Estimating a cointegrating demand system

Attfield, C.L.F.

doi:10.1016/0014-2921(95)00062-3

Cited by 67 publications

(36 citation statements)

References 12 publications

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“…Figure 4 shows the graphs for aggregate budget shares w it , with seasonal adjustments obtained by the X12 census procedure, highlighting the fact that at least some of these shares may also appear non-stationary. This study confirms the systematic evidence of non-stationary in the variables of the demand system (Ng, 1995;Lewbel, 1999;Attfield, 1997Attfield, , 2004. But, as discussed in Lewbel and Ng (2005), because budget shares must, by construction, lie between 0 and 1, they cannot remain nonstationary forever.…”

Section: Data and Elasticitiessupporting

confidence: 82%

“…One way of solving these issues is to use linear model cointegration methods (Attfield, 1997(Attfield, , 2004) although they may not be completely consistent, since errors in demand systems tend to be autocorrelated (Lewbel and Ng, 2005). Standard asymptotic theory may provide a poor guide to finite-sample inference when the errors are persistent in a cointegrated demand system.…”

Section: Econometric Framework Data Issues and Elasticitiesmentioning

confidence: 99%

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Food prices and overweight patterns in Italy

2011

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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 examine the role of relative food prices in determining the recent increase in body weight in Italy. Cross-price elasticities of unhealthy and healthy foods estimated by a demand system provide a consistent framework to evaluate substitution effects, when a close association is assumed between unhealthy (healthy) foods and more (less) energy-dense foods. We used a dataset constructed from a series of cross-sections of the Italian Household Budget Survey (1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) to obtain the variables of the demand system, which accounts for regional price variability. The relative increase of healthy food prices was found to produce nontrivial elasticities of substitution towards higher relative consumption of unhealthy foods, with effects on weight outcomes. In addition, these changes were unevenly distributed among individuals and were particularly significant for those who were poorer and had less education.

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Section: Data and Elasticitiessupporting

confidence: 82%

Section: Econometric Framework Data Issues and Elasticitiesmentioning

confidence: 99%

Food prices and overweight patterns in Italy

2011

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“…Ng (1995) and Attfield (1997) specified a system of variables in triangular form, estimating the system using Dynamic Ordinary Least Squares (Stock and Watson, 1993) and testing homogeneity with a Wald statistic. Balcombe and Davis (1996) used the canonical cointegrating regression (CCR) (Park, 1992) to estimate an AID system for food consumption in Bulgaria.…”

Section: The Aid Model and Cointegration Implicationsmentioning

confidence: 99%

“…As a result, we consider them to be non-stationary. A similar approach is used in Ng (1995), Attfield (1997) and Pesaran and Shin (1999 Juselius (1999) proposes an alternative approach in which she tests the significance of the adjustment coefficients for the r-th cointegrating vector (α ir ). If all α ir coefficients are non-significant, then the cointegration rank should be reduced to (r-1).…”

Section: Cointegrating Rankmentioning

confidence: 99%

“…On the other hand, if the variables are cointegrated, the specification of a demand system in first differences is biased due to the misspecification of the long-run relationships. Thus, the estimation of cointegrated demand systems has become increasingly popular (see, for instance Denbaly and Vroomen, 1993;Balcombe and Davis, 1996;Attfield, 1997;and Chambers and Nowman, 1997).…”

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confidence: 99%

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Estimation and inference in cointegrated demand systems: an application to Tunisian meat consumption

Kaabia

Gil²

2001

European Review of Agriculture Economics

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-In this paper, a new framework for estimating and identifying cointegrated demand systems is presented. This method is then applied to the analysis of meat demand in Tunisia with an Almost Ideal Demand system functioning as the underlying model. TheJohansen and Juselius approach is used to identify cointegrated vectors as demand equations and to test theoretical economic restrictions. Finally, a structural VAR model is specified and impulse response functions are calculated to analyse both long and short-run dynamics of demand elasticities.

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