Business cycle implications of internal consumption habit for New Keynesian models

This paper studies the implications of internal consumption habit for propagation and monetary transmission in New Keynesian dynamic stochastic general equilibrium (NKDSGE) models. We use Bayesian methods to evaluate the role of internal consumption habit in NKDSGE model propagation and monetary transmission. Simulation experiments show that internal consumption habit often improves NKDSGE model fit to output and consumption growth spectra by dampening business cycle periodicity. Nonetheless, habit NKDSGE model fit is vulnerable to nominal rigidity, the choice of monetary policy rule, the frequencies used for evaluation, and spectra identified by permanent productivity shocks.

Business Cycle Implications of Internal Consumption Habit for New Keynesian Model

This paper studies the implications of internal consumption habit for propagation and monetary transmission in new Keynesian dynamic stochastic general equilibrium (NKDSGE) models. Bayesian methods are employed to evaluate the role of internal consumption habit in NKDSGE model propagation and monetary transmission. Simulation experiments show that internal consumption habit often improves NKDSGE model fit to output and consumption growth spectra by dampening business cycle periodicity. Nonetheless, habit NKDSGE model fit is vulnerable to the nominal rigidity, to the choice of monetary policy rule, to the frequencies used for evaluation, and to spectra identified by permanent productivity shocks.

"Business Cycle Implications of Internal Consumption Habit for New Keynesian Models"

This paper studies the implications of internal consumption habit for new Keynesian dynamic stochastic general equilibrium (NKDSGE) models. Bayesian Monte Carlo methods are employed to evaluate NKDSGE model fit. Simulation experiments show that consumption habit often improves the ability of NKDSGE models to match output and consumption growth spectra. Nonetheless, the fit of NKDSGE models with consumption habit is susceptible to the source of the nominal rigidity, to spectra identified by permanent productivity shocks, to the frequencies used for evaluation, and to the choice of monetary policy rule. These vulnerabilities suggest that NKDSGE model specification is fragile.

Business Cycle Implications of Habit Formation

The inability of a wide array of dynamic stochastic general equilibrium (DSGE) models to generate fluctuations that resemble actual business cycles has lead to the use of habit formation in consumption. For example, habit formation has been shown to help explain the negative response of labour input to a positive, permanent technology shock, several asset pricing puzzles, and the impact of monetary shocks on real variables. Investigating four different DSGE models with the Bayesian calibration approach, this paper observes that, especially in a new Keynesian monetary business cycle model with both staggered price and wage, habit formation fails to mimic the shape of output growth in the frequency domain: it counterfactually emphasizes low frequency fluctuations in output growth, compared to the U.S. data. On the other hand, habit formation has no clear implications on other business cycle aspects including impulse responses and forecast error variance decompositions of output to permanent and transitory shocks. These observations cast doubt on habit formation as an important ingredient of the DSGE model with a rich set of internal propagation mechanisms.Business Cycle; Habit Formation; Frequency Domain; Bayesian Calibration

Nanoflare Evidence from Analysis of the X-Ray Variability of an Active Region Observed with Hinode/XRT

The heating of the solar corona is one of the big questions in astrophysics. Rapid pulses called nanoflares are among the best candidate mechanisms. The analysis of the time variability of coronal X-ray emission is potentially a very useful tool to detect impulsive events. We analyze the small-scale variability of a solar active region in a high cadence Hinode/XRT observation. The dataset allows us to detect very small deviations of emission fluctuations from the distribution expected for a constant rate. We discuss the deviations in the light of the pulsed-heating scenario.Comment: 6 pages, 4 figure

A generalization of heterochromatic graphs

In 2006, Suzuki, and Akbari & Alipour independently presented a necessary and sufficient condition for edge-colored graphs to have a heterochromatic spanning tree, where a heterochromatic spanning tree is a spanning tree whose edges have distinct colors. In this paper, we propose $f$-chromatic graphs as a generalization of heterochromatic graphs. An edge-colored graph is $f$-chromatic if each color $c$ appears on at most $f(c)$ edges. We also present a necessary and sufficient condition for edge-colored graphs to have an $f$-chromatic spanning forest with exactly $m$ components. Moreover, using this criterion, we show that a $g$-chromatic graph $G$ of order $n$ with $|E(G)|>\binom{n-m}{2}$ has an $f$-chromatic spanning forest with exactly $m$ ($1 \le m \le n-1$) components if $g(c) \le \frac{|E(G)|}{n-m}f(c)$ for any color $c$.Comment: 14 pages, 4 figure

Visualisation of the T cell differentiation programme by Canonical Correspondence Analysis of transcriptomes

BACKGROUND: Currently, in the era of post-genomics, immunology is facing a challenging problem to translate mutant phenotypes into gene functions based on high-throughput data, while taking into account the classifications and functions of immune cells, which requires new methods. RESULTS: Here we propose a novel application of a multidimensional analysis, Canonical Correspondence Analysis (CCA), to reveal the molecular characteristics of undefined cells in terms of cellular differentiation programmes by analysing two transcriptomic datasets. Using two independent datasets, whether RNA-seq or microarray data, CCA successfully visualised the cross-level relationships between genes, cells, and differentiation programmes, and thereby identified the immunological features of mutant cells (Gata3-KO T cells and Stat3-KO T cells) in a data-oriented manner. With a new concept, differentiation variable, CCA provides an automatic classification of cell samples, which had a high sensitivity and a comparable performance to other classification methods. In addition, we elaborate how CCA results can be interpreted, and reveal the features of CCA in comparison with other visualisation techniques. CONCLUSIONS: CCA is a visualisation tool with a classification ability to reveal the cross-level relationships of genes, cells and differentiation programmes. This can be used for characterising the functional defect of cells of interest (e.g. mutant cells) in the context of cellular differentiation. The proposed approach fits with common hypothesis-oriented studies in immunology, and can be used for a wide range of molecular and genomic studies on cellular differentiation mechanisms. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-1028) contains supplementary material, which is available to authorized users

Orbital Ordering in ferromagnetic Lu2V2O7

We have observed the orbital ordering in the ferromagnetic Mott-insulator Lu2V2O7 by the polarized neutron diffraction technique. The orbital ordering pattern determined from the observed magnetic form factors can be explained in terms of a linear combination of wave functions |yz>, |zx> and |xy>; |0> = (1/3)^(1/2) |xy> + (1/3)^(1/2)|yz> + (1/3)^(1/2) |zx> which is proportional to |(x + y + z)^2 - r^2>; where each orbital is extended toward the center-of-mass of the V tetrahedron. We discuss the stability of the ferromagnetic Lu2V2O7, using a Hubbard Hamiltonian with these three orbitals.Comment: 17pages. to be published in J. Phys. Soc. Jpn. 74 (2005

Visualising the cross-level relationships between pathological and physiological processes and gene expression: analyses of haematological diseases.

The understanding of pathological processes is based on the comparison between physiological and pathological