FORESIGHT, Issue 30

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Description

Summer 2013

Articles

  1. How Good Is a “Good” Forecast? Forecast Errors and Their Avoidability by Steve Morlidge 
    With this article, Foresight continues its examination of forecastability – the potential accuracy of our forecasting efforts – which is one of the most perplexing yet essential issues for the business forecasting profession. We first tackled the subject with a special feature section in our Spring 2009 issue. My introduction there indicated that assessing the forecastability of a historical time series can give us a basis for judging how successful our modeling has been (benchmarking), and how much improvement we can still hope to attain.
  2. Book Review: Is Success a Result of Skill or Luck? by Roy Batchelor offers an academic’s angle on the relative roles of luck and skill in forecasting success.
    The Success Equation – Untangling Skill and Luck in Business, Sports, and Investing
     by Michael J. Mauboussin
  3. Book Review: Tracking and Improving Our Performance in the Skill-Luck Continuum by Sean Schubert 
    The Success Equation – Untangling Skill and Luck in Business, Sports, and Investing
    Michael J. Mauboussin
  4. ARIMA: The Models of Box and Jenkins by Len Tashman and Eric Stellwagen 
    Foresight tutorials are designed to be nontechnical overviews of important methodologies, enabling business forecasters to make more informed use of their forecasting software. Our Fall 2012 issue contained Eric Stellwagen’s tutorial “Exponential Smoothing: The Workhorse of Business Forecasting.” Eric and Len now team up to discuss ARIMA, the models popularized by Box and Jenkins. They examine the pros and cons of ARIMA modeling, provide a conceptual overview of how the technique works, and discuss how best to apply it to business data.
  5. Rare Events: Come Rain or Shine: Better Forecasts for All Seasons by Paul Goodwin 
    This is Paul Goodwin’s 11th Hot New Research column for Foresight, a feature that seeks to offer non-technical summaries of important new research for students, teachers, and practitioners of forecasting. See the list of his other subjects at the end of the article.
  6. Forecasting Consumer Purchases Using Google Trends by Torsten Schmidt and Simeon Vosen 
    Torsten Schmidt and Simeon Vosen have done extensive research on the potential benefits of using Google Trends’ product search data for forecasting economic behavior. Their results point strongly to the predictive value of using Google indicators in forecasting models. Here, they provide an introduction to the Google indicators of personal consumption expenditures 
    (PCE) and compare the performance of PCE forecasting models that use the Google data with those that don’t.
  7. Book Review: by Jim Hoover {Free Article}
    Supply Chain Forecasting Software
     by Shaun Snapp

Additional information

Content

Complete Issue, Special Feature, Special Feature 1, Special Feature 2, Article 1, Article 2, Article 3, Article 4, Article 5, Article 6, Article 7, Article 8, Article 9, Free Article, Free Article 1, Free Article 2, Free Article 3, Free Article 4