In this paper we develop a measure of competition based on management's disclosures in their 10‐K filing and find that firms’ rates of diminishing marginal returns on new and existing investment vary significantly with our measure. We show that these firm‐level disclosures are related to existing industry‐level measures of disclosure (e.g., Herfindahl index), but capture something distinctly new. In particular, we show that the measure has both across‐industry variation and within‐industry variation, and each is related to the firm's future rates of diminishing marginal returns. As such, our measure is a useful complement to existing measures of competition. We present a battery of specification tests designed to explore the boundaries of our measure and how it varies with the definition of industry and the presence of other measures of competition.
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires either collecting or simulating a diverse set of time series benchmarking data to enable reliable comparisons against alternative approaches. We propose GeneRAting TIme Series with diverse and controllable characteristics, named GRATIS, with the use of mixture autoregressive (MAR) models. We simulate sets of time series using MAR models and investigate the diversity and coverage of the generated time series in a time series feature space. By tuning the parameters of the MAR models, GRATIS is also able to efficiently generate new time series with controllable features. In general, as a costless surrogate to the traditional data collection approach, GRATIS can be used as an evaluation tool for tasks such as time series forecasting and classification. We illustrate the usefulness of our time series generation process through a time series forecasting application.
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