Academic journal article Journal of Business Strategies

Estimation of Consumption Elasticities for OECD Countries: Testing Price Asymmetry with Alternative Dynamic Panel Data Techniques

Academic journal article Journal of Business Strategies

Estimation of Consumption Elasticities for OECD Countries: Testing Price Asymmetry with Alternative Dynamic Panel Data Techniques

Article excerpt


In recent years, the dynamic panel data literature has begun to focus on panels in which the number of cross-sectional observations (N) and the number of time-series observations (T) are both large. The availability of data with greater frequency is certainly a key contributor to this shift. Some cross-national and cross-state data sets, for example, are now large enough in T such that each nation (or state) can be estimated separately. See Blackburne and Frank, (2007) for further details.

The asymptotics of large N, large T dynamic panels are quite different from the asymptotics of traditional large N, small T dynamic panels. Small T panel estimation usually relies on fixed or random effects estimators, or a combination of fixed effects estimators and instrumental variable estimators, such as the Arellano and Bond, (1991) GMM estimator. These methods require pooling individual groups and allowing only the intercepts to differ across the groups. One of the central findings from the large N, large T literature, however, is that the assumption of homogeneity of slope parameters is often inappropriate. This point has been made by Pesaran and Smith (1995); lm et al. (2003), Pesaran et al; (1997, 1999), Phillips and Moon, (2000) (1).

With the increase in time observations inherent in large N, large T dynamic panels, nonstationarity is also a concern. Recent papers by Pesaran et al. (1997, 1999) offer two important new techniques to estimate nonstationary dynamic panels in which the parameters are heterogeneous across groups: the mean-group and pooled mean-group estimators. The mean-group estimator (MG) (see Pesaran and Smith, 1995) relies on estimating TV time series regressions and averaging the coefficients, while the pooled mean-group estimator (PMG) (see Pesaran et al., 1997, 1999) relies on a combination of pooling and averaging of coefficients.

In recent empirical research, the MG and PMG estimators have been applied in a variety of settings. Freeman, (2000), for example, uses the estimators to evaluate state-level alcohol consumption over the period 1961 to 1995. Martinez-Zarzoso and Bengochea-Morancho, (2004) employ them in an estimation of an environmental Kuznets curve in a panel of 22 OECD nations over a period 1975 to 1998. Frank, (2005) uses the MG and PMG estimators to evaluate the long-term impact of income inequality on economic growth in a panel of U.S. states over the period 1945 to 2001.

This paper applies the MG and PMG estimators to a panel of OECD nations for the years 1970-2004. We present a simple dynamic model of oil consumption as a function of income and prices. As in previous studies, we allow demand to respond asymmetrically to price shocks. Specifically, this paper has three goals:

* test the degree of heterogeneity in oil consumption among the OECD nations

* test the asymmetric response of oil consumption with respect to price

* estimate precise price and income elasticities for OECD oil consumption

This paper proceeds as follows. Section 2 discusses the methods involved, including price decomposition and alternative dynamic panel estimators. Section 3 briefly describes the data. Section 4 presents the results and Section 5 concludes.


Demand Asymmetries

Following the recent work of Gately and Huntington, (2002), this paper allows for asymmetric price response in oil demand. Models that assume price symmetry when, in fact, it does not exist introduce model misspecification and downwardly bias income elasticity estimates. Accordingly, we decompose the world price of oil (in logs), Pr, into three components:

[P.sub.max,t] = max([P.sub.t], [P.sub.t-1]) (1)

[P.sub.rec,t] = [T.summation over (t=1)] max(0, ([P.sub.t] - [P.sub.t-1]) - ([P.sub.max,t] - [P.sub.max,t-1])) (2)

[P.sub.cut,t] = [T.summation over (t=1)] min(0, ([P.sub.t] - [P. …

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