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Empirical Economics

Erschienen in:

24.08.2016

Structural shocks and dynamic elasticities in a long memory model of the US gasoline retail market

verfasst von: Yuliya Lovcha, Alejandro Perez-Laborda

Erschienen in: Empirical Economics | Ausgabe 2/2017

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Abstract

A structural multivariate long memory model of the US gasoline market is employed to disentangle structural shocks and to estimate the own-price elasticity of gasoline demand. Our main empirical findings are: (1) there is strong evidence of nonstationarity and mean reversion in the real price of gasoline and in gasoline consumption; (2) accounting for the degree of persistence present in the data is essential to assess the responses of these two variables to structural shocks; (3) the contributions of the different supply and demand shocks to fluctuations in the gasoline market vary across frequency ranges; and (4) long memory makes available an interesting range of convergent possibilities for gasoline demand elasticities. Our estimates suggest that after a change in prices, consumers undertake a few measures to reduce consumption in the short- and medium-run but are reluctant to implement major changes in their consumption habits.

Vorheriger Artikel Are state–local government expenditures converging? New evidence based on sequential unit root tests

Nächster Artikel Time-varying persistence in US inflation

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A notable multivariate exception can be found in Haldrup et al. (2010).

Throughout this paper, we measure persistence by the speed of mean reversion, as in Diebold and Rudebusch (1989). Note that if persistence is measured by the traditional infinite sum of impulse responses, two FI processes cannot be compared, since persistence would be either 0 for negative orders of fractional integration, or infinity for positive (see e.g. Hauser et al. 1999).

In fact, the importance of frequency-domain concepts in the relation between energy prices and the economy was already emphasized by Granger (1966). More recent studies include Granger and Lin (1995), Gronwald (2008), or Aguiar-Conraira and Soares (2011).

Fractional cointegrated models, as Johansen (2008) or Johansen and Nielsen (2012), require equal coefficients of fractional integration for all variables.

Given that we expect large orders of integration (as they are indeed), we difference the series prior to estimation and we subsequently transform them back by adding 1 to the estimated FI orders. Computing the periodogram, we taper the data with the cosine bell taper. See Robinson and Velasco (2000) for details.

The effects of long memory on other identification procedures have been discussed in Tschering et al. (2013) and Lovcha and Perez-Laborda (2015).

The gasoline demand is the sum of gasoline consumption in industrial, commercial, and transportation sectors.

http://www.eia.gov/totalenergy/data/monthly/.

http://www-personal.umich.edu/~lkilian/reaupdate.txt.

http://research.stlouisfed.org/fred2/.

For testing this hypothesis, we assume one autoregressive lag in the VAR, ensuring that the two models are nested. As in previous VAR literature, the 1st and 5th variables are assumed to be I(1) and enter to the model in differences while the other variables enter in levels. Given that we pre-difference data prior to FIVAR estimation (see footnote 5), to make the models comparable we transform back to levels only the 2nd, 3rd, and 4th variables, leaving the 1st and 5th in differences (with negative orders of integration). For the LR statistic, we estimate both models in the frequency domain and compute the values of the likelihood function. We bootstrap the empirical distribution of this statistic using both residual-based and frequency-domain bootstrap methods. We generate 500 bootstrap replications in each case.

Although there are some techniques to distinguish between fractional integration and short memory processes containing trends and/or breaks, most of them deal with a single series and have not been extended to the multivariate case.

We have also taken April 1991 as the initial date (just after the oil price shock and the end of the early 1990s recession) and November 2007 as the final date (before the beginning of the Great Recession) obtaining similar results.

To compute confidence intervals we produce 500 bootstrap replications. Conditions for the application of the bootstrap are satisfied for all frequencies except for frequency 0. Consistent with standard practice, the 0 frequency is excluded from estimation and bootstrap.

Kilian (2010) selects 14 lags for the VAR (without using a formal criterion) to yield sufficient persistence in the responses of real prices to shocks. Note that this model requires the estimation of 365 parameters. As a robustness check, we also recover IRFs from a VAR(14). It turns out that price responses to shocks from the FIVAR(1) model also converge to 0 more slowly than those of a VAR(14). This is because FIVAR models exhibit hyperbolic decay of the autocorrelations, while autocorrelations in VARs decay at a faster exponential rate. In fact, the IRFs of the VAR(14) and the VAR(2) do not differ much, especially if one is guided by their statistical significance. In this sense, parsimony is another justification of the FIVAR model. We thank a referee for pointing this last issue out.

If the order integration is strictly positive at 0, the spectrum tends to infinity at this frequency. Consistent with standard procedure, we have excluded the 0 frequency for the estimation and also for posterior analysis.

Business cycle corresponds to a range of frequencies with period from 1.5 to 8 years; high frequencies with a period smaller than or equal to 1 year.

Besides, finding a valid instrument for the price is not an easy task. Previous literature has employed tax changes, but this strategy delivers unreasonably large elasticity estimates. Coglianese et al. (2016) argue that consumers shift gasoline purchases in anticipation of tax changes, which may affect the validity of the instrument. The authors circumvent this problem by employing suitable leads and lags, which results in a substantial reduction of the elasticity estimate.

The coefficients of real economic activity index can be interpreted in the following way: if the economic activity index increases 1 unit (1 % since this index is expressed in %), gasoline demand increases\(100\times C_2 \left( L \right) \% \).

Using data over the period 1966–2001, Small and Dender (2007) find short-run and long-run elasticities of 0.04 and −0.22, respectively. These numbers fall to −0.02 and −0.10 for the period 1997–2001. Hughes et al. (2008) also document a decrease in the short-run elasticity. For the period 1975–1980, they find estimates ranging from −0.21 to −0.34 depending on the model, but these values fall to −0.034 to −0.077 for the period 2001–2006.

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Titel: Structural shocks and dynamic elasticities in a long memory model of the US gasoline retail market
verfasst von: Yuliya Lovcha
Alejandro Perez-Laborda
Publikationsdatum: 24.08.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 2/2017
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-016-1145-x

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