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Testing of Parameter's Instability in a Balanced Panel: An Application to Real Effective Exchange Rate for SAARC Countries

Author

Listed:
  • Varun Agiwal
  • Jitendra Kumar
  • Sumit Kumar Sharma

Abstract

Present paper considers structural break in panel AR(1) model which allows instability in mean, variance and autoregressive coefficient. This model is extension of univariate model proposed by Meligkotsiduo et al. (2004) and review of existing panel data time series model considering break studied by Levin et al. (2002), Pesaran (2004), Bai (2010), Liu et al. (2011), Wachter and Tzavalis (2012). Paper dealt the identification of structural break by comparing the posterior probability of all possible models like break on all three parameters, only two parameters, one parameter and there is no break. A simulation study is carried out to validate the derived theorems. An Empirical analysis on Real Exchange Rate of India and its neighboring countries (SAARC countries including China) are also carried out. The present study is correctly identifying the common break on 1991 which happened due to second gulf war and international debt crisis.

Suggested Citation

  • Varun Agiwal & Jitendra Kumar & Sumit Kumar Sharma, 2018. "Testing of Parameter's Instability in a Balanced Panel: An Application to Real Effective Exchange Rate for SAARC Countries," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 61(2), pages 18-46.
  • Handle: RePEc:eei:journl:v:61:y:2018:i:2:p:18-46
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    Keywords

    Panel autoregressive model; Structural break; Prior and Posterior probability.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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