In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Social Network Analysis

The authors update and extend their 1978 review of conjoint analysis. In addition to discussing several new developments, they consider alternative approaches for measuring preference structures in...

https://www.jstor.org/stable/info/1251756

Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice:

The authors study the effect of word-of-mouth (WOM) marketing on member growth at an Internet social networking site and compare it with traditional marketing vehicles. Because social network sites record the electronic invitations from existing members, outbound WOM can be precisely tracked. Along with traditional marketing, WOM can then be linked to the number of new members subsequently joining the site (sign-ups). Because of the endogeneity among WOM, new sign-ups, and traditional marketing activity, the authors employ a vector autoregressive (VAR) modeling approach. Estimates from the VAR model show that WOM referrals have substantially longer carryover effects than traditional marketing actions and produce substantially higher response elasticities. Based on revenue from advertising impressions served to a new member, the monetary value of a WOM referral can be calculated; this yields an upper-bound estimate for the financial incentives the firm might offer to stimulate WOM.

https://eresearch.ozyegin.edu.tr/xmlui/bitstream/handle/10679/56/Effects_of_Word-of-Mouth_Versus_Traditional_Marketing_Findings_from_an_Internet_Social_Networking_Site.pdf;sequence=5

Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site

Customer satisfaction, customer retention, and market share

The success of Internet social networking sites depends on the number and activity levels of their user members. While users typically have numerous connections to other site members (i.e., “friends”), only a fraction of those “friends” may actually influence a member’s site usage. Since the influence of potentially hundreds of friends needs to be evaluated for each user, inferring precisely who is influential - and therefore of managerial interest for advertising targeting and retention efforts - is difficult. We develop an approach to determine which users have significant effects on the activities of others using the longitudinal records of members’ login activity. We propose a non-standard form of Bayesian shrinkage implemented in a Poisson regression. Instead of shrinking across panelists, strength is pooled across variables within the model for each user. The approach identifies the specific users who most influence others’ activity and does so considerably better than simpler alternatives. For the social networking site data, we find that, on average, about one-fifth of a user's friends actually influence his/her activity level on the site.

Determining Influential Users in Internet Social Networks

The success of Internet social networking sites depends on the number and activity levels of their user members. Although users typically have numerous connections to other site members (i.e., “friends”), only a fraction of those so-called friends may actually influence a member's site usage. Because the influence of potentially hundreds of friends needs to be evaluated for each user, inferring precisely who is influential—and, therefore, of managerial interest for advertising targeting and retention efforts—is difficult. The authors develop an approach to determine which users have significant effects on the activities of others using the longitudinal records of members' log-in activity. They propose a nonstandard form of Bayesian shrinkage implemented in a Poisson regression. Instead of shrinking across panelists, strength is pooled across variables within the model for each user. The approach identifies the specific users who most influence others' activity and does so considerably better than...

When consumers are exposed to pricing and promotional activity by frequently purchased packaged goods, they may develop expectations that are used as points of reference in evaluating future activi...

Reference Effects of Price and Promotion on Brand Choice Behavior

Using the clickstream data recorded in Web server log files, the authors develop and estimate a model of the browsing behavior of visitors to a Web site. Two basic aspects of browsing behavior are examined: (1) the visitor’s decisions to continue browsing (by submitting an additional page request) or to exit the site and (2) the length of time spent viewing each page. The authors propose a type II tobit model that captures both aspects of browsing behavior and handles the limitations of server log-file data. The authors fit the model to the individual-level browsing decisions of a random sample of 5000 visitors to the Web site of an Internet automotive company. Empirical results show that visitors’ propensity to continue browsing changes dynamically as a function of the depth of a given site visit and the number of repeat visits to the site. The dynamics are consistent both with “within-site lock-in” or site “stickiness” and with learning that carries over repeat visits. In particular, repeat vis...

Randolph E. Bucklin

Papers

Effects of Word-of-Mouth Versus Traditional Marketing: Findings from an Internet Social Networking Site

Determining Influential Users in Internet Social Networks

Determining Influential Users in Internet Social Networks

Reference Effects of Price and Promotion on Brand Choice Behavior

A Model of Web Site Browsing Behavior Estimated on Clickstream Data