Effective judgmental forecasting in the context of fashion products

We study the conditions that influence judgmental forecasting effectiveness when predicting demand in the context of fashion products. Human judgment is of practical importance in this setting. Our goal is to investigate what type of decision support, in particular historical and/or contextual predictors, should be provided to human forecasters to improve their ability to detect and exploit linear and nonlinear cue-criterion relationships in the task environment. Using a field experiment on new product forecasts in the music industry, our analysis reveals that when forecasters are concerned with predictive accuracy and only managerial judgments are employed, providing both types of decision support data is beneficial. However, if judgmental forecasts are combined with a statistical forecast, restricting the decision support provided to human judges to contextual anchors is beneficial. We identify two novel interactions demonstrating that the exploitation of nonlinearities is easiest for human judgment if contextual data are present but historical data are absent. Thus, if the role of human judgment is to detect these nonlinearities (and the linearities are taken care of by some statistical model with which judgments are combined), then a restriction of the decision support provided would make sense. Implications for the theory and practice of building decision support models are discussed

The 2001 recession and the Chicago Fed National Index: identifying business cycle turning points

The initial release of the Chicago Fed National Activity Index (CFNAI) in early 2001 pointed to the very real possibility that the U.S. economy was teetering on the brink of recession. This article quantifies the statistical ability of the CFNAI to act as an early warning indicator of economic recessions. In simulation experiments, the CFNAI performed virtually as well as the statistical model's ideal measure of the business cycle.Recessions ; Economic indicators ; Index numbers (Economics) ; Business cycles

Understanding new products’ market performance using Google Trends

This paper seeks to empirically examine diffusion models and Google Trends’ ability to explain and nowcast the new product growth phenomenon. In addition to the selected diffusion models and Google Trends, this study proposes a new model that incorporates the two. The empirical analysis is based on the cases of the iPhone and the iPad. The results show that the new model exhibits a better curve fit among all the studied ones. In terms of nowcasting, although the performance of the new model differs from that of Google Trends in the two cases, they both produce more accurate results than the selected diffusion models

New tools for analyzing the Mexican economy: indexes of coincident and leading economic indicators

New composite indexes presented in this article could prove useful in analyzing and forecasting the Mexican economy. Keith Phillips, Lucinda Vargas, and Victor Zarnowitz present composite indexes of leading and coincident indexes for Mexico. In constructing the indexes, the economists use an approach similar to that developed by the National Bureau of Economic Research to create the composite indexes of U.S. economic activity. The authors classify peaks and troughs in the Mexican business cycle since 1980. Using these business cycle turning points, the authors determine which indicators consistently turned down prior to recessions and turned up prior to expansions. Eight of the best performing indicators are combined to create a composite index of leading economic indicators.Texas ; Economic indicators

APPLICATION OF MODERN STATISTICAL TOOLS TO SOLVING CONTEMPORARY ECONOMIC PROBLEMS: EVALUATION OF THE REGIONAL AGRICULTURAL CAMPAIGN IMPACT AND THE USDA FORECASTING EFFORTS

The research is comprised with three studies to implement statistical tools for examining two economic issues: the impact of a regional agricultural campaign on participating restaurants and efforts of U.S. Department of Agriculture (USDA) forecasting reports in agricultural commodity markets. The first study examined how various components of the Certified South Carolina campaign are valued by participating restaurants. A choice experiment was conducted to estimate the average willingness to pay (WTP) for each campaign component using a mixed logit model. Three existing campaign components--Labeling, Multimedia Advertising, and the \u27Fresh on the Menu\u27 program were found to have a significant positive economic value. Results also revealed that the type of restaurant, the level of satisfaction with the campaign, and the factors motivating participation significantly affected restaurants\u27 WTP for the campaign components. The second study evaluated the revision inefficiencies of all supply, demand, and price categories of World Agricultural Supply and Demand Estimates (WASDE) forecasts for U.S. corn, soybeans, wheat, and cotton. Significant correlations between consecutive forecast revisions were found in all crops, all categories except for the seed category in wheat forecasts. This study also developed a statistical procedure for correction of inefficiencies. The procedure took into account the issue of outliers, the impact of forecasts size and direction, and the stability of revision inefficiency. Findings suggested that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts. The third study evaluated the impact of four public reports and one private report on the cotton market: Export Sales, Crop Processing, World Agricultural Supply and Demand Estimates (WASDE), Perspective Planting, and Cotton This Month. The \u27best fitting\u27 GARCH-type models were selected separately for the daily cotton futures close-to-close, close-to-open, and open-to-close returns from January 1995 through January 2012. In measuring the report effects, we controlled for the day-of-week, seasonality, stock level, and weekend-holiday effects on cotton futures returns. We found statistically significant impacts of the WASDE and Perspective Planting reports on cotton returns. Furthermore, results indicated that the progression of market reaction varied across reports

Indirect Network Effects in New Product Growth

Indirect network effects are of prime interest to marketers because they affect the growth and takeoff of software availability for, and hardware sales of, a new product. While prior work on indirect network effects in the economics and marketing literature is valuable, these literatures show two main shortcomings. First, empirical analysis of indirect network effects is rare. Second, in contrast to the importance the prior literature credits to the chicken-and-egg paradox in these markets, the temporal pattern – which leads which? – of indirect network effects remains unstudied. Based on empirical evidence of nine markets, this study shows, among others, that: (1) indirect network effects, as commonly operationalized by prior literature, are weaker than expected from prior literature; (2) in most markets we examined, hardware sales leads software availability, while the reverse almost never happens, contradicting existing beliefs. These findings are supported by multiple methods, such as takeoff and time series analyses, and fit with the histories of the markets we studied. The findings have important implications for academia, public policy and management practice. To academia, it identifies a need for new, and more relevant, conceptualizations of indirect network effects. To public policy, it questions the need for intervention in network markets. To management practice, it downplays the importance of the availability of a large library of software for hardware technology to be successful.Chicken-and-Egg;New Product Growth;Indirect Network Effects;Takeoff

Diderot's rule

The odds of success in creative industries like the book, music or movie industry are often said to be particularly low. A 1763 rule by Denis Diderot, for example, says that only one out of ten published books is a commercial success. Yet, representative evidence on new-product success rates and their development over time is scarce. Furthermore, the standard approach to use sales as success measure can be misleading from the producer's perspective. This paper presents a novel approach to empirically identify producer success by incorporating the standard terms of contract between creator and producer into a parsimonious model of information diffusion (word-of-mouth). The model is applied to a random sample of novels. Parametric and semiparametric estimates imply a success rate between 10 and 15\% for this market. Set against Diderot's rule, these results suggest that new-product success in the book industry has been fairly constant over time.New-product success; word-of-mouth; creative industries; technological change