Study of Merger and Acquisition (M&A) with Spline function in AR Time Series Model Under Bayesian Framework
Keywords:
Autoregressive model, posterior distribution, loss function, merger, spline function, linear and non-linear time trendAbstract
In this paper, we use an autoregressive model to investigate the behavior of mergers and acquisitions. It studies a non-linear time trend, which is approximately converted into a linear time trend using the spline function, which divides the series into piecewise linear segments between the knots. These knots are the change points where the trend pattern gets changed. The major aim of this study is to offer a merged autoregressive spline (M-ARS) model that can be used to analyze the influence of the merger on the parameters as well as model behavior. First, we obtained an estimation setup based on the well-known classical least square method and posterior distributions under the Bayesian approach with different loss functions. Then, the effect on the series based on the merger variable is significantly determined by the Bayes factor. The applicability of the proposed model is illustrated based on a simulation study and real application in the Indian banking sector.
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