The Kalman filter approach for testing structural change in the demand for alcoholic beverages in the US

Tegene, Abebayehu

doi:10.1080/00036849000000111

Cited by 28 publications

(12 citation statements)

References 17 publications

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“…To make use of this dynamic model, it is necessary to make initial estimates for the state vector (b 0 = b 0 ), its covariance matrix (P 0 ), the covariance matrices of the random errors u t and v t (H t and Q, respectively), and the matrix of the transition equation, C. Some authors, like Chow (1983), Slade (1989) and Tegene (1990) introduce certain simplifying hypotheses. For instance, they consider C as the identity matrix, and Q and H t as being diagonal or even scalar matrices.…”

Section: Dynamicsmentioning

confidence: 99%

An Assessment of the Agricultural Trade Impact of Spain's Integration into the EU

Sanz¹,

Gil

2001

Canadian J Agri Economics

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The aim of this paper is to provide an econometric framework from which to assess the impact on Spanish agricultural trade by the entry of Spain into the European Union. This framework is based on the estimation of a gravity equation. Special attention is paid to the inclusion of dynamics in the gravity equation. To do that, the Kalman filter is used to define the antimonde, that is, the hypothetical situation that would exist without the integration of Spain into the EU. Results show that although Spain joined the EU in 1986, there existed a transition period that lasted until 1990. This result is consistent with the homogenization of agricultural intervention prices during the same period. Empirical results indicate that internal trade creation and external trade diversion are the predominant effects of integration on both imports and exports. L'objectif de cet article est d'illustrer une approche économétrique pour évaluer l'impact de l'adhésion de l'Espagne à la Union Européenne sur le commerce agricole. La méthodologie utilisée est basée sur l'estimation des équations de gravité. Une attention spéciale est portée à la manière d'introduire des effets dynamiques dans ces équations. Sur un autre plan, et au-delà de l'intérêt des relations dynamiques, le filtre de Kalman est utilisé pour définir l'antimonde, ou la situation hypothétique qu'il doit exister sans que le système d'intégration soit mis en oeuvre. Les résultats obtenus permettent de confirmer que malgré l'intégration avait lieu en 1986, la période de transition a prolongé jusqu'à 1990. Ce résultat est consistent avec l'homogénéisation des prix d'intervention agricoles dans le même période. Les résultats empiriques indiquent que la création du commerce interne et la diversion du commerce externe constituent les effets plus prédominants sur l'intégration aussi bien sur les exportations comme sur les importations.

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Section: Dynamicsmentioning

confidence: 99%

An Assessment of the Agricultural Trade Impact of Spain's Integration into the EU

Sanz¹,

Gil

2001

Canadian J Agri Economics

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“…It was concluded that advertising had very small effects on budget shares and no effect on the amount spent on all alcoholic beverages. Tegene (1990) used Kalman filters to address the issue of structural change in the computations of elasticities. Indeed, he found structural changes in the demands for beer, wine and spirits but failed to identify a significant relationship between advertising and demand.…”

Section: The Consumption Of Alcoholic Beverages and Advertising: Somementioning

confidence: 99%

Modeling the demand for alcoholic beverages and advertising specifications

Larivière¹

2000

Agricultural Economics

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In this paper, the demand for beer, wine, spirits and soft drinks in Ontario is modeled in two parts: an equation is specified to endogenize group expenditures and a demand system is set up to allocate budgeted group expenditures across ,types of beverages. Advertising is allowed to influence both the level of group expenditures and its allocation. Three popular advertising specifications are compared using the ]-test and the likelihood dominance criterion. Even though all three specifications fitted well according to standard criteria, the calculated expenditure, price and advertising elasticities were sensitive to the manner with which advertising is specified. This clearly highlights the need to rely on a sound criterion to identify a dominant specification. From the identified dominant specification, we found that advertising has very subtle effects on expenditures on alcoholic beverages (group and individual beverages). Thus, advertising is not effective in enlarging markets and this suggests that firms (especially breweries) use advertising to compete in zero-sum market share games. From a public policy perspective, our results are comforting but future research should investigate whether the neutral effect of advertising on aggregated expenditures hide substantial offsetting changes in the drinking habits of individuals.

