IDEAS home Printed from https://ideas.repec.org/p/ags/aaea12/124363.html
   My bibliography  Save this paper

Modeling Advertising Expenditures and Spillover Effects Applied to the U.S. Non-Alcoholic Beverage Industry: Vector Autoregression (VAR) and Polynomial Distributed Lag (PDL) Approaches

Author

Listed:
  • Dharmasena, Senarath
  • Capps, Oral, Jr.
  • Bessler, David A.

Abstract

The non-alcoholic beverage market in the U.S. is a multi-billion dollar industry growing steadily over the past decade. Also, non-alcoholic beverages are among the most heavily advertised food and beverage groups in the United States. Several studies pertaining to non-alcoholic beverages including the incorporation of advertising effects have been conducted, but most of these have centered attention on milk consumption. Some studies have considered demand interrelationships for several beverages including advertising effects in systems-wide analyses. In our analysis, we develop and employ a unique monthly time-series data set derived from Nielsen Homsescan panels for household purchases of non-alcoholic beverages over the period from January 1998 through December 2009. This data set is subjected to the use of vector autoregression (VAR) and polynomial distributed lags (PDL) to examine own-advertising and cross-advertising effects for non-alcoholic beverages. Contemporaneous causal structures among advertising expenditures, quantities and prices of various non-alcoholic beverages also are studied using artificial intelligence approaches such as directed acyclic graphs (DAGs). Once the VAR and PDL (through augmenting the AIDS model) are estimated, we can ascertain own- and cross-advertising effects of various beverages. Impulse response functions gleaned from the use of the VAR approach would show us the impacts of advertising expenditures of various beverages to a one-time-only shock. The direction and the strength of the impulse would speak to the effect of the adverting expenditure of one beverage on others. Error variance decompositions help us determine the magnitude of the contribution of advertising expenditures of several beverages on a given beverage. Similarly, own- and cross-adverting expenditure elasticities generated through the PDL help us determine the negative and positive spillover effects of advertising expenditure of one beverage on several others. The contemporaneous causal structure pertaining to advertising expenditures, quantities and prices of various beverages help identify endogenous, exogenous and/or weakly exogenous factors. Ultimately, the better model to ascertain the effect of advertising expenditures of various non-alcoholic beverages is identified through the study of out-of-sample forecasts generated though both models.

Suggested Citation

  • Dharmasena, Senarath & Capps, Oral, Jr. & Bessler, David A., 2012. "Modeling Advertising Expenditures and Spillover Effects Applied to the U.S. Non-Alcoholic Beverage Industry: Vector Autoregression (VAR) and Polynomial Distributed Lag (PDL) Approaches," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124363, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea12:124363
    as

    Download full text from publisher

    File URL: http://purl.umn.edu/124363
    Download Restriction: no

    References listed on IDEAS

    as
    1. Zheng, Yuqing & Kaiser, Harry M., 2008. "Advertising and U. S. Nonalcoholic Beverage Demand," Agricultural and Resource Economics Review, Cambridge University Press, vol. 37(02), pages 147-159, October.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    non-alcoholic beverages; vector autoregression; polynomial distributed lags; beverage advertizing; directed acyclic graphs; Agricultural and Food Policy; Consumer/Household Economics; Demand and Price Analysis; Food Consumption/Nutrition/Food Safety; Marketing; C18; C22; C52; C53; C81; D11; D12;

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea12:124363. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: http://edirc.repec.org/data/aaeaaea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.