@article{Pena-Levano:169814,
      recid = {169814},
      author = {Pena-Levano, Luis M. and Ramirez, Octavio A.},
      title = {EFFICIENCY GAINS IN COTTON PRICE FORECASTING USING  DIFFERENT LEVELS OF DATA AGGREGATION},
      address = {2014},
      number = {329-2016-13049},
      series = {4877},
      year = {2014},
      note = {POSTER PRESENTATION AAEA 2014},
      abstract = {The forecasting efficiency gains obtained by building time  series models in which the data are optimally aggregated  have been studied from a theoretical perspective in  numerous studies.  However, an empirical study focused on  the potential benefits of temporal disaggregation in  commodity price forecasting has not been conducted. 
This  is the case even though commodities markets are extremely  important for the economic performance of the U.S.  agricultural sector, where a slight difference in a  prediction represents losses of million of dollars. One  important commodity is cotton, which generated  approximately $25.0 billion in annual revenue and was  responsible for 200,000 jobs in 2008 (USDA, 2012). 
This  study evaluates the efficiency gains in forecasting cotton  cash prices using alternative ARMA models with varying  levels of temporal aggregations (daily, weekly, monthly and  annual). 
More specifically, it evaluates whether the  disaggregated models can produce more accurate aggregated  price predictions than the corresponding aggregated models.  Likewise, this is the first study that incorporates the  daily level of aggregation to evaluate the efficiency gain  in forecasting.The dataset consisted of approximately 60  years of daily cotton prices (9,120 observations from  1972-2010) in which the prices were adjusted using the  Consumer Price Index (CPI).
The results suggested that  overall, disaggregation leads to gains in efficiency; which  would be consistent with the results of the theoretical  studies of Tiao (1972), Koreisha and Fang (2004). Finally,  the weekly model was the most efficient in forecasting the  cotton prices. These results are important for cotton  farmers because it could lead to better investment and  hedging strategies.},
      url = {http://ageconsearch.umn.edu/record/169814},
      doi = {https://doi.org/10.22004/ag.econ.169814},
}