Response predictions in regressions on panel data

Kadiyala, R. K.; Oberhelman, Dennis

doi:10.1080/03610928208828416

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…This test supports the random coefficient model for our data. In this analysis, the realizations of the coefficients, q 1i and q 2i are estimated using a method suggested by Kadiyala and Oberhelman (1979). Details are given in the Appendix.…”

Section: Estimation Resultsmentioning

confidence: 99%

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Modelling the development of supply‐restricted telecommunications markets

Islam

Fiebig

2001

Journal of Forecasting

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A large proportion of the world telecommunications market can be characterized as supply restricted. In ITU (1999) official waiting lists numbered about 50 million worldwide with an average waiting time of two years. More than 100 countries had not eliminated the waiting list for telephone connections and hence a supply restricted market prevailed in all of these countries. Only about 25 countries have succeeded in eradicating their waiting list for basic telephone service. In terms of the pattern of diffusion, the subscriber's flow from waiting applicants to adopters is controlled by supply restrictions adding an important dimension that needs to be addressed when modeling and forecasting demand. An empirical analysis of the diffusion of main telephones in 46 supply-restricted countries is presented to demonstrate the usefulness of a three-stage Bass model that has been proposed to capture the dynamics of supply restrictions. We also compare the forecasting ability of different approaches to estimation when panel data are available.

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Section: Estimation Resultsmentioning

confidence: 99%

“…The objective is to construct a predictor for the coefficient vector for each panel unit that utilizes information not just from i alone, but from all cross-sectional units. The predictor vector of the coefficients as derived by Kadiyala and Oberhelman (1979) is as follows:…”

Section: Testing the Random Coefficientsmentioning

confidence: 99%

Modelling the development of supply‐restricted telecommunications markets

Islam

Fiebig

2001

Journal of Forecasting

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“…It means that when the dispersion is large (i.e., the cross sections are very distinctive from each other), we should give more weight to individual 2SLS, whereas when the MSE is large (i.e., the individual 2SLS estimate is not reflective), we should borrow more information from the pooled RCR. This weighted RCR estimator can help us capture both the common properties of all the cross sections as well as the stochastic character of the individual response (Kadiyala andOberhelman 1982, Leone et al 1993). The details of this estimation process are in the Technical Appendix, located at http://mktsci.pubs.informs.org.…”

Section: Model Developmentmentioning

confidence: 99%

Practice Prize Paper—Marketing-Mix Recommendations to Manage Value Growth at P&G Asia-Pacific

Kumar

Fan

Gulati³

et al. 2009

Marketing Science

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P rocter & Gamble (P&G) Asia-Pacific is interested in managing value growth. Only after fully understanding the true effects of the marketing-mix variables can P&G managers make strategic decisions answering questions such as the following: (1) Are the P&G brands in the detergent market inelastic or elastic with respect to price? How has the price elasticity changed over time? Can P&G increase the price of its brands to gain value growth? (2) What are the price, distribution, and sizing combinations needed to achieve the desirable value growth? (3) How can P&G gain market share from its competitors without cannibalizing its own brands? P&G Asia-Pacific approached us to develop a value growth framework to answer these questions. To generate the answers for the above questions, we develop a three-step weighted random coefficient estimator that captures the heterogeneity across cross sections (different stock-keeping units and states) and the endogeneity of distribution. Based on the parameter estimates, we provide strategic recommendations to P&G for a field test to validate our suggestions. We developed a simulator for P&G managers so that they can generate appropriate marketing-mix strategies for achieving the desired value growth. As a result, P&G gained over $39 million in value growth over a one-year period by implementing the recommendations from our modeling approach.

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Prediction in Random Coefficient Regression Models

Bondeson

1990

Biometrical J

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SummaryMuch attention has been given to the problem ofpredicting future obeervatiomfor some individual within a random coefficient regreasion (RCR) model, using the previous observations on that individual aa well es the information from the re& of the data material. In thia paper, the literature on this subject ie critically reviewed and new methode of linear prediction are proposed for the g e n d RCR model. Exact reaulte am derived for the mean squared errom of Bome predictors in a spacial case, but this ie not poeaible in the general RCR model when ite parametem are not known. In this model, the old and new predictom are compared in a simulation efudy, and further illustrated by prediction in a medical data material.

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Response predictions in regressions on panel data

Cited by 5 publications

References 11 publications

Modelling the development of supply‐restricted telecommunications markets

Modelling the development of supply‐restricted telecommunications markets

Practice Prize Paper—Marketing-Mix Recommendations to Manage Value Growth at P&G Asia-Pacific

Prediction in Random Coefficient Regression Models

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