in Finance and Economics, Academic Press. Golub, J., 1994. Does inflation uncertainty increases with inflation. Federal Reserve Bank of Kansas City.Third Quarter, 2838. Gourieroux, C., Sufana, R., 2010. Derivative pricing with Wishart multivariate stochastic volatility. J. Bus. Econom. Statist. 28, 438–451. Grier, K.B., Perry, M.J., 1998. On inflation and inflation uncertainty in the G7 countries. J. International Money and Finance. 17, 671–689. Grinsted, A., Moore, J., Jevrejeva, S., 2004.Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics.11,561–566. Haven, E., Liu, X., Shen, L., 2012. Denoising option prices with the wavelet method. European J. Operational Res. 222, 104–112. Holland, S., 1993. Comment on inflation regimes and the sources of inflation uncertainty. J. Money Credit Bank. 25, 514–520. Holland, S., 1995. Inflation and uncertainty: Tests for temporal ordering. J. Money Credit Bank. 27, 827–837. Johnson, D.R., 2002. The effect of inflation targeting on the behavior of expected inflation: Evidence from an 11 country panel. J. Monetary Economics. 49, 1521–1538. Kontonikas, A., 2005. Inflation and inflation uncertainty in the United Kingdom, evidence from GARCH modelling, Economic Modelling. 21, 525–543. Koopman, S.J., Mallee, M.I., Van der Wel, M., 2010. Analyzing the term structure of interest rates using the dynamic Nelson–Siegel model with timevarying parameters. J. Bus. Economic Statistics. 329–343. Liu, Y., Liang, X.S., Weisberg, R.H., 2007. Rectification of the bias in the wavelet power spectrum. J. Atmospheric Ocean. Technology. 24, 2093–2102. Logue, D., Willett, T., 1976. A note on the relation between the rate and variability of inflation. Economica. 43, pp. 151158. Madaleno, M., Pinho, C., 2014. Wavelet dynamics for oilstock world interactions. Energy Economics. 45, 120–133. Mallick, S, and Sousa, R. (2013), “Commodity prices, inflationary pressures, and monetary policy: Evidence from BRICS economies”, Open Economies Review, 24(4), 677694. Mallick, S. and Mohsin, M. (2016) Macroeconomic Effects of Inflationary Shocks with Durable and NonDurable Consumption, Open Economies Review, 27(5): 895921. Neanidis, K.C., and Savva, C.S., 2011. Nominal uncertainty and inflation: The role of European Union membership. Economic letters. 112, 26–30. Nelson, D. B., 1991. Conditional heteroskedasticity in asset returns: A new approach. Econometrica. 59, 347—370. Ng, E.K., Chan, J.C., 2012. Geophysical applications of partial wavelet coherence and multiple wavelet coherence. J. Atmospheric Ocean. Technology. 29, 1845–1853. Pourgerami A, Maskus KE (1987) The effects of inflation on the predictability of price changes in Latin America: Some estimates and policy implications. World Development 15(2): 287–290. Priestley, M.B., 1965. Evolutionary spectra for nonstationary processes. J. R. Statistical Society. Series B 27, 204–237. Priestley, M.B., 1966. Design relations for nonstationary processes. J. R. Statistical Society. Series B 28, 228–240. Priestley, M.B., 1988. NonLinear and NonStationary Time Series Analysis, Academic Press, London. Priestley, M.B., Tong, H., 1973. On the analysis of bivariate nonstationary processes. J. R. Statistical Society. Series B 35, 135–166. Rua, A., Nunes, L.C., 2012. A waveletbased assessment of market risk: The emerging markets case. Q. Rev. Economics Finance. 52, 84–92. Rossi, B., Sekhposyan, T. and Soupre, M. 2016. "Understanding the Sources of Macroeconomic Uncertainty," Working Papers 920, Barcelona Graduate School of Economics. Serletis, A. and Rahman, S., 2009a. The output effects of money growth uncertainty: Evidence from a multivariate GARCHinMean VAR. Open Economies Review. 20, 607630. Serletis, A. and Rahman, S., 2009b. On the output effects of monetary variability. Open Economies Review 26. 225236. Serletis, A. and Shahmoradi, A., 2006. Velocity and variability of money growth: Evidence from a VARMA, GARCHM model. Macroeconomic Dynamics. 10, 652666. Serletis, A. and Xu, L., 2017. Money supply volatility and the macroeconomy, University of Calgary, working paper. Torrence, C., Compo, G.P., 1998. A practical guide to wavelet analysis, Bull. American Meteorological Society. 79, 605–618. Torrence, C., Webster, P., 1999. Interdecadal changes in the ENSOmonsoon system. J. Climate. 12, 2679–2690. Ungar, M., Zilberfarb, B., 1993. Inflation and its unpredictability—theory and empirical evidence. J. Money, Credit Bank. 25, 709–720. Van Bellegem, S., 2013. Locally stationary volatility modelling, in: Bauwens, L., Hafner, C., Laurent, S. (Eds.), Handbook in Financial Engineering and Econometrics: Volatility models and their applications. Wiley, New York, chapter 10. Van Bellegem, S., Von Sachs, R., 2008. Locally adaptive estimation of evolutionary wavelet spectra. J. Statistics. 36, 1879–1924. Veleda, D., Montagne, R., Araujo, M., 2012. Crosswavelet bias corrected by normalizing scales. J. Atmospheric Ocean. Technology. 29, 1401–1408. Zapodeanu, D., Cociuba, ML., Sorina, P., 2014. Inflation uncertainty and inflation in the case of Romania, Czech Republic, Hungary, Poland and Turkey. Procedia Economics and Finance. 15, 1225–1234. Zar, J.H., 1999. Biostatistical Analysis, fourth ed. Prentice Hill. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/86478 