Forecasting Patent Revenue with Regression Analysis and Monte Carlo Simulations




About the Course:

Forecasting the revenue streams associated with patents calls for statistical analysis. During this session regression analysis and Monte Carlo simulations are explained in great detail. Listeners are walked through over 20 Excel worksheets and are privy to the application of dozens of calculations and formulas. Case studies of emerging technology and mature technology companies are presented.

The following are among the issues discussed:

  • Time series regression analysis
  • Hypothesis testing with regression analysis
  • Excel forecasting tools–e.g. LINEST and FORECAST
  • Improving accuracy of regression analysis using residuals, ordinary least squares and Excel’s Solver
  • Projecting royalty revenue using Linear, Polynomial, Logarithmic and Exponential trendlines
  • Harnessing statistical tools such as R2, F stat, p-value to enhance forecasting accuracy
  • Correcting for homoscedasticity and autocorrelation
  • The intersection of the discounted cash flow method and Monte Carlo analysis
  • The Fisher-Pry method of early stage forecasting of patent revenues
  • Monte Carlo analysis with normal, lognormal, triangle and Latin Hypercube distributions
  • Creation of confidence intervals
  • Determination of incidence of various scenarios

Course Length: Approx. 2.0 hours

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