AR(1) time series with approximated Beta marginal
We consider the AR(1) time series model Xt ? ?Xt?1 = ?t, ??p ? N \ {1}, when Xt has Beta distribution B(p, q), p ? (0, 1], q > 1. Special attention is given to the case p = 1 when the marginal distribution is approximated by the power law distribution closely connected with the Kumaraswamy distri...
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| Veröffentlicht in: | Publications de l'Institut mathématique (Belgrade) 2010, Vol.88 (102), p.87-98 |
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| container_title | Publications de l'Institut mathématique (Belgrade) |
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| creator | Popovic, Bozidar |
| description | We consider the AR(1) time series model Xt ? ?Xt?1 = ?t, ??p ? N \ {1}, when
Xt has Beta distribution B(p, q), p ? (0, 1], q > 1. Special attention is
given to the case p = 1 when the marginal distribution is approximated by
the power law distribution closely connected with the Kumaraswamy
distribution Kum(p, q), p ? (0, 1], q > 1. Using the Laplace transform
technique, we prove that for p = 1 the distribution of the innovation
process is uniform discrete. For p ? (0, 1), the innovation process has a
continuous distribution. We also consider estimation issues of the model.
nema |
| doi_str_mv | 10.2298/PIM1002087P |
| format | Article |
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Xt has Beta distribution B(p, q), p ? (0, 1], q > 1. Special attention is
given to the case p = 1 when the marginal distribution is approximated by
the power law distribution closely connected with the Kumaraswamy
distribution Kum(p, q), p ? (0, 1], q > 1. Using the Laplace transform
technique, we prove that for p = 1 the distribution of the innovation
process is uniform discrete. For p ? (0, 1), the innovation process has a
continuous distribution. We also consider estimation issues of the model.
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Xt has Beta distribution B(p, q), p ? (0, 1], q > 1. Special attention is
given to the case p = 1 when the marginal distribution is approximated by
the power law distribution closely connected with the Kumaraswamy
distribution Kum(p, q), p ? (0, 1], q > 1. Using the Laplace transform
technique, we prove that for p = 1 the distribution of the innovation
process is uniform discrete. For p ? (0, 1), the innovation process has a
continuous distribution. We also consider estimation issues of the model.
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Xt has Beta distribution B(p, q), p ? (0, 1], q > 1. Special attention is
given to the case p = 1 when the marginal distribution is approximated by
the power law distribution closely connected with the Kumaraswamy
distribution Kum(p, q), p ? (0, 1], q > 1. Using the Laplace transform
technique, we prove that for p = 1 the distribution of the innovation
process is uniform discrete. For p ? (0, 1), the innovation process has a
continuous distribution. We also consider estimation issues of the model.
nema</abstract><doi>10.2298/PIM1002087P</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 0350-1302 |
| ispartof | Publications de l'Institut mathématique (Belgrade), 2010, Vol.88 (102), p.87-98 |
| issn | 0350-1302 1820-7405 |
| language | eng |
| recordid | cdi_crossref_primary_10_2298_PIM1002087P |
| source | EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| title | AR(1) time series with approximated Beta marginal |
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