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Parameter estimating in time series models
 Kostárová, Aneta ; Zichová, Jitka (advisor) ; Prášková, Zuzana (referee)
This bachelor thesis deals with some methods of parameter estimating in linear time series models. The most used approach in software products is the maximum likelihood estimation. The theoretical part explains the parameter estimation of the ARMA model by conditional and unconditional maximum likelihood estimation and demonstrates both methods for lower order models. The practical part examines and describes the imple- mentation of parameter estimating in Mathematica and R software. The comparison of the quality of the estimates calculated by various procedures of the chosen software is included. Finally, the acquired findings is used in a simulation study. 1
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