Wavelet based long memory model for modelling wheat price in India
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Authors
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RANJIT KUMAR PAUL
ICAR-Indian Agricultural Statistics Research Institute, New Delhi
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SANDIPAN SARKAR
ICAR-Indian Agricultural Statistics Research Institute, New Delhi
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SATISH KUMAR YADAV
ICAR-Indian Agricultural Statistics Research Institute, New Delhi
Keywords:
ARIMA, ARFIMA, Long memory, Wavelet analysis, Wheat priceAbstract
Agricultural time-series data concerning production, prices, export and import of several agricultural commodities is published by Indian government along with other private agricultural sectors every year. The analysis of these factors is necessary to formulate and apply several policies regarding food acquisition and its distribution, quality and quantity of import and export products, pricing structure, MSP of agricultural commodities etc. Box - Jenkins's Autoregressive integrated moving average (ARIMA) model is broadly utilized in the field of time-series. In the field of time-series analysis, it is assumed by most of the researchers that the data points of different time lags do not depend on each other, i.e. absence of long memory process. But in agriculture, market price data exhibits that the observation are dependent on distant past. This is the possible indication of long memory process or long range dependency in the mean model. Autoregressive fractionally integrated autoregressive moving average (ARFIMA) model is generally used to portray the characteristic features of the long memory time series models as well as for the forecasting purposes. In this study wavelet decomposition is used for increasing the forecasting accuracy of the ARFIMA model. Daily wholesale data of wheat of Rewari market of Haryana for the period of January, 2010 to November, 2017 is used for the demonstration of our approach.
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References
Antoniadis A. 1997. Wavelets in statistics: A review. Journal of Italian Statistics Society 6: 291–304. DOI: https://doi.org/10.1007/BF03178905
Aminghafari M and Poggi J M (2007). Forecasting time series using wavelets. International Journal of Wavelets, Multiresolution and Information Process 5: 709–24. DOI: https://doi.org/10.1142/S0219691307002002
Beran J. 1995. Statistics for Long-Memory Processes, Chapman and Hall Publishing Inc, New York. DOI: https://doi.org/10.2307/2532977
Box G E P, Jenkins G M and Reinsel G C. 2007. Time-Series Analysis: Forecasting and Control, 3rd edition. Pearson Education, India. DOI: https://doi.org/10.1002/9781118619193.ch5
Daubechies I. 1992. Ten Lectures on Wavelets, SIAM, Philadelphia. DOI: https://doi.org/10.1137/1.9781611970104
Geweke J and Porter-Hudak S. 1983. The estimation and application of long-memory time series models. Journal of Time Series Analysis 4: 221–38. DOI: https://doi.org/10.1111/j.1467-9892.1983.tb00371.x
Granger C W J and Joyeux R. 1980. An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis 1(1): 15–19. DOI: https://doi.org/10.1111/j.1467-9892.1980.tb00297.x
Hosking J R M. 1981. Fractional differencing. Biometrika 68: 559–67. DOI: https://doi.org/10.1093/biomet/68.1.165
Ogden T. 1997. Essential wavelets for statistical applications and data analysis, Birkhauser, Boston. DOI: https://doi.org/10.1007/978-1-4612-0709-2
Paul R K, Gurung B, Samanta S and Paul A K. 2014. Modelling long memory in volatility for spot price of lentil with multi-step ahead out-of-sample forecast using AR-FIGARCH model. Economic Affairs 60(3): 457–66. DOI: https://doi.org/10.5958/0976-4666.2015.00065.0
Paul R K, Prajneshu and Ghosh H. 2013. Wavelet frequency domain approach for modelling and forecasting of Indian monsoon rainfall time-series data. Journal of the Indian Society of Agricultural Statistics 67(3): 319–27.
Paul R K. 2014. Forecasting wholesale price of pigeon pea using long memory time-series models. Agricultural Economics Research Review 27(2): 167–76. DOI: https://doi.org/10.5958/0974-0279.2014.00021.4
Paul R K. 2017. Modelling long memory in maximum and minimum temperature series in India. Mausam 68(2): 317–26. DOI: https://doi.org/10.54302/mausam.v68i2.634
Paul R K and Mitra D. 2020. Forecasting of price of rice in India using long memory time series model. National Academy of Science Letter (Accepted).
Paul R K, Gurung B and Paul A K. 2015. Modelling and forecasting of retail price of arhar dal in Karnal, Haryana. Indian Journal of Agricultural Science 85(1): 69–72.
Percival D B and Walden A T. 2000. Wavelet Methods for Time- Series Analysis. Cambridge University Press, UK. DOI: https://doi.org/10.1017/CBO9780511841040
Renaud O, Stark J L and Murtagh F. 2003. Prediction based on a multiscale decomposition. International Journal of Wavelets, Multiresolution and Information Process 1: 217–32. DOI: https://doi.org/10.1142/S0219691303000153
Soltani S, Boichu D, Simard P and Canu S. 2000. The long-term memory prediction by multiscale decomposition. Signal Processing 80: 2195–05. DOI: https://doi.org/10.1016/S0165-1684(00)00077-3
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