Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption

Baltagi, Badi H.; Bresson, Georges; Griffin, James M.; Pirotte, Alain

doi:10.1007/s00181-003-0161-9

Cited by 68 publications

(56 citation statements)

References 15 publications

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“…On the one hand, the finding that pooling helps to increase the forecasting accuracy is consistent with the results obtained in Baltagi and Griffin (1997);Baltagi et al (2003), Baltagi et al (2000), Baltagi et al (2002), Baltagi et al (2004, and Brücker and Siliverstovs (2006), inter alia, for diverse data sets. On the other hand, the fact that accounting for spatial effects helps to improve the forecast performance further strengthens conclusions of…”

Section: Resultssupporting

confidence: 86%

“…The problem of data collection for each region is circumvented by pooling the annual growth rates of GRP into a panel and correspondingly utilizing panel data models for forecasting. The advantages of such a pooling approach for forecasting have been widely demonstrated in a series of articles for diverse data sets such as Baltagi and Griffin (1997); Baltagi et al (2003) -for gasoline demand, Baltagi et al (2000) -for cigarette demand, Baltagi et al (2002) -for electricity and natural gas consumption, Baltagi et al (2004) -for Tobin's q estimation, and Brücker and Siliverstovs (2006) -for international migration, among others.…”

Section: Introductionmentioning

confidence: 99%

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Does Accounting for Spatial Effects Help Forecasting the Growth of Chinese Provinces?

Girardin

Kholodilin

2009

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractIn this paper, we make multi-step forecasts of the annual growth rates of the real GRP for each of the 31Chinese provinces simultaneously. Beside the usual panel data models, we use panel models that explicitly account for spatial dependence between the GRP growth rates. In addition, the possibility of spatial effects being different for different groups of provinces (Interior and Coast) is allowed. We find that both pooling and accounting for spatial effects helps substantially improve the forecast performance compared to the benchmark models estimated for each of the provinces separately. It was also shown that effect of accounting for spatial dependence is even more pronounced at longer forecasting horizons (the forecast accuracy gain as measured by the root mean squared forecast error is about 8% at 1-year horizon and exceeds 25% at 13-and 14-year horizon).

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Section: Resultssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Does Accounting for Spatial Effects Help Forecasting the Growth of Chinese Provinces?

Girardin

Kholodilin

2009

SSRN Journal

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“…The estimator "shrinks" country-specific parameters of the individual countries toward a common probability distribution, but where the individual country estimates remain heterogeneous after shrinkage. The iterative shrinkage estimator has become popular in heterogeneous estimation on panel data models since it appear to provide more plausible elasticity estimates (Baltagi et al, 2003;Baltagi and Griffin, 1997;Maddala et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Natural Gas Demand in the European Household Sector

Asche¹,

Nilsen²,

Tveterås³

2008

The Energy Journal

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This paper analyzes the residential natural gas demand in 12 European countries using a dynamic demand model, which allows for country-specific short- and long-run elasticity estimates. The own-price and income elasticities tend to be very inelastic in the short run, albeit with much greater responsiveness in the longer run. Our results support very limited technological substitution possibilities between different energy carriers in the short run. Furthermore, the results suggested structural differences of residential natural gas demand across European countries and provide support for employing a heterogeneous estimator such as the shrinkage estimator.

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“…We estimate Equation 8 in levels using instruments for the lagged dependent variable. As demonstrated by Baltagi and Griffin (1983) and Baltagi, Bresson, Griffin, and Pirotte (2003), this model performs better than the model in first differences in terms of out-of-sample forecast accuracy. First, we present the results for the static model.…”

Section: Empirical Implementationmentioning

confidence: 59%

Unit Values, Productivity, and Trade - Determinants of Spanish Export Strength

2009

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In this paper we assess the current relevance of different sources of international competitiveness. Relative prices, labor costs, and productivity are evaluated as determinants of a country's international competitiveness at the industry level. Working with detailed data on unit values and with industry data on productivity, we empirically implement a MacDougall-type model for Spanish and French trade to Brazil, China, Japan, and the U.S. The period under study is 1980 to 2001 and we distinguish in our analysis between homogenous, reference-priced, and differentiated goods. Our results indicate that cost competitiveness factors are only valid for explaining trade with developing countries while other factors are of importance for developed economies. Overall price competitiveness is of importance, but for differentiated goods, factors distinct from prices seem to determine export success.

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Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption

Cited by 68 publications

References 15 publications

Does Accounting for Spatial Effects Help Forecasting the Growth of Chinese Provinces?

Does Accounting for Spatial Effects Help Forecasting the Growth of Chinese Provinces?

Natural Gas Demand in the European Household Sector

Unit Values, Productivity, and Trade - Determinants of Spanish Export Strength

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