@article{Gelauff:272160,
      recid = {272160},
      author = {Gelauff, G. M. M. and Harkema, R.},
      title = {ESTIMATING QUARTERLY MODELS WITH PARTLY MISSING QUARTERLY  OBSERVATIONS},
      address = {1977},
      number = {2099-2018-3139},
      series = {REPORT 7724/E},
      pages = {24},
      year = {1977},
      abstract = {In this paper Monte Carlo simulation is used in order to  compare the performance of five different methods to  estimate quarterly models with partly missing quarterly  observations. The methods are compared on the basis of the  parameter estimates they produce. The first three methods  solve the estimation problem in two steps: first the yearly  series is disaggregated into a quarterly one and then the  quarterly model is estimated. The fourth method considers  disaggregation of the yearly series within the context of  the model to be estimated and arrives simultaneously at  estimates of the missing quarterly observations and of the  parameters of the model. The last method simply consists of  maximum likelihood estimation of the yearly model. The  conclusions from this simulation study are twofold: (i)  none of the methods that are developed for the purpose of  estimating quarterly models with partly missing  observations performs significantly better than maximum  likelihood estimation of the yearly model; (ii) the  standard errors that result from application of the first  three methods are deceptive.},
      url = {http://ageconsearch.umn.edu/record/272160},
      doi = {https://doi.org/10.22004/ag.econ.272160},
}