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Inflation and relative price variability: new evidence from survey-based measures of inflation expectations in Australia

Abstract

For the first time in the literature, this paper uses survey-based measures of inflation expectations to examine the relationship between inflation and relative price variability (RPV). Using quarterly consumer price index data for 74 consumption categories in Australia from 1989 to 2014, we estimate the basic linear and piecewise liner models to investigate the impacts of expected and unexpected inflation on RPV. Both headline and core inflation measures are used. The results show a statistically significant and robust positive impact of unexpected inflation on RPV. There is little evidence of asymmetry between the effects of positive and negative inflation shocks. This paper further investigates the specific functional form of the inflation-RPV relationship. While the results suggest a J-shaped nonlinear relationship between inflation and unexpected inflation, there is little evidence of any specific functional form for an expected inflation-RPV relationship. Finally, two structural breaks in the inflation-RPV relationship are identified: 2003Q2 and 2007Q2 for headline inflation and 2000Q2 and 2006Q2 for core inflation. The first two regimes are characterized by a positive and convex association between RPV and unexpected inflation, which disappears in the third regime. The results are qualitatively similar when the model is re-estimated using standard forecast-based inflation expectation measures, suggesting that the traditional approach captures the inflation-RPV relationship reasonably well, at least for Australia.

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Fig. 1
Fig. 2

Notes

  1. 1.

    There is a broader literature on the relationship between inflation and distribution of relative prices that allows for two additional possibilities: one of different moments of relative price distribution affecting inflation and the other of both inflation and different moments being simultaneously affected by some common factors. For example, Ball and Mankiw (1995) consider relative price variability/skewness as aggregate supply shocks that drive inflation. Balke and Wynne (2000) argue that sectoral technology shocks can lead to relative price changes and aggregate inflation. Fischer (1981) gives a good summary of all three possibilities discussed in the larger literature.

  2. 2.

    A formal definition is included in Sect. 2.

  3. 3.

    In a related study, Lourenco and Gruen (1995) examine the effect of relative price shocks on inflation. They find that a rise in the economy-wide dispersion of shocks is inflationary only when expected inflation is high.

  4. 4.

    Appendix Table 9 lists these expenditure items along with their respective weights.

  5. 5.

    Since Parks’ seminal work in 1978, the majority of empirical literature has been using variations in relative price changes as a measure of RPV. However, as Danziger (1987) and Hajzler and Fielding (2014) show, variability of relative price changes (they call it relative inflation variability-RIV) does not always capture RPV. In this paper, inflation is an increase in the general price level as opposed to changes in prices of different expenditure items. Furthermore, the theoretical discussion on the distinction between RPV and RIV focuses on the variability across locations, not across commodities as in this paper. Therefore, following the convention in the empirical literature, we stick to the RPV measure as defined in Eq. (1).

  6. 6.

    Alternatively, we could construct a core inflation measure based on all groups CPI excluding food and energy (i.e. food and non-alcoholic beverages except restaurant meals, electricity, gas and other household fuels, and automotive fuel), similar to that used in the United States and other countries. However, this measure exhibits volatility very similar to headline inflation, particularly until about 2000.

  7. 7.

    See, for example, Jaramillo (1999) and Nautz and Scharff (2012).

  8. 8.

    The computational procedure is handled by a Stata plug-in, called lpoly. The Stata plug-in bypasses the need to compute full-blown nonparametric regressions required for each point in the smoothing grid, and only estimates the intercepts from the polynomial regression fitted around \( x_{0} \). Therefore, considerable efficiency is gained relative to that required in obtaining bootstrapped standard errors.

  9. 9.

    The mean and standard deviation of RPV for the period: 1989Q4–2006Q1 were 2.04 and 0.84 and, for 2006Q2–2013Q3, they were 2.71 and 1.46 respectively.

  10. 10.

    Productivity growth accelerated from a long run average of 1.2–2.4% per year during this period of time (Parham 1999). Business investment as a ratio of GDP rose from a little over 10% in 1992 to more than 14% in 1997 (RBA 2012).

  11. 11.

    For example, as per the US Energy Information Administration, the standard deviation of West Texas Intermediate prices increased from 11.65 during 2000Q1–2005Q4, to 18.26 during 2006Q1–2014Q4. Available at: https://www.eia.gov/forecasts/steo/query/; accessed on Jan. 13, 2016.

  12. 12.

    It is often difficult to beat the forecasting performance of univariate time series models of inflation. For example, see Stock and Watson (2007).

  13. 13.

    We believe that people’s expectations are about headline inflation and, therefore, we model headline inflation only.

  14. 14.

    Note that the pairwise correlation coefficient between survey-based and forecast-based inflation expectation measures is 0.54 which is highly statistically significant. It also implies that the inflation forecasts based on an ARMA(2,2) model fairly represent the expectations of businesses about inflation in Australia.

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Acknowledgements

The authors would like to thank two anonymous referees and the editor, Subal Kumbhakar, for their valuable comments and suggestions. An earlier version of the paper was presented at the 89th Annual Conference of Western Economic Association International held in Denver, Colorado (USA), on June 27–July 1, 2014. The authors are thankful to Professor Hiroaki Hayakawa and the members of the audience for their comments and questions. This paper was written when Nath was a Visiting Fellow at QUT, Australia. He is grateful to the School of Economics and Finance at QUT for its hospitality and support. The authors are solely responsible for all remaining errors and omissions.

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Correspondence to Hiranya K. Nath.

Appendix

Appendix

See Table 9.

Table 9 List of expenditure items and their weights in the consumption basket

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Nath, H.K., Sarkar, J. Inflation and relative price variability: new evidence from survey-based measures of inflation expectations in Australia. Empir Econ 56, 2001–2024 (2019). https://doi.org/10.1007/s00181-018-1422-y

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Keywords

  • Inflation
  • Relative price variability
  • Australia
  • Survey-based measures of inflation expectations
  • Menu cost model
  • Signal extraction model
  • Search model

JEL Classification

  • E31
  • E52