Optimal spectral bandwidth for long memory

For long range dependent time series with a spectral singularity at frequency zero, a theory for optimal bandwidth choice in non-parametric analysis ofthe singularity was developed by Robinson (1991b). The optimal bandwidths are described and compared with those in case of analysis of a smooth spectrum. They are also analysed in case of fractional ARIMA models and calculated as a function of the self similarity parameter in some special cases. Feasible data dependent approximations to the optimal bandwidth are discussed

New methods for the analysis of long memory time series: application to Spanish inflation

Models for long-memory time series are considered, in which the autocovariance sequence is only parameterized at very long lags, or the spectral density is only parametized at very low frequencies. Various recently proposed methods for estimating the differencing parameters are reviewed, and applied to an economic time series of prices in Spain

Testing of Seasonal Fractional Integration in UK and Japanese Consumption and Income

The seasonal structure of quarterly UK and Japanese consumption and income is examined by means of fractionally-based tests proposed by Robinson (1994). These series were analysed from an autoregressive unit root viewpoint by Hylleberg, Engle, Granger and Yoo (HEGY, 1990) and Hylleberg, Engle, Granger and Lee (HEGL, 1993). We find that seasonal fractional integration, with amplitudes possibly varying across frequencies, is an alternative plausible way of modelling these series.Fractional integration, nonstationarity, seasonality

Saturn Forms by Core Accretion in 3.4 Myr

We present two new in situ core accretion simulations of Saturn with planet formation timescales of 3.37 Myr (model S0) and 3.48 Myr (model S1), consistent with observed protostellar disk lifetimes. In model S0, we assume rapid grain settling reduces opacity due to grains from full interstellar values (Podolak 2003). In model S1, we do not invoke grain settling, instead assigning full interstellar opacities to grains in the envelope. Surprisingly, the two models produce nearly identical formation timescales and core/atmosphere mass ratios. We therefore observe a new manifestation of core accretion theory: at large heliocentric distances, the solid core growth rate (limited by Keplerian orbital velocity) controls the planet formation timescale. We argue that this paradigm should apply to Uranus and Neptune as well.Comment: 4 pages, including 1 figure, submitted to ApJ Letter

Non-nested testing of spatial correlation

We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial aspect can be interpreted quite generally, in either a geographical sense, or employing notions of economic distance, or when parametric modelling arises in part from a common factor or other structure. In the former case, observations may be regularly-spaced across one or more dimensions, as is typical with much spatio-temporal data, or irregularly-spaced across all dimensions; both isotropic models and non-isotropic models can be considered, and a wide variety of correlation structures. In the second case, models involving spatial weight matrices are covered, such as “spatial autoregressive models”. The setting is sufficiently general to potentially cover other parametric structures such as certain factor models, and vector-valued observations, and here our preliminary asymptotic theory for parameter estimates is of some independent value. The test statistic is based on a Gaussian pseudo-likelihood ratio, and is shown to have an asymptotic standard normal distribution under the null hypothesis that one of the two models is correct; this limit theory rests strongly on a central limit theorem for the Gaussian pseudo-maximum likelihood parameter estimates. A small Monte Carlo study of finite-sample performance is included