Expectations, Shocks, and Asset Returns

I use the consumer’s budget constraint to derive a relationship between stock market returns, the residuals of the trend relationship among consumption, aggregate wealth, and labour income, cay, and three major sources of risk: future changes in the housing consumption share, cr, future labour income growth, lr, and future consumption growth, lrc. Using a VAR, I compute measures of expected and unexpected long-run changes of the major determinants of asset returns and find that: (i) cay, cday, expected lr, cr, lrc and expected long-run changes in ex-ante real returns, lrret, strongly forecast future asset returns; (ii) unexpected lrc and unexpected lrret contain some predictive power for asset returns; (iii) unexpected lr and unexpected cr do not predict future asset returns. One can, therefore, use the intertemporal budget constraint and the forecasting properties of an informative VAR to generate the predictability of many economically motivated variables developed in the literature on asset pricing. The framework presented is sufficiently flexible to accommodate the implications of a wide class of optimal models of consumer behaviour without imposing a functional form on preferences.expectations, shocks, asset returns, wealth, income, consumption, housing share.

Is it possible to discriminate between different switching regressions models? An empirical investigation

In this paper we study, using the sup LR test, the possibility of discrimination between two classes of models: the Markov switching models of Hamilton (1989) and the Threshold Auto-Regressive Models (TAR) of Lim and Tong (1980). This work is motivated by the fact that generally practicians use, in applications, switching models without any statistical justification. Using experiment simulations, we show that it is very difficult to discriminate between the MSAR and the SETAR models specially using large samples. This means that when the null hypothesis is rejected, it appears that different switching models are significant. Moreover, the results show that the power of the sup LR test is sensitive to the mean, the noise variance and the delay parameter. Then, we apply this methodology to two time series: the US GNP growth rate and the US/UK exchange rate. We shall retain retain a Markov switching process for the US GNP growth rate and the US/UK exchange rate (monthly data). For the US/UK exchange rate (quarterly data), we accept the null hypothesis of a random walk.Switching Models, Sup LR test, Empirical power, Exchange rate

A Comparison of classification/regression trees and logistic regression in failure models

The use of non-parametric statistical methods, the development of models geared towards the homogeneous characteristics of corporate sub-populations, and the introduction of non-financial variables, are three main issues analysed in this paper. This study compares the predictive performance of a non-parametric methodology, namelyClassification/Regression Trees (CART), against traditional logistic regression (LR) by employing a vast set of matched-pair accounts of the smallest enterprises, known as micro-entities,from the United Kingdom for the period 1999 to 2008 that includes financial, non-financial, and macroeconomic variables. Our findings show that CART outperforms the standard approach in the literature, LR

Kernel Logistic Regression-linear for Leukemia Classification Using High Dimensional Data

Kernel Logistic Regression (KLR) is one of the statistical models that has been proposed for classification in the machine learning and data mining communities, and also one of the effective methodologies in the kernel–machine techniques. Basely, KLR is kernelized version of linear Logistic Regression (LR). Unlike LR, KLR has ability to classify data with non linear boundary and also can accommodate data with very high dimensional and very few instances. In this research, we proposed to study the use of Linear Kernel on KLR in order to increase the accuracy of Leukemia Classification. Leukemia is one of the cancer types that causes mortality in medical diagnosis problem. Improving the accuracy of Leukemia Classification is essential for more effective diagnosis and treatment of Leukemia disease. The Leukemia data sets consists of 7120 (very high dimensional) DNA micro arrays data of 72 (very few instances) patient samples on the state of Leukemia types. In Leukemia classification based upon gene expression, monitoring data using DNA micro array offer hope to achieve an objective and highly accurate classification. It can be demonstrated that the use of Linear Kernel on Kernel Logistic Regression (KLR–Linear) can improve the performance in classifying Leukemia patient samples and also can be shown that KLR–Linear has better accuracy than KLR–Polynomial and Penalized Logistic Regression

Classification and Ranking of Fermi LAT Gamma-ray Sources from the 3FGL Catalog using Machine Learning Techniques

We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or Active Galactic Nuclei (AGN). Using 1904 3FGL sources that have been identified/associated with AGN (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a sub-sample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (~90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of {\it unassociated} sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g. binaries, SNR/PWN). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest for both in-depth follow-up searches (e.g. pulsar) at various wavelengths, as well as for broader population studies.Comment: Accepted by Ap

Mortality assessment in intensive care units via adverse events using artificial neural networks

This work presents a novel approach for the prediction of mortality in intensive care units (ICUs) based on the use of adverse events, which are defined from four bedside alarms, and artificial neural networks (ANNs). This approach is compared with two logistic regression (LR) models: the prognostic model used in most of the European ICUs, based on the simplified acute physiology score (SAPS II), and a LR that uses the same input variables of the ANN model. Materials and Methods: A large dataset was considered, encompassing forty two ICUs of nine European countries. The recorded features of each patient include the final outcome, the case mix (e.g. age) and the intermediate outcomes, defined as the daily averages of the out of range values of four biometrics (e.g. heart rate). The SAPS II score requires seventeen static variables (e.g. serum sodium), which are collected within the first day of the patient's admission. A nonlinear least squares method was used to calibrate the LR models while the ANNs are made up of multilayer perceptrons trained by the RPROP algorithm. A total of 13164 adult patients were randomly divided into training (66%) and test (33%) sets. The two methods were evaluated in terms of receiver operator characteristic (ROC) curves. Results: The event based models predicted the outcome more accurately than the currently used SAPS II model (P<0.05), with ROC areas within the ranges 83.9-87.1% (ANN) and 82.6-85.2% (LR) vs 80% (LR SAPS II). When using the same inputs, the ANNs outperform the LR (improvement of 1.3-2%). Conclusion: Better prognostic models can be achieved by adopting low cost and real-time intermediate outcomes rather than static data.BIOMED Project BMH4-CT96-0817 for the provision of part of the EURICUS II data.FRICE

Slowdown and splitting of gap solitons in apodized Bragg gratings

We study the motion of gap solitons in two models of apodized nonlinear fiber Bragg gratings (BGs), with the local reflectivity (LR) varying along the fiber. A single step of LR, and a periodic array of alternating steps with opposite signs (a "Bragg superstructure") are considered. A challenging possibility is to slow down and eventually halt the soliton by passing it through the step of increasing reflectivity, thus capturing a pulse of standing light. First, we develop an analytical approach, assuming adiabatic evolution of the soliton, and making use of the energy conservation and balance equation for the momentum. Comparison with simulations shows that the analytical approximation is quite accurate (unless the inhomogeneity is too steep): the soliton is either transmitted across the step or bounces back. If the step is narrow, systematic simulations demontrate that the soliton splits into transmitted and reflected pulses (splitting of a BG soliton which hits a chirped grating was observed in experiments). Moving through the periodic "superstructure", the soliton accummulates distortion and suffers radiation loss if the structure is composed of narrow steps. The soliton moves without any loss or irreversible deformation through the array of sufficiently broad steps.Comment: to appear in a special issue on Wave-Optical Engineering, Journal of Modern Optic

Simulation-Based Exact Tests with Unidentified Nuisance Parameters under the Null Hypothesis : the Case of Jumps Tests in Model with Conditional Heteroskedasticity

We use the Monte-Carlo (MC) test technique to find valid p-values when testing for discontinuities in jump-diffusion models. While the distribution of the LR statistic for this test is typically non-standard, we show that the MC p-value is finite sample exact if no other (identified) nuisance parameter is present. Otherwise, we derive nuisance-parameter free bounds and obtain exact bounds p-values. We illustrate our approach on four classes of jump-diffusion models we use to model spot prices of copper, nickel, gold, and crude oil. We find significant jumps in all weekly time series and in a few monthly time series.Monte-Carlo test, bounds test, discontinuous process, conditional heteroscedasticity