Estimating the Indirect Effect of Sports Books on Other In-House Gaming Volumes

Using data from a repeater market hotel in Las Vegas, Nevada, the relationship between sports book and slot machine revenues is examined. Daily sports book write and daily slot handle are compared over a 250 day period. Though many industry leaders theorize that sports book gamblers also wager in slot banks, the results of this Autoregressive Integrated Moving Average (ARIMA) analysis fail to demonstrate a statistically significant relationship between sports book write and slot coin-in at the 0.05 alpha cutoff. This study advances literature currently available by establishing the lack of such a relationship and disputing the generally accepted assumption that sports books produce a substantial indirect contribution to slot revenues. While the sports book does generate a fairly constant direct profit for the casino, the absolute value of that profit is minimal and the results of the study show there is no indirect profit contribution from sports books to slot machines. Given these results, casino management may want to consider that a sports book is not an optimal use of casino floor space

Time-Series Models in Marketing

Marketing data appear in a variety of forms. An often-seen form is time-series data, like sales per month, prices over the last few years, market shares per week. Time-series data can be summarized in time-series models. In this chapter we review a few of these, focusing in particular on domains that have received considerable attention in the marketing literature. These are (1) the use of persistence modelling and (2) the use of state space models.Marketing;Persistence;State Space;Time Series

Countercyclical Price Movements during Periods of Peak Demand: Evidence from Grocery Retail Price for Avocados

Using a unique micro dataset and advanced panel models, this study examines the effects of demand shocks on grocery retail price for avocados, a key Californian fresh produce commodity. Retail prices for avocados exhibited countercyclical movements over seasonal demand shocks for avocados associated with some holidays and events. Demand for avocados is shown to be higher during some holidays/events, e.g., Christmas/New Year, Super Bowl Sunday, and Cinco de Mayo. Super Bowl Sunday and Cinco de Mayo are identified as holidays/events associated with idiosyncratic demand peaks for avocados, but not associated with high aggregate consumer demand. Retail price and margin were significantly lower during some holidays/events associated with high demand for avocados, e.g., Christmas/New Year, Super Bowl Sunday, and Cinco de Mayo. The study also shows that the increase in demand and decrease in retail price during holidays/events with demand peaks for avocados was present for both large and small sizes of avocados, and the size of demand increases and the size of price reductions were not statistically different between large and small size of avocados. Furthermore, shipping price did not change or increased slightly, and hence moved opposite from retail the price during most holidays/events with high demand for avocados. We examine and test the predictions by four classes of theories that put forward to explaining countercyclical price movements over demand peaks. Overall, the evidence provides support for the Lal and Matutes (1994) model that retailers reduce retail prices and/or margins during a commodity's high-demand periods, but does not support alternative explanations for countercyclical price movements, such as Bernheim and Whinston (1990), Warner and Barskey (1995), or Nevo and Hatzitaskos (2006). The findings are consistent with the findings by Chevalier, Kashyap, and Rossi (2003). The study estimates the effects of the CAC's promotion programs on retail sales, retail price, and shipping price at disaggregate level. The analysis demonstrates that the CAC's promotion programs were associated with positive retail sales. In particular, the evidence from the long-panel data suggests that the CAC's promotion programs were successful in raising avocado sales. There is no evidence that retailers charged higher prices during the CAC's promotions.retail price, retail price determination, countercyclical price movement, dynamic panel model, GMM, Demand and Price Analysis,

Combining time series and cross sectional data for the analysis of dynamic marketing systems

Vector AutoRegressive (VAR) models have become popular in analyzing the behavior of competitive marketing systems. However, an important drawback of VAR models is that the number of parameters to be estimated can become very large. This may cause estimation problems, due to a lack of degrees of freedom. In this paper, we consider a solution to these problems. Instead of using a single time series, we develop pooled models that combine time series data for multiple units (e.g. stores). These approaches increase the number of available observations to a great extent and thereby the efciency of the parameter estimates. We present a small simulation study that demonstrates this gain in efficiency. An important issue in estimating pooled dynamic models is the heterogeneity among cross sections, since the mean parameter estimates that are obtained by pooling heterogenous cross sections may be biased. In order to avoid these biases, the model should accommodate a sufficient degree of heterogeneity. At the same time, a model that needlessly allows for heterogeneity requires the estimation of extra parameters and hence, reduces efciency of the parameter estimates. So, a thorough investigation of heterogeneity should precede the choice of the nal model. We discuss pooling approaches that accommodate for parameter heterogeneity in different ways and we introduce several tests for investigating cross-sectional heterogeneity that may facilitate this choice. We provide an empirical application using data of the Chicago market of the three largest national brands in the U.S. in the 6.5 oz. tuna sh product category. We determine the appropriate level of pooling and calibrate the pooled VAR model using these data.

Integrating Routine, Variety Seeking and Compensatory Choice in a Utility Maximizing Framework

Given the large number of choices that consumers make each day it seems likely that they will generally adopt decision strategies that minimize cognitive effort, particularly with low price products such as most items found in a supermarket. One such strategy may be to simply choose what has been chosen in the past, i.e. to fall into a pattern of routine choices or decisions. In contrast, there may be preferences for variety in markets for low price, highly differentiated goods. We develop a conceptual and empirical model of routine choice, and the factors that result in transitions to two strategies other than routine selection, to wit, utility maximizing choice among available alternatives and a variety seeking strategy. The empirical approach we employ provides a mechanism for the examination of panel data that avoids the state dependence issues present in most applications to these types of data. We apply this framework to the choice of two food products that illustrate the heterogeneity across types of products in decision strategies and routine choice patterns.Choice modeling, routine behavior, variety‐seeking, panel data, Consumer/Household Economics, Demand and Price Analysis, Institutional and Behavioral Economics, D12, D03, C25,

Annotated Bibliography of Generic Commodity Promotion Research (Revised)

A.E. Res. 91-

Measuring the Implications of Sales and Consumer Inventory Behavior

Temporary price reductions (sales) are common for many goods and naturally result in large increases in the quantity sold. Demand estimation based on temporary price reductions may mis-measure the long run responsiveness to prices. In this paper we quantify the extent of the problem and assess its economic implications. We structurally estimate a dynamic model of consumer choice using two years of scanner data on the purchasing behavior of a panel of households. The results suggest that static demand estimates, which neglect dynamics: (i) overestimate own price elasticities by 30 percent; (ii) underestimate cross-price elasticities to other products by up to a factor of 5; and (iii) overestimate the substitution to the no purchase, or outside option, by over 200 percent.

Time-Series Models in Marketing

Marketing data appear in a variety of forms. An often-seen form is time-series data, like sales per month, prices over the last few years, market shares per week. Time-series data can be summarized in time-series models. In this chapter we review a few of these, focusing in particular on domains that have received considerable attention in the marketing literature. These are (1) the use of persistence modelling and (2) the use of state space models