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Robinson, Peter M.
Phillips, Peter C. B.
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106
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1
Nonlinear time series with long memory : a model for stochastic volatility
Robinson, Peter M.
;
Zaffaroni, Paolo
-
1997
Persistent link: https://www.econbiz.de/10000954585
Saved in:
2
Rate optimal semiparametric estimation of the memory parameter of the Gaussian time series with long range dependence
Giraitis, Liudas
;
Robinson, Peter M.
;
Samarov, Alexander
-
1997
Persistent link: https://www.econbiz.de/10000959150
Saved in:
3
Correlation testing in time series, spatial and cross-sectional data
Robinson, Peter M.
(
contributor
)
-
2009
Persistent link: https://www.econbiz.de/10003805787
Saved in:
4
Inference on nonparametrically trending time series with frational errors
Robinson, Peter M.
(
contributor
)
-
2009
Persistent link: https://www.econbiz.de/10003805794
Saved in:
5
Inference on nonparametrically trending time series with fractional errors
Robinson, Peter M.
- In:
Econometric theory
25
(
2009
)
6
,
pp. 1716-1733
Persistent link: https://www.econbiz.de/10003904438
Saved in:
6
Efficient inference on fractionally integrated panel data models with fixed effects
Robinson, Peter M.
;
Velasco, Carlos
- In:
Journal of econometrics
185
(
2015
)
2
,
pp. 435-452
Persistent link: https://www.econbiz.de/10011348967
Saved in:
7
The estimation of misspecified long memory models
Robinson, Peter M.
- In:
Journal of econometrics
178
(
2014
)
1
,
pp. 225-230
Persistent link: https://www.econbiz.de/10010256170
Saved in:
8
Pseudo-maximum likelihood estimation of ARCH(∞) models
Robinson, Peter M.
(
contributor
); …
-
2005
Persistent link: https://www.econbiz.de/10003164285
Saved in:
9
Efficiency improvements in inference on stationary and nonstationary fractional time series
Robinson, Peter M.
-
2004
Persistent link: https://www.econbiz.de/10002458629
Saved in:
10
Robust covariance matrix estimation : HAC estimates with long memory/antipersistence correction
Robinson, Peter M.
- In:
Econometric theory
21
(
2005
)
1
,
pp. 171-180
Persistent link: https://www.econbiz.de/10002674673
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