Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

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

Understanding customer experience and the customer journey over time is critical for firms. Customers now interact with firms through myriad touch points in multiple channels and media, and customer experiences are more social in nature. These changes require firms to integrate multiple business functions, and even external partners, in creating and delivering positive customer experiences. In this article, the authors aim to develop a stronger understanding of customer experience and the customer journey in this era of increasingly complex customer behavior. To achieve this goal, they examine existing definitions and conceptualizations of customer experience as a construct and provide a historical perspective of the roots of customer experience within marketing. Next, they attempt to bring together what is currently known about customer experience, customer journeys, and customer experience management. Finally, they identify critical areas for future research on this important topic.

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Understanding Customer Experience Throughout the Customer Journey

The Theory of Monopolistic Competition.

https://www.robjhyndman.com/papers/ijf25.pdf

25 years of time series forecasting

We introduce a multi-level smooth transition model for a panel of time series, which can be used to examine the presence of common nonlinear business cycle features across many variables. The model is positioned in between a fully pooled model, which imposes such common features, and a fully heterogeneous model, which allows for unrestricted nonlinearity. We introduce a second-stage model linking the parameters that determine the timing of the switches between business cycle regimes to observable explanatory variables, thereby allowing for lead–lag relationships across panel members. We discuss representation, estimation by concentrated simulated maximum likelihood and inference. We illustrate our model using quarterly industrial production in 19 US manufacturing sectors, and document that there are subtle differences across sectors in leads and lags for switches between business cycle recessions and expansions. Copyright © 2005 John Wiley & Sons, Ltd.

A multi‐level panel STAR model for US manufacturing sectors

In a collaborative study with a major Dutch retailer, the authors assess the short- and long-term effects of a 25% item reduction on category sales. On an aggregate level, a major assortment reduction can lead to substantive short-term category sales losses but only a weak negative long-term category sales effect. Short-term category sales losses are caused mainly by fewer category purchases by former buyers of delisted detergent items. However, the results also show that the assortment reduction attracts new category buyers. These new buyers partially offset the sales losses among former buyers of the delisted items. The collection of supplemental process data on assortment perceptions and actual search time in the test stores before and after the assortment reduction provides evidence that delisting results in an increase in perceived search efficiency and a decrease in actual search time.

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The short- and long-term impact of an assortment reduction on category sales

textDe recente toename in de beschikbaarheid van grote marketingdatabases heeft geleid tot een groei in de vraag naar geschikte econometrische modellen. Over het algemeen bevatten deze databases gegevens over de voorkeuren van consumenten. Deze voorkeuren kunnen worden weergegeven door marktaandelen, totale verkopen of merkkeuzebeslissingen. Econometrische modellen vormen handige hulpmiddelen om nuttige informatie over consumentengedrag uit deze databases te halen. In dit proefschrift bespreken wij econometrische modellen voor marktaandelen, tussenaankooptijden en merkkeuzebeslissingen. Deze modellen kunnen bijvoorbeeld worden gebruikt om inzicht te verkrijgen in het effect van marketinginstrumenten op het gedrag van consumenten. Voorbeelden van de onderwerpen die worden besproken zijn heterogeniteit in beslissingsprocessen en de ontwikkeling van eenvoudig te interpreteren modellen voor dynamische eigenschappen van marktaandelen en tussenaankooptijden. Daarnaast leveren wij een bijdrage aan de econometrische literatuur door de uitbreiding en ontwikkeling van modellen en schattingstechnieken.

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Advanced Econometric Marketing Models

In this paper we consider the situation where one wants to study the preferences of individuals over a discrete choice set through a survey. In the classical setup respondents are asked to select their most preferred option out of a (selected) set of alternatives. It is well known that, in theory, more information can be obtained if respondents are asked to rank the set of alternatives instead. In statistical terms, the preferences can then be estimated more e‐ciently. However, when individuals are unable to perform (part of) this ranking task, using the complete ranking may lead to a substantial bias in parameter estimates. In practice, one usually opts to only use a part of the reported ranking. In this paper we introduce a latent-class rank-ordered logit model in which we use latent segments to endogenously identify the ranking capabilities of individuals. Each segment corresponds to a difierent assumption on the ranking capability. Using simulations and an empirical application, we show that using this model for parameter estimation results in a clear e‐ciency gain over a multinomial logit model in case some individuals are able to rank. At the same time it does not sufier from biases due to ranking inabilities of some of the respondents.

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Dennis Fok

Papers

A multi‐level panel STAR model for US manufacturing sectors

The short- and long-term impact of an assortment reduction on category sales

Advanced Econometric Marketing Models

A rank-ordered logit model with unobserved heterogeneity in ranking capabilities

Reward Redemption Effects in a Loyalty Program when Customers Choose how much and when to Redeem