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The Consumer Panel of Australia data collected by The Roy Morgan Research Centre is used to investigate the determinants of brand choice behavior in the laundry detergent market. To reduce potential problems of heterogeneity in the market all purchases of the eleven leading brands in the Melbourne metropolitan area over the period July 1992 to June 1993 are considered. The purchases over this period are divided into an estimation sample and a holdout sample. A mixed Logit model is then fitted using the estimation sample. Price, brand loyalty, household size and household income are found to be significant in explaining brand choice behavior. A test for the, potentially restrictive, property of Independence of Irrelevant Alternatives (IIA) is carried out and it is found that IIA is acceptable for this data. The model appears to fit well and estimated elasticities are presented. The estimated model is then validated using the holdout sample. It is found that the model is very good at predicting the brand choices made by consumers in the holdout sample.

This paper conducts a Monte Carlo analysis of the size properties of combining choice set partition tests of the independence of irrelevant alternatives property in the Logit model in the four alternatives setting. Most of the tests have poor size properties. The exception is a version of the test proposed by Small and Hsiao (1985).

Estimation of attraction models in marketing typically involves the use of the log-centering transformation. The resultant estimating equations are then linear in the parameters. The log-centering transformation also appears in the statistical analysis of compositional data (CODA). CODA techniques are applied to data on "shares" in a wide variety of disciplines. This paper uses CODA techniques to rationalize the stochastic specification of attraction models. It further shows that another transformation from CODA, the log-ratio transform, can yield simpler estimating equations. The results are illustrated using an empirical example.

We compare three methods of estimating the duration, or half-life, of advertising using computer simulation experiments. In particular, we investigate how well each method works with the data aggregated over different time intervals. In contrast with the existing theory on the, so called, data interval bias, our experiments are based upon realistic advertising schedules. Our results appear to indicate that the indirect "t-ratio" estimation procedure favoured by practitioners works well in the presence of such temporal aggregation. Additionally, we suggest a transformation that can be used in combination with the indirect "t-ratio" estimation procedure to obtain estimates of the underlying micro-period half-life from a variety of common (macro) data frequencies.

In analysing the effects of economic policy in a Federal system, such as Australia, it is important to understand the interactions between the States and Territories. In particular, given that there is free movement between labour markets, to analyse economic policy it is important to understand the factors influencing inter-regional migration. In this paper we use data from 1982 to 1996 to estimate a structural econometric model of inter-regional migration. The results are then used to re-specify and calibrate the Computable General Equilibrium model MONASH-MRF. This then provides a more detailed picture of labour market responses when we subsequently simulate the response of net inter-state migration to changes in State Government spending.

This paper conducts a Monte Carlo analysis of the size and power properties of combining choice set partition tests of the IIA property of the Logit model. On the basis of this comparison we recommend that applied researchers testing for IIA calculate all versions of the McFadden, Train and Tye (1981) test and reject IIA if any single test rejects IIA.

First application of compositional data analysis techniques to Australian election data Geologische Vereinigung; Institut d’Estadística de Catalunya; International Association for Mathematical Geology; Patronat de l’Escola Politècnica Superior de la Universitat de Girona; Fundació privada: Girona, Universitat i Futur; Càtedra Lluís Santaló d’Aplicacions de la Matemàtica; Consell Social de la Universitat de Girona; Ministerio de Ciencia i Tecnología.