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Revisiting Purchasing Power Parity for India using threshold cointegration and nonlinear unit root test

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Abstract

This study examines the Purchasing Power Parity (PPP) hypothesis in case of India for her five major trading partners over the period of 1991M1–2009M2. The study used the DF-GLS unit root test and threshold autoregressive (TAR) model as well as momentum-TAR (M-TAR) models for empirical analysis. However, we relied on TAR and MTAR models based cointegration tests to draw conclusions because of their superiority to traditional cointegration techniques as these models have limit cycles, amplitude dependent frequencies, and jump phenomena. These models are capable of producing asymmetric limit cycles and are suitable for time series data. Our empirical exercise reveals that PPP hypothesis does not exist for all major trading partners in case of India. This reveals that intermediate goods face high barriers to trade in this sampled countries. This supports the argument that Indian government has not been able to strike out the proper balance between flexibility and stability between real bilateral exchange rates and thus unable to maintaining confidence in the domestic currency that has been evident from the recent fall of rupee in relation to the US dollar.

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Notes

  1. Table 1 has been sourced from: http://www.infodriveindia.com/export-import/trade-statistics/trading-partners.aspx.

  2. It is important to mention that there is some difference in the countries mentioned in Table 1: Indian top exporter partners and in Fig. 1: Time series plots of the India’s real exchange rate with major trading partners. This is because CPI data was not available from where we sourced our data i.e., from the International Monetary Fund’s International Financial Statistics CD-ROM. Therefore, we were unable to construct the real exchange rate series for the excluded countries.

  3. http://commerce.nic.in/publications/anualreport_chapter3-2009-10.asp.

  4. See the literature review part for related review.

  5. See for more details Cuddington and Hong (2000), Baum et al. (2001), Layton and Stark (1990).

  6. Shahbaz (2009), Shahbaz and Wahid (2010) did same exercise for Pakistan and Philippines respectively. Bahmani-Oskooee and Kutan (2008) investigated the impact of devaluation on domestic output of Central European economies.

  7. A detailed review of literature on PPP hypothesis is available in Bahmani-Oskooee and Hegerty (2009).

  8. Pakistan, India, Sri Lanka, Bangladesh and China.

  9. The results by Shabbir and Rashid (2008) may be biased because they did not mention which lag length criteria was used. It is well established fact that lag-length selection plays very crucial role in the out-come of test statistics.

  10. See Bozoklu and Kutlu (2012) for more details.

  11. The real exchange rate (RER) series of a country at time t is define as (NER t *CPIT t )/CPII t , where RERt is the nominal exchange rate of India per foreign currency of trading partner, CPITt and CPIIt denote the consumer price indices of trading country and the India, respectively.

  12. The‘t-sig’ approach has been shown to produce test statistics which have better properties in terms of size and power than information-based methods such as the Akaike Information Criterion or Schwartz Bayesian Criterion (see for example, Hall 1994, Ng and Perron, 1995).

  13. We used conventional level of significance that is 5% level of significance as a benchmark and fixed k max  = 12.

  14. Chan (1993) shows to obtain a consistent estimate of the threshold \( \tau \). For example, with the M-TAR adjustment mechanism, the consistent estimate of the threshold can be estimated by ordering the \( \left\{ {\hat{\varepsilon }_{t} } \right\} \) sequence in ascending order such that \( \Updelta \varepsilon_{1}^{\tau } < \Updelta \varepsilon_{2}^{\tau } < \Updelta \varepsilon_{3}^{\tau } < \ldots < \Updelta \varepsilon_{T}^{\tau } \) where T denotes the number of usable observations. For each value of \( \Updelta \varepsilon_{j}^{\tau } \), the threshold \( \tau = \Updelta \varepsilon_{j}^{\tau } \) is set and the M-TAR model estimated in the form of Eqs. (5), (6). The estimated threshold yielding the lowest residual sum squares (RSS) is the consistent estimate of the threshold. To ensure an adequate number of observations in each regime, the standard procedure of using only the middle 80% of the observations as potential thresholds is followed.

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Acknowledgments

The authors gratefully acknowledge the constructive suggestions offered by the two anonymous referees on the earlier version of the paper. We also thank each of the individuals involved in the process and express our sincere gratitude for the in-depth reviews which helped improve the quality of the paper significantly.

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  1. ICFAI University, Room No.405, Kamalghat, Sadar, West Tripura, 799210, Tripura, India

    Aviral Kumar Tiwari

  2. COMSATS Institute of Information Technology, M. A Jinnah Campus, Defence Road, Off Raiwind Road, Lahore, Pakistan

    Muhammad Shahbaz

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  1. Aviral Kumar Tiwari
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Tiwari, A.K., Shahbaz, M. Revisiting Purchasing Power Parity for India using threshold cointegration and nonlinear unit root test. Econ Change Restruct 47, 117–133 (2014). https://doi.org/10.1007/s10644-013-9144-9

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