13,517 research outputs found
Comparisons of Hyv\"arinen and pairwise estimators in two simple linear time series models
The aim of this paper is to compare numerically the performance of two
estimators based on Hyv\"arinen's local homogeneous scoring rule with that of
the full and the pairwise maximum likelihood estimators. In particular, two
different model settings, for which both full and pairwise maximum likelihood
estimators can be obtained, have been considered: the first order
autoregressive model (AR(1)) and the moving average model (MA(1)). Simulation
studies highlight very different behaviours for the Hyv\"arinen scoring rule
estimators relative to the pairwise likelihood estimators in these two
settings.Comment: 14 pages, 2 figure
Dynamic models in space and time
This paper presents a first-order autoregressive distributed lag model in both space and time. It is shown that this model encompasses a wide series of simpler models frequently used in the analysis of space-time data as well as models that better fit the data and have never been used before. A framework is developed to determine which model is the most likely candidate to study space-time data. As an application, the relationship between the labor force participation rate and the unemployment rate is estimated using regional data of Germany, France and the UK derived from Eurostat, 1983-1993.
Estimation of AR and ARMA models by stochastic complexity
In this paper the stochastic complexity criterion is applied to estimation of
the order in AR and ARMA models. The power of the criterion for short strings
is illustrated by simulations. It requires an integral of the square root of
Fisher information, which is done by Monte Carlo technique. The stochastic
complexity, which is the negative logarithm of the Normalized Maximum
Likelihood universal density function, is given. Also, exact asymptotic
formulas for the Fisher information matrix are derived.Comment: Published at http://dx.doi.org/10.1214/074921706000000941 in the IMS
Lecture Notes Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org
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