The relevance of supply shocks for inflation: the spanish case

Abstract

The methodology applied in this article to the Spanish economy is based on Ball and Mankiw (1995). These authors assume that a good proxy for supply shocks is the third moment of the price changes distribution. The main data used are the monthly consumer price indexes of each region, disaggregated in 57 categories, for the 1993–2005 period. We estimate the relation between mean inflation and the higher moments of the distribution, including several control variables. Our results point out that Spanish regions show a common pattern with regard to nominal rigidities, and that Spanish inflation is vulnerable to supply shocks.

Acknowledgements

The authors would like to thank two anonymous referees, Carlos Dabús, Diego Romero-Avila, seminar participants at the Pablo de Olavide University and Centro de Estudios Andaluces, and participants at the EEFS 5th Annual Conference (Crete, Greece, 2006) and ERSA 46th Congress (Volos, Greece, 2006) for valuable comments and suggestions. The authors also acknowledge financial support from Junta de Andalucía: Centro de Estudios Andaluces (ECO 17–2004 and ECOD1.05/033) and CICE (Proyecto de Excelencia 01252). The usual disclaimer applies.

Notes

¹ See Caraballo and Dabús (2005) for further details.

² In the Appendix we present the results for a common unit root – Breitung (2000) and Levin et al . (2002) – , and the general result is that it does not exist. Results of individual unit root tests are available from the authors upon request. The specific testing procedure adopted is the Augmented Dickey–Fuller (ADF) test with the Schwartz information criterion used to select the number of lags included in the ADF regressions. By default, the maximum number of lags allowed in the tests is 12. In the Appendix we also show the summary statistics for inflation, RPV and skewness.

³ As well known, if the lagged endogenous variable is not correlated with the error term, the validity of the OLS estimator holds. To prove that there is no correlation, we have estimated the model with OLS and verified that there is not autocorrelation in the residuals.

⁴ See Baltagi (1995, p. 126).

⁵ In order to reinforce the validity of the division in the sample period that we use in our analysis we have implemented a Chow test. The critical value of this test is 3.02 at 1% (the F statistic is 27.49) so we reject the null hypothesis of lack of a break in 1998:12.

⁶ See Baltagi (1995, p. 12).

⁷ Results for the constant term and trend are not included in the tables. They are available from the authors upon request.

⁸ More detailed information about these variables and data sources is available from the authors upon request.

⁹ Moreover, another worrying feature found in our analysis is the positive value of the skewness variable in the 63% of the sample period. This feature may indicate the presence of inflationary tension in the menu cost models framework that we use.

¹⁰ See, among others, Dolado and Jimeno (1997), Estrada and López-Salido (2002) and López-Salido et al . (2005).

¹¹ Recent contributions show the relevance of sectoral factors on the results of this kind of analysis –see, for instance, Banerjee and Russell (2001) and Nath (2004).