Narrow Results

This chapter examines the impact of product market competition on the benchmarking of a CEO’s compensation to their counterparts in peer companies. Using a large sample of US firms, we find a significantly greater effect of CEO pay benchmarking in more competitive industries than in less competitive industries. Using three proxies for managerial talent that have been used by Albuquerque et al. (2013), we find that CEO benchmarking is more pronounced in competitive markets wherein managerial talent is more valuable. This suggests that pay benchmarking and product market competition are complements. The above results are not due to industry homogeneity.

In this chapter, we will introduce the classical linear regression theory, including the classical model assumptions, the statistical properties of the Ordinary Least Squares (OLS) estimator, the t-test and the F-test, as well as the Generalized Least Squares (GLS) estimator and related statistical procedures. This chapter will serve as a starting point from which we will develop modern econometric theory.

With the development of digital technology, digital platforms have become important channels for audiences to watch movies. When studying the performance of movies on digital platforms, scholars often only focus on one of the temporal factors or non-temporal factors, but the performance of movies is affected by both factors. Based on relevant data from 128 movies collected by big data technology, this paper uses the OLS and mediating effect models to analyze the influence of the release time interval and other non-time factors on movie performance. The key findings are as follows: (1) box office plays a decisive role in the performance of movies on digital platforms, and quite a few other non-time factors have an impact on the performance of movies through the box office as a mediating variable; (2) the release time interval of the DVD&Blu-ray channel has no effect on the performance of movies in this channel; (3) there is a U-shaped relationship between the release time interval of digital platforms and the performance of digital platforms; (4) movie ratings have an additional impact on the performance of movies in network platform. This paper explains the interaction mechanism between movie release channels in a more realistic way, which has a certain reference value for relevant practitioners and scholars.

In this chapter, we introduce the classical linear regression theory, including the classical model assumptions, the statistical properties of the Ordinary Least Squares (OLS) estimator, the t-test and the F-test, as well as the Generalized Least Squares (GLS) estimator and related procedures. Various applications in economics and finance are also used to illustrate the applications of the statistical procedures.