Field Experiments

Valli, Veronica; Stahl, Florian; Feit, Elea McDonnell

doi:10.1007/978-3-319-57413-4_3

Cited by 2 publications

(1 citation statement)

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“…Third and relatedly, in contrast to typical applications with cross-sectional data, panel data allow to include time lags between dependent and independent variables. Thereby, panel data open up opportunities for novel research questions and, again, increase the researchers' ability to rule out endogeneity concerns Hattula 2021, andValli et al 2021 in this Handbook further discuss criteria for evaluating causality).…”

Section: Introduction: Relevance Of Panel Data For Marketing Researchmentioning

confidence: 99%

Panel Data Analysis: A Nontechnical Introduction for Marketing Researchers

Vomberg¹,

Wies

2021

Handbook of Market Research

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The analysis of panel data is now part of the standard repertoire of marketers and marketing researchers. Compared to the analysis of cross-sectional data, panel data allow marketers to alleviate endogeneity concerns when linking an independent variable (e.g., price) to an outcome variable (e.g., sales volume). The more accurate estimates that result from panel data analysis help improve marketers' decision-making in focal areas such as price setting and marketing budget allocation. Besides, panel data allow marketers to track customer behavior changes and distinguish real loyalty effects (i.e., same customer repeatedly buys a brand) from spurious effects (i.e., the same number of, but each time different set of, customers buys a brand). This chapter provides a nontechnical introduction to panel data analysis. Marketers will learn how to manage and analyze panel datasets in Stata. They will learn about the focal panel data estimators (pooled OLS, fixed effects, and random effects estimator), their underlying assumptions, advantages, and pitfalls. Besides, we introduce the between effects estimator, the combined approach, the Hausman-Taylor approach, and the first differences estimator as further techniques to analyze panel data. Finally, readers will receive an introduction to advanced topics such as dynamic panel models, panel data multilevel modeling, and using panel data to address measurement errors. Keywords Cluster-robust standard errors • Dynamic panel data models • Endogeneity • Fixed effects estimator • Hausman test • Hausman-Taylor method • Measurement error • Omitted variable bias • Panel data analysis • Pooled OLS • Random effects estimator • Serial correlation

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Section: Introduction: Relevance Of Panel Data For Marketing Researchmentioning

confidence: 99%