@article{Wang:285884,
      recid = {285884},
      author = {Wang, Yu and Dorfman, Jeffrey H.},
      title = {Forecasting Crop Prices using Leading Economic Indicators  and Bayesian Model Selection},
      address = {2018-04},
      series = {NCCC-134 Applied Commodity Price Analysis, Forecasting,  and Market Risk Management},
      year = {2018},
      abstract = {Corn, wheat and soybeans are very important to the US  agricultural sector as the main sources of many farmers’  income. Thus, forecasting the prices of these three crops  is important. When considering model specification of crop  price forecasting models, this paper focuses on potential  benefits from including leading economic indicators  variables, both those clearly related to agriculture such  as the crude oil price and interest rate and those not  clearly related such as the purchasing managers index or  the S&P500 stock price index. To do this, our paper tests  whether leading economic indicators can be used to improve  the forecasts of corn, wheat, and soybean future prices. We  take a Bayesian approach to estimate the probability that a  set of leading indicators belong in the forecasting model  where specification uncertainty is explicitly modeled by  assuming a prior distribution over a very large set of  models. Model specifications considered vary by different  lag lengths for leading indicators and crop prices as well  as which variables are included at all. We apply this  method to corn, soybean and wheat monthly spot price data  from 1985 to 2016. The results show that several leading  economic indicators appear to be useful for forecasting  crop prices.},
      url = {http://ageconsearch.umn.edu/record/285884},
      doi = {https://doi.org/10.22004/ag.econ.285884},
}