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Original Variables (original + variable)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	40%

Selected Abstracts

Constrained multivariate trend analysis applied to water quality variables

ENVIRONMETRICS, Issue 1 2002
D. M. Cooper
Abstract Constrained multivariate regression analysis is used to model trends and seasonal effects in time series measurements of water quality variables. The constraint used ensures that when identifying trends the scientifically important charge balance of model-fitted concentrations is maintained, while accounting for between variable dependencies. The analysis is a special case of linear reduction of dimensionality which preserves the integrity of a subset of the original variables, while allowing the remainder to be identified as linear combinations of this subset. The technique is applied to water quality measurements made at the outflow from Loch Grannoch, an acid-sensitive loch in Scotland. A reduction in marine ion concentrations is observed in water samples collected four times a year over the period 1988,2000. This is identified with long term variability in the marine component in rainfall. Separation of the non-marine component of the solute load shows a reduction in non-marine sulphate and calcium concentrations, and an increase in the non-marine sodium concentration. There is no significant change in either alkalinity or acid neutralizing capacity over the period. The reduction in non-marine sulphate is consistent with reductions in atmospheric inputs of sulphate. However, the reduction in sulphate has not been accompanied by a reduction in the acidity of water samples from Loch Grannoch, but with a reduction in calcium concentration and an apparent increase in organic acids, as evidenced by increased dissolved organic carbon concentrations, with possible increases in nitrate and non-marine sodium concentrations. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Random perturbation methods applied to multivariate spatial sampling design

ENVIRONMETRICS, Issue 7 2001
J. M. Angulo
Abstract The problem of estimating a multivariate spatial random process from observations obtained by sampling a related multivariate spatial random process is considered. A method based on additive perturbation of the variables of interest is proposed for the assignment of degrees of relative importance to the variables and/or locations of interest in the design of sampling strategies. In the case where the variables involved have a multivariate Gaussian distribution, some theoretical results are provided to justify the method proposed; in particular, it is proved that the amount of information contained in the data on the perturbed variables of interest is never higher than that contained in the original variables of interest. These results and the application of the method are illustrated with an empirical study, showing the variation of the effects of perturbation on spatial sampling design configurations and related ratios of information for different degrees of dependence according to the model specifications. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Towards an integrated computational tool for spatial analysis in macroecology and biogeography

GLOBAL ECOLOGY, Issue 4 2006
Thiago Fernando L. V. B. Rangel
ABSTRACT Because most macroecological and biodiversity data are spatially autocorrelated, special tools for describing spatial structures and dealing with hypothesis testing are usually required. Unfortunately, most of these methods have not been available in a single statistical package. Consequently, using these tools is still a challenge for most ecologists and biogeographers. In this paper, we present sam (Spatial Analysis in Macroecology), a new, easy-to-use, freeware package for spatial analysis in macroecology and biogeography. Through an intuitive, fully graphical interface, this package allows the user to describe spatial patterns in variables and provides an explicit spatial framework for standard techniques of regression and correlation. Moran's I autocorrelation coefficient can be calculated based on a range of matrices describing spatial relationships, for original variables as well as for residuals of regression models, which can also include filtering components (obtained by standard trend surface analysis or by principal coordinates of neighbour matrices). sam also offers tools for correcting the number of degrees of freedom when calculating the significance of correlation coefficients. Explicit spatial modelling using several forms of autoregression and generalized least-squares models are also available. We believe this new tool will provide researchers with the basic statistical tools to resolve autocorrelation problems and, simultaneously, to explore spatial components in macroecological and biogeographical data. Although the program was designed primarily for the applications in macroecology and biogeography, most of sam's statistical tools will be useful for all kinds of surface pattern spatial analysis. The program is freely available at http://www.ecoevol.ufg.br/sam (permanent URL at http://purl.oclc.org/sam/). [source]

Forecasting key macroeconomic variables from a large number of predictors: a state space approach

JOURNAL OF FORECASTING, Issue 4 2010
Arvid Raknerud
Abstract We use state space methods to estimate a large dynamic factor model for the Norwegian economy involving 93 variables for 1978Q2,2005Q4. The model is used to obtain forecasts for 22 key variables that can be derived from the original variables by aggregation. To investigate the potential gain in using such a large information set, we compare the forecasting properties of the dynamic factor model with those of univariate benchmark models. We find that there is an overall gain in using the dynamic factor model, but that the gain is notable only for a few of the key variables. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Data Revisions Are Not Well Behaved

JOURNAL OF MONEY, CREDIT AND BANKING, Issue 2-3 2008
AN ARUOBA, S. BORA
forecasting; news and noise; real-time data; NIPA variables We document the empirical properties of revisions to major macroeconomic variables in the United States. Our findings suggest that they do not satisfy simple desirable statistical properties. In particular, we find that these revisions do not have a zero mean, which indicates that the initial announcements by statistical agencies are biased. We also find that the revisions are quite large compared to the original variables and they are predictable using the information set at the time of the initial announcement, which means that the initial announcements of statistical agencies are not rational forecasts. [source]