000113520 001__ 113520
000113520 005__ 20170826033100.0
000113520 037__ $$a1607-2016-134487
000113520 041__ $$aen
000113520 245__ $$aConclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts
000113520 260__ $$c2011-08
000113520 269__ $$a2011-08
000113520 270__ $$moramirez@uga.edu$$pRamirez,   Octavio A.
000113520 300__ $$a32
000113520 336__ $$aWorking or Discussion Paper
000113520 490__ $$aFaculty Series
000113520 490__ $$a11-03
000113520 520__ $$aSimulation methods are used to measure the expected differentials between the Mean Square Errors of the forecasts from models based on temporally disaggregated versus aggregated data. This allows for novel comparisons including long-order ARMA models, such as those expected with weekly data, under realistic conditions where the parameter values have to be estimated. The ambivalence of past empirical evidence on the benefits of disaggregation is addressed by analyzing four different economic time series for which relatively large sample sizes are available. Because of this, a sufficient number of predictions can be considered to obtain conclusive results from out-of-sample forecasting contests. The validity of the conventional method for inferring the order of the aggregated models is revised.
000113520 542__ $$fLicense granted by laura  alfonso (lalfonso@uga.edu) on 2011-08-22T15:30:14Z (GMT):

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000113520 650__ $$aResearch Methods/ Statistical Methods
000113520 6531_ $$aData Aggregation
000113520 6531_ $$aEfficient Forecasting
000113520 700__ $$aRamirez, Octavio A.
000113520 8564_ $$s887587$$uhttp://ageconsearch.umn.edu/record/113520/files/RamirezPaperAUG2011.pdf
000113520 887__ $$ahttp://purl.umn.edu/113520
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000113520 912__ $$nSubmitted by laura  alfonso (lalfonso@uga.edu) on 2011-08-22T15:58:10Z
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  Previous issue date: 2011-08
000113520 982__ $$gUniversity of Georgia>Department of Agricultural and Applied Economics>Faculty Series
000113520 980__ $$a1607