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“…During the past two decades, the standard Kalman filter also has been adopted to estimate social systems (e.g., Athans 1974; Duncan, Gorr, and Szczypula 1993; Morrison and Pike 1977;Slade 1989;Tegene 1990Tegene , 1991. During the past two decades, the standard Kalman filter also has been adopted to estimate social systems (e.g., Athans 1974; Duncan, Gorr, and Szczypula 1993; Morrison and Pike 1977;Slade 1989;Tegene 1990Tegene , 1991.…”

Section: The Standard Kalman Filter Techniquementioning

confidence: 99%

“…The discrete standard Kalman filter can be used to estimate the unknown parameters if an autoregression equation can be used to describe the diffusion process: xk = -i I= i Xk_ -i + ?k, where xk is the number of new adopters in the kth period (Kahl and Ledolter 1983;Meade 1985;Morrison and Pike 1977;Tegene 1990Tegene , 1991. However, because it requires that the sales at a given time, Xk, be expressed as a recursive function of the parameters and previous observations, this formulation of a discrete Kalman filter cannot be applied to diffusion models expressed as a differential equation unless (1) the differential equation is approximated by a difference equation describing xk, possibly introducing time interval bias, or (2) the differential equation for x(t) has an analytical solution and x(t) can be explicitly written as a function of lagged values of x.…”

Section: The Standard Kalman Filter Techniquementioning

confidence: 99%

Kalman Filter Estimation of New Product Diffusion Models

Xie¹,

Song²,

Sirbu³

et al. 1997

Journal of Marketing Research

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Marketing Association is collaborating with JSTOR to digitize, preserve and extend access toThe authors introduce a new estimation procedure, Augmented Kalman Filter with Continuous State and Discrete Observations (AKF(C-D)), for estimating diffusion models. This method is directly applicable to differential diffusion models without imposing constraints on the model structure or the nature of the unknown parameters. It provides a systematic way to incorporate prior knowledge about the likely values of unknown parameters and updates the estimates when new data become available. The authors compare AKF(C-D) empirically with five other estimation procedures, demonstrating AKF(C-D)'s superior prediction performance. As an extension to the basic AKF(C-D) approach, they also develop a parallel-filters procedure for estimating diffusion models when there is uncertainty about diffusion model structure or prior distributions of the unknown parameters. KalmanFilter Estimation o f New Product Diffusion Models The desire to forecast the diffusion of new products has inspired a large body of research during the past two decades. The accurate prediction of new product diffusions is critical in designing marketing strategies for new product planning and management. Before predicting sales, diffusion model specifications must be determined and parameters must be estimated. A variety of estimation methods for estimating diffusion models have been proposed. (For a review of the literature on these estimation techniques, see Mahajan, Muller, and Bass 1990.) In their article, Mahajan, Muller, and Bass (1990) classify diffusion model estimation procedures into two groups: time-invariant estimation procedures and timevarying estimation procedures. Time-invariant estimation procedures include the conventional estimation methods such as ordinary least square (OLS) (Bass 1969), maximum likelihood estimation (MLE) (Schmittlein and Mahajan 1982), and nonlinear least squares (NLS) (Srinivasan and Mason 1986). for their helpful suggestions. They also are thankful to JMR editor Vijay Mahajan and three anonymous reviewers for their insightful and constructive comments.(2) x(t)= [p + q n(t -1) i[m n(t -1)] = oi + a2n(t -1) + oa3n2(t -I), t = 1, 2,.... where x(t) is the number of new adopters in the tth interval, and 378 This content downloaded from 150.135.239.97 on Thu,

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The Kalman filter approach for testing structural change in the demand for alcoholic beverages in the US

Cited by 28 publications

References 17 publications

An Assessment of the Agricultural Trade Impact of Spain's Integration into the EU

An Assessment of the Agricultural Trade Impact of Spain's Integration into the EU

Modeling the demand for alcoholic beverages and advertising specifications

Kalman Filter Estimation of New Product Diffusion Models

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