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Bayesian inference for merged panel autoregressive model

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  • Jitendra Kumar
  • Varun Agiwal

Abstract

This paper proposes a new panel autoregressive model named as merged panel autoregressive (M-PAR) model that explains the desired inferences of merger and acquisition (M&A) concept. Bayesian analysis of the M-PAR model is introduced to show the impact of the merger series in the acquire series and then obtain the Bayesian estimator under different loss functions. It is noticed that the conditional posterior distribution of all model parameters appears in standard distribution form, so the Gibbs sampler algorithm is applied for Bayesian computation. Various Bayesian testing procedures are performed to understand the influence of the merged variables into the acquired variable. The proposed model is evaluated based on simulation exercises, with the result shows that the merged variable has a significant impact on the M&A series. On the empirical application, banking indicators of the Indian banking system are analyzed to support our model.

Suggested Citation

  • Jitendra Kumar & Varun Agiwal, 2022. "Bayesian inference for merged panel autoregressive model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(18), pages 6197-6217, September.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:18:p:6197-6217
    DOI: 10.1080/03610926.2020.1858101
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