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

The theory of affordances

The corporation of the 1990s: Information technology and organizational transformation

Firm disclosures often reach only a portion of investors, which results in information asymmetry among investors, and therefore lower market liquidity. This issue is particularly salient for firms that are not highly visible, as they tend not to receive broad news dissemination via traditional intermediaries, such as the press. This paper examines whether firms can reduce information asymmetry by more broadly disseminating their news. To isolate the impact of dissemination, we focus our analysis on firms' use of Twitter and exploit the 140-character message restriction. Specifically, using a sample of technology firms, we examine the impact of using Twitter to send market participants links to press releases that are provided via traditional disclosure methods. We find this additional dissemination of firm-initiated news via Twitter is associated with lower abnormal bid-ask spreads and greater abnormal depths, consistent with a reduction in information asymmetry. Moreover, this result holds mainly for firms that are not highly visible, consistent with them being in greater need of this additional dissemination channel. We also examine the impact of dissemination on a volume-based measure of liquidity, and find that dissemination is positively associated with liquidity.

The Role of Dissemination in Market Liquidity: Evidence from Firms' Use of Twitter

Adopting a platform approach in servitization : Leveraging the value of digitalization

What do hotel customers complain about? Text analysis using structural topic model

Why do top movie reviewers receive invitations to exclusive screenings? Even popular technology bloggers get free new gadgets for reviewing. How much do these reviewers really matter for businesses? While the impact of online reviews on sales of products and services has been well established, not much literature is available on impact of reviewers for businesses. Source credibility theory expounds how a communication's persuasiveness is affected by the perceived credibility of its source. So, perceived trustworthiness of reviewers should influence acceptance of reviews, and consequently should have an indirect impact on sales. Using local business review data from Yelp.com, this paper successfully tests the premise that reviewer trustworthiness positively moderates the impact of review-based online reputation on business patronages. Given the importance of reviewer trustworthiness, the next logical question is how to estimate and predict it, if no direct proxy is available? We propose a theoretical model with several reviewer characteristics (positivity, involvement, experience, reputation, competence, sociability) affecting reviewer trustworthiness, and find all factors to be significant using the robust regression method. Further, using these factors, a predictive classification of reviewers into high and low level of potential trustworthiness is done using logistic regression with nearly 83% accuracy. Our findings have several implications - firstly, businesses should focus on building a good review-based online reputation; secondly, they should encourage top trustworthy reviewers to review their products and services; and thirdly, trustworthy reviewers could be identified and ranked using reviewer characteristics. Trustworthiness of reviewers impacts sales of products they review.Review data from Yelp is analyzed to identify trustworthiness of reviewers.Various characteristics of reviewers that influence trustworthiness are identified.Logistic regression is used to identify highly trustworthy reviewers.

Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business

This study investigates if reviewers' pattern of rating is consistent over time and predictable. Two interesting results emerge from the econometric analyses using publicly available data from TripAdvisor.com . First, reviewers' rating behavior is consistent over time and across products. Furthermore, most of the variation in their future rating behavior can be explained by their rating behavior in the past rather than by the observed average rating. Second, reviews by reviewers with higher absolute bias in rating in the past receive more helpful votes in future. We further divide the bias in rating into intrinsic bias (driven by intrinsic reviewer characteristics) and extrinsic bias (driven by influences beyond intrinsic bias) and document that intrinsic bias plays a more significant role in influencing helpful votes for reviews than extrinsic bias. Our results are robust to different product categories and different definition of bias. Overall our results indicate that in the online review context, the observed average rating or an attention grabbing strategy may not be as important as believed in the past. This study provides insights into reviewers' rating behavior and prescribes actionable items for online vendors so that they can proactively influence online opinion instead of passively responding to them.

Indranil Bose

Papers

What do hotel customers complain about? Text analysis using structural topic model

Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business

Follow the herd or be myself? An analysis of consistency in behavior of reviewers and helpfulness of their reviews

My words for your pizza: An analysis of persuasive narratives in online crowdfunding

Digital transformation in entrepreneurial firms through information exchange with operating environment