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Diffusion Index (diffusion + index)
Selected AbstractsMonitoring and Forecasting Currency CrisesJOURNAL OF MONEY, CREDIT AND BANKING, Issue 2-3 2008ATSUSHI INOUE currency crises; forecasting; leading indicators; Diffusion Index; exchange rates Can we improve forecasts of currency crises by using a large number of predictors? Which predictors should we use? This paper evaluates the performance of traditional leading indicators and a new Diffusion Index (DI) method as Early Warning Systems to monitor the risk and forecast the likelihood of the recent currency crises in East Asia. We find that the DI performs quite well in real time. For most countries, the forecasted probabilities of a crisis increase substantially around the actual time of the crisis. The economic variables that help in forecasting future crises are output growth, interest rates and money growth. [source] Nowcasting and predicting data revisions using panel survey dataJOURNAL OF FORECASTING, Issue 3 2010Troy D. Matheson Abstract The qualitative responses that firms give to business survey questions regarding changes in their own output provide a real-time signal of official output changes. The most commonly used method to produce an aggregate quantitative indicator from business survey responses,the net balance or diffusion index,has changed little in 40 years. This paper investigates whether an improved real-time signal of official output data changes can be derived from a recently advanced method on the aggregation of survey data from panel responses. We find, in a New Zealand application, that exploiting the panel dimension to qualitative survey data gives a better in-sample signal about official data than traditional methods. Out-of-sample, it is less clear that it matters how survey data are quantified, with simpler and more parsimonious methods hard to improve. It is clear, nevertheless, that survey data, exploited in some form, help to explain revisions to official data. Copyright © 2009 John Wiley & Sons, Ltd. [source] FORECASTING AUSTRALIAN MACROECONOMIC VARIABLES USING A LARGE DATASETAUSTRALIAN ECONOMIC PAPERS, Issue 1 2010SARANTIS TSIAPLIAS This paper investigates the forecasting performance of the diffusion index approach for the Australian economy, and considers the forecasting performance of the diffusion index approach relative to composite forecasts. Weighted and unweighted factor forecasts are benchmarked against composite forecasts, and forecasts derived from individual forecasting models. The results suggest that diffusion index forecasts tend to improve on the benchmark AR forecasts. We also observe that weighted factors tend to produce better forecasts than their unweighted counterparts. We find, however, that the size of the forecasting improvement is less marked than previous research, with the diffusion index forecasts typically producing mean square errors of a similar magnitude to the VAR and BVAR approaches. [source] Asynchrony of the early maturation of white matter bundles in healthy infants: Quantitative landmarks revealed noninvasively by diffusion tensor imagingHUMAN BRAIN MAPPING, Issue 1 2008Jessica Dubois Abstract Normal cognitive development in infants follows a well-known temporal sequence, which is assumed to be correlated with the structural maturation of underlying functional networks. Postmortem studies and, more recently, structural MR imaging studies have described qualitatively the heterogeneous spatiotemporal progression of white matter myelination. However, in vivo quantification of the maturation phases of fiber bundles is still lacking. We used noninvasive diffusion tensor MR imaging and tractography in twenty-three 1,4-month-old healthy infants to quantify the early maturation of the main cerebral fascicles. A specific maturation model, based on the respective roles of different maturational processes on the diffusion phenomena, was designed to highlight asynchronous maturation across bundles by evaluating the time-course of mean diffusivity and anisotropy changes over the considered developmental period. Using an original approach, a progression of maturation in four relative stages was determined in each tract by estimating the maturation state and speed, from the diffusion indices over the infants group compared with an adults group on one hand, and in each tract compared with the average over bundles on the other hand. Results were coherent with, and extended previous findings in 8 of 11 bundles, showing the anterior limb of the internal capsule and cingulum as the most immature, followed by the optic radiations, arcuate and inferior longitudinal fascicles, then the spinothalamic tract and fornix, and finally the corticospinal tract as the most mature bundle. Thus, this approach provides new quantitative landmarks for further noninvasive research on brain-behavior relationships during normal and abnormal development. Hum Brain Mapp, 2008. © 2007 Wiley-Liss, Inc. [source] Twenty-five pitfalls in the analysis of diffusion MRI data,NMR IN BIOMEDICINE, Issue 7 2010Derek K. Jones Abstract Obtaining reliable data and drawing meaningful and robust inferences from diffusion MRI can be challenging and is subject to many pitfalls. The process of quantifying diffusion indices and eventually comparing them between groups of subjects and/or correlating them with other parameters starts at the acquisition of the raw data, followed by a long pipeline of image processing steps. Each one of these steps is susceptible to sources of bias, which may not only limit the accuracy and precision, but can lead to substantial errors. This article provides a detailed review of the steps along the analysis pipeline and their associated pitfalls. These are grouped into 1 pre-processing of data; 2 estimation of the tensor; 3 derivation of voxelwise quantitative parameters; 4 strategies for extracting quantitative parameters; and finally 5 intra-subject and inter-subject comparison, including region of interest, histogram, tract-specific and voxel-based analyses. The article covers important aspects of diffusion MRI analysis, such as motion correction, susceptibility and eddy current distortion correction, model fitting, region of interest placement, histogram and voxel-based analysis. We have assembled 25 pitfalls (several previously unreported) into a single article, which should serve as a useful reference for those embarking on new diffusion MRI-based studies, and as a check for those who may already be running studies but may have overlooked some important confounds. While some of these problems are well known to diffusion experts, they might not be to other researchers wishing to undertake a clinical study based on diffusion MRI. Copyright © 2010 John Wiley & Sons, Ltd. [source] | ||||||||||