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Venkatesh, Panasa
- ARIMA Model for Forecasting of Greengram Prices in Telangana by using SAS
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Authors
Affiliations
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
2 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
2 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 10, No 2 (2019), Pagination: 210-214Abstract
Autoregressive integrated moving average (ARIMA) approach has been applied for modeling and forecasting of greengram prices in Telangana. Autocorrelation (AC) and partial autocorrelation (PAC) functions were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting the future production. To this end, evaluation of forecasting is carried out with Akaike’s information criterion (AIC) and Schwarz’s Bayesian information criterion ( BIC). The best identified model for the data under consideration was used for out-of-sample forecasting upto November 2019.Keywords
ARIMA Model, Forecasting, Greengram, SAS.
References
- Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (2007). Time series analysis: forecasting and control, 3rd Ed. Dorling Kindersley, Pvt. Ltd., New Delhi (India).
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- Meyler, Aidan, Kenny, Geoff and Quinn, Terry (1998). Forecasting Irish inflation using ARIMA models, Central Bank and Financial Services Authority of Ireland Technical Paper Series, Vol. 1998, No. 3/RT/98, pp. 1-48.
- Prajneshu and Venugopalan, R. (1998). On nonlinear procedure for obtaining length - weight relationship. Indian J. Anim. Sci., 68 (1) : 452-456.
- Singh, S., Ramasubramanian,V. and Mehta, S.C. (2007). Statistical models for forecasting rice production of India. J. Indian Society of Agric. Statist., 61(2): 80- 83.
- Stergiou, K. I. (1989), Modeling and forecasting the fishery for pilchard (Sardina pilchardus) in Greek waters using ARIMA time-series models, ICES J. Marine Sci., 46 (1):16-23.
- Price Movements of Redgram Major Markets in India by using Cointegration Analysis
Abstract Views :441 |
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Authors
Affiliations
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 10, No 2 (2019), Pagination: 234-239Abstract
The main objective of the study is to assess cointegration of major redgram markets and price movement in major markets in India using important econometric tools like Augmented dickey-fuller (ADF), Johansen’s cointegration test, granger causality test and vector error correction model (VECM). The results of the study indicated that in the long-run there was a two direction relationship between market prices. There is bidirectional causality affected on redgram prices redgram prices of Mumbai – Indore and Mumbai –Amravati. There is unidirectional causality affected on redgram prices of Amravati –Vijayawada, Gulbarga – Tandur, Gulbarga –Vijayawada, Indore -Vijayawada, Mumbai –Vijayawada. Results of vector error correction model (VECM) showed Mumbai market one month lag price is affecting current prices of Gulbarga market. Amravati market one month lag price is affecting current prices of Vijayawada market. Tandur market two months lag price is affecting current prices of Gulbarga and Amravati markets.Keywords
Price Movements, Redgram Major Markets, using Cointegration Analysis.References
- Adämmer, P. and Bohl, T.M. (2015). Speculative bubbles in agricultural prices. Quarterly Rev. Econ. & Finance, 55: 67–76.
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- Campiche, J.L., Bryant, H.L., Richardson, J.W. and Outlaw, J.L. (2007). Examining the evolving correspondence between petroleum prices and agricultural commodity prices. The American Agricultural Economics Association Annual Meeting, Portland, OR. July 29-August 1, 2007.
- Engle, R.F. and Granger, C.W.J. (1987). Cointegration and error correction: Representation, Estimation and Testing, Econometrica, 55 : 251-276.
- Esposti, R. and Listorti, G. (2013). Agricultural price transmission across space and commodities during price bubbles, Agric. Econ., 44: 125–139.
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- Granger, C.W.J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37 (2) : 424-438.
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- Johansen, S. (1988). Statistical analysis of cointegration vectors, J. Econ. Dynamics & Control, 12(2-3): 231-254.
- Johansen, S. and Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with application to the demand for money. Oxford Bulletin Economics & Statistics, 52(3): 169-209.
- Nazlioglu, S. and Soytas, S. (2012). Oil price, agricultural commodity prices and the dollar: A Panel Cointegration &Causality Analysis, 34 : 1098–1104.
- Zhang, Z., Lohr, L., Escalante, C. and Wetzstein, M. (2010). Food versus fuel: What do prices tell us? Energy Policy, 38 : 445–451.
- WEBLIOGRAPHY
- www.agriwatch.com
- agrimarketing.telangana.gov.in.
- Agricultural Market Intelligence Center–A Case Study of Chilli Crop Price Forecasting Intelangana
Abstract Views :541 |
PDF Views:0
Authors
Affiliations
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
1 Department of Agricultural Economics (A.M.I.C.), College of Agriculture, Professor Jayashankar Telangana State Agricultural University, Hyderabad (Telangana), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 10, No 2 (2019), Pagination: 257-261Abstract
Indian is an agriculture based country, where more than 50 per cent of population is depend on agriculture. This structures the main source of income. The commitment of agribusiness in the national income in India is all the more, subsequently, it is said that agriculture in India is a backbone for Indian Economy. The majority of the rural producers are unable to understand and interpret the market and price behaviour to their advantages. Hence, market information and intelligence are crucial to enable farmers and traders to make informed decisions about what to grow, when to sell, and where to sell. The price forecasts are made by analyzing the prices of agricultural commodities concerned over 17 years using advanced statistical tools like ARIMA, ARCH, GARCH models, comparing the same with prices of futures markets and national and international reports of trade surveys besides conducting state level trade surveys. Under the project price forecasts were made for chilli twice once during Kharif season for 2 years fromKharif 2017-18 and 2018-19. Thus, out of total 4 price forecasts 3 price forecasts with more than 90 per cent precision were developed and disseminated through various means like university website, university magazine “Vyavasayam”, SMS to contact farmers, All India radio, farmers’ trainings and meetings, etc.Keywords
Agricultural Market, Intelligence Center, Chilli Crop.References
- Bharathi, R., Havaldhar, Y.N., Megeri, S.N. and Patil, G.M. (2009). Forecasting of arrivals and prices in Ramnagar and Siddlagatta market. J. Indian Society Agric. Stat., 63(3). 247-257.
- Box, G.E.P. and Jenkin, G.M. (1976). Time series of analysis. Forecasting and Control, Sam Franscico, Holden Day, California, USA.
- Haridev Singh, E. (2013). Forecasting tourist Inflow in Bhutan using seasonal ARIMA. Internat. J. Sci. & Res., 9 (2) : 242 - 245.
- Meyler, Aidan, Kenny, Geoff and Quinn, Terry (1998). Forecasting Irish inflation using ARIMA models, Central Bank and Financial Services Authority of Ireland Technical Paper Series, Vol. 1998, No. 3/RT/98 (December 1998), pp.1-48.
- Paul, R. K., Alam, Wasi and Paul, A.K. (2014). Prospects of livestock and dairy production in India under time series framework. Indian J.Anim. Sci., 84 (4): 462–466.
- Prajneshu and Venugopalan, R. (1998). On non-linear procedure for obtaining length - weight relationship. Indian J. Anim. Sci., 68 (1) : 452-456.
- Singh, S., Ramasubramanian,V. and Mehta, S.C. (2007). Statistical models for forecasting rice production of India. J. Indian Soc. Agric. Statist., 61(2) : 80- 83.
- WEBLIOGRAPHY
- http://agrimarketing.telangana.gov.in/indexnew.jsp.