IDEAS home Printed from https://ideas.repec.org/p/sbp/wpaper/50.html
   My bibliography  Save this paper

Construction and Seasonal Patterns of Islamic Hijri Calendar Monthly Time Series: An Application to Consumer Price Index (CPI) in Pakistan

Author

Listed:
  • Riaz Riazuddin

    (State Bank of Pakistan)

Abstract

Time series data are compiled and analysed in accordance with Gregorian calendar, given its world-wise use. This paper presents a simple method of constructing time series in accordance with Hijri Calendar from an already compiled Gregorian time series. Preliminary seasonal analysis of Hijri time series for CPI in Pakistan provides new insights of price behavior that depends both on Gregorian and Hijri seasonality. A spliced series of monthly CPI from January 1976 to December 2008 spanning 33 Gregorian years (396 Gregorian months) is used to capture a full cycle of 34 Hijri years (408 Hijri months). Method presented is general and can be used to construct and analyse any variable of interest. Paper proposes that statistical agencies and central banks of Islamic countries should also compile data according to Hijri Calendar, in addition to existing compilation according to Gregorian calendar. This will add to a better understanding of socioeconomic behaviours in Islamic countries.

Suggested Citation

  • Riaz Riazuddin, 2012. "Construction and Seasonal Patterns of Islamic Hijri Calendar Monthly Time Series: An Application to Consumer Price Index (CPI) in Pakistan," SBP Working Paper Series 50, State Bank of Pakistan, Research Department.
    • Handle: RePEc:sbp:wpaper:50
    as

    Download full text from publisher

    File URL: http://www.sbp.org.pk/repec/sbp/wpaper/wp50.pdf
    File Function: First version, 2012
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bukhari, Syed Kalim Hyder & Abdul, Jalil & Rao, Nasir Hamid, 2011. "Detection and Forecasting of Islamic Calendar Effects in Time Series Data: Revisited," MPRA Paper 31124, University Library of Munich, Germany.
    2. Muhammad Akmal & Muhammad Usman Abbasi, 2010. "Ramadan Effect on Price Movements: Evidence from Pakistan," SBP Working Paper Series 32, State Bank of Pakistan, Research Department.
    3. Riaz Riazuddin & Mahmood ul Hasan Khan, 2002. "Detection and Forecasting of Islamic Calendar Effects in Time series Data," SBP Working Paper Series 02, State Bank of Pakistan, Research Department.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Riaz Riazuddin, 2012. "Construction and Seasonal Patterns of Islamic Hijri Calendar Monthly Time Series: An Application to Consumer Price Index (CPI) in Pakistan," Working Papers id:4927, eSocialSciences.
    2. Bukhari, Syed Kalim Hyder & Abdul, Jalil & Rao, Nasir Hamid, 2011. "Detection and Forecasting of Islamic Calendar Effects in Time Series Data: Revisited," MPRA Paper 31124, University Library of Munich, Germany.
    3. Moayedi, Vafa, 2012. "Detecting Islamic Calendar Effects on U.S. Meat Consumption: Is the Muslim Population Larger than Widely Assumed?," MPRA Paper 41554, University Library of Munich, Germany.
    4. Nida Shah & Muhammad Nadeem Qureshi & Yasra Aslam, 2017. "An Empirical Investigation of Islamic Calendar Effect in Global Islamic Equity Indices," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(6), pages 57-68, June.
    5. International Monetary Fund, 2005. "Three Attempts at Inflation Forecasting in Pakistan," IMF Working Papers 2005/105, International Monetary Fund.
    6. Mr. Joannes Mongardini & Tahsin Saadi Sedik, 2003. "Estimating Indexes of Coincident and Leading Indicators: An Application to Jordan," IMF Working Papers 2003/170, International Monetary Fund.

    More about this item

    Keywords

    Hijri; CPI; seasonal effects; gregorian; time series;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sbp:wpaper:50. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Faisal Saleem (email available below). General contact details of provider: https://edirc.repec.org/data/sbpgvpk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.