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1 – 10 of 409Wayne S. DeSarbo, Qiong Wang and Simon J. Blanchard
The paper aims to examine the nature of competition within an industry by proposing and examining three separate sources of competitive heterogeneity: the strategies that industry…
Abstract
Purpose
The paper aims to examine the nature of competition within an industry by proposing and examining three separate sources of competitive heterogeneity: the strategies that industry members use, the performance that they obtain, and how effectively the strategies are utilized to obtain such performance results.
Design/methodology/approach
To do so, a restricted latent structure finite mixture model is devised that can quantify the contribution of these three potential sources of heterogeneity in the formulation of latent competitive groups within an industry. The paper illustrate this modeling framework with respect to COMPUSTAT strategy and performance data collected for public banks in the USA.
Findings
The paper shows how traditional conceptualizations via strategic or performance groups are inadequate to fully represent intra‐industry heterogeneity.
Originality/value
This research paper proposes a new class of restricted finite mixture‐based models, which fit a variety of alternative forms/models of heterogeneity. Information heuristics are developed to indicate “best model.”
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Laurent Callot and Johannes Tang Kristensen
This paper shows that the parsimoniously time-varying methodology of Callot and Kristensen (2015) can be applied to factor models. We apply this method to study macroeconomic…
Abstract
This paper shows that the parsimoniously time-varying methodology of Callot and Kristensen (2015) can be applied to factor models. We apply this method to study macroeconomic instability in the United States from 1959:1 to 2006:4 with a particular focus on the Great Moderation. Models with parsimoniously time-varying parameters are models with an unknown number of break points at unknown locations. The parameters are assumed to follow a random walk with a positive probability that an increment is exactly equal to zero so that the parameters do not vary at every point in time. The vector of increments, which is high dimensional by construction and sparse by assumption, is estimated using the Lasso. We apply this method to the estimation of static factor models and factor-augmented autoregressions using a set of 190 quarterly observations of 144 US macroeconomic series from Stock and Watson (2009). We find that the parameters of both models exhibit a higher degree of instability in the period from 1970:1 to 1984:4 relative to the following 15 years. In our setting the Great Moderation appears as the gradual ending of a period of high structural instability that took place in the 1970s and early 1980s.
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