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Adaptive semiparametric estimation of the memory parameter

Giraitis, Liudas; Robinson, Peter M.; Samarov, Alexander - London School of Economics (LSE) - 2000

Persistent link: https://www.econbiz.de/10010745466

Long and short memory conditional heteroscedasticity in estimating the memory parameter of levels

Robinson, Peter M.; Henry, Marc - London School of Economics (LSE) - 1998

Semiparametric estimates of long memory seem useful in the analysis of long financial time series because they are consistent under much broader conditions than parametric estimates. However, recent large sample theory for semiparametric estimates forbids conditional heteroscedasticity. We show...

Persistent link: https://www.econbiz.de/10010745869

Modified whittle estimation of multilateral models on a lattice

Robinson, Peter M.; Vidal Sanz, J. - London School of Economics (LSE) - 2005

In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensional lattice, for d ≥ 2, the achievement of asymptotic efficiency under Gaussianity, and asymptotic normality more generally, with standard convergence rate, faces two obstacles. One is the...

Persistent link: https://www.econbiz.de/10011071125

A model for long memory conditional heteroscedasticity

Giraitis, Liudas; Robinson, Peter M.; Surgailis, Donatas - London School of Economics (LSE) - 2000

For a particular conditionally heteroscedastic nonlinear (ARCH) process for which the conditional variance of the observable sequence rt is the square of an inhomogeneous linear combination of rs, s < t, we give conditions under which, for integers 1 > 2, r' has long memory autocorrelation and normalized partial sums of ri converge to fractional...</t,>

Persistent link: https://www.econbiz.de/10011071148

Efficient inference on fractionally integrated panel data models with fixed effects

Robinson, Peter M.; Velasco, Carlos - London School of Economics (LSE) - 2015

to bias caused by the individual effects, or by the consequences of eliminating them, which appears in the central limit …, though in case of two estimates these can be relaxed by bias correction, where the biases depend only on the parameters … describing autocorrelation. For the fourth estimate, there is no bias problem, and no restrictions on NN. Implications for …

Persistent link: https://www.econbiz.de/10011171755

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/10011190712

Statistical inference on regression with spatial dependence

Robinson, Peter M.; Thawornkaiwong, Supachoke - In: Journal of Econometrics 167 (2012) 2, pp. 521-542

Central limit theorems are developed for instrumental variables estimates of linear and semiparametric partly linear regression models for spatial data. General forms of spatial dependence and heterogeneity in explanatory variables and unobservable disturbances are permitted. We discuss...

Persistent link: https://www.econbiz.de/10010574069

Nonparametric trending regression with cross-sectional dependence

Robinson, Peter M. - In: Journal of Econometrics 169 (2012) 1, pp. 4-14

Panel data, whose series length T is large but whose cross-section size N need not be, are assumed to have common time trend, of unknown form. The model includes additive, unknown, individual-specific components and allows for spatial or other cross-sectional dependence and/or...

Persistent link: https://www.econbiz.de/10010574083

Efficient estimation of the semiparametric spatial autoregressive model

Robinson, Peter M. - London School of Economics (LSE) - 2007

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series...

Persistent link: https://www.econbiz.de/10010928599

Nonparametric trending regression with cross-sectional dependence

Robinson, Peter M. - 2011

Panel data, whose series length T is large but whose cross-section size N need not be, are assumed to have a common time trend. The time trend is of unknown form, the model includes additive, unknown, individual-specific components, and we allow for spatial or other cross-sectional dependence...

Persistent link: https://www.econbiz.de/10010288338

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