Factors Affecting the Financial Success of Motion Pictures: What is the Role of Star Power?

In the mid-1940s, American film industry was on its way up to its golden era as studios started mass-producing iconic feature films. The escalating increase in popularity of Hollywood stars was actively suggested for its direct links to box office success by academics. Using data collected in 2007, this paper carries out an empirical investigation on how different factors, including star power, affect the revenue of ‘home-run’ movies in Hollywood. Due to the subjective nature of star power, two different approaches were used: (1) number of nominations and wins of Academy Awards by the key players, and (2) average lifetime gross revenue of films involving the key players preceding the sample year. It is found that number of Academy awards nominations and wins was not statistically significant in generating box office revenue, whereas star power based on the second approach was statistically significant. Other significant factors were critics’ reviews, screen coverage and top distributor, while number of Academy awards, MPAA-rating, seasonality, being a sequel and popular genre were not statistically significant.

Expectations, Learning and Monetary Policy: An Overview of Recent Research

Expectations about the future are central for determination of current macroeconomic outcomes and the formulation of monetary policy. Recent literature has explored ways for supplementing the benchmark of rational expectations with explicit models of expectations formation that rely on econometric learning. Some apparently natural policy rules turn out to imply expectational instability of private agents’ learning. We use the standard New Keynesian model to illustrate this problem and survey the key results about interest-rate rules that deliver both uniqueness and stability of equilibrium under econometric learning. We then consider some practical concerns such as measurement errors in private expectations, observability of variables and learning of structural parameters required for policy. We also discuss some recent applications including policy design under perpetual learning, estimated models with learning, recurrent hyperinflations, and macroeconomic policy to combat liquidity traps and deflation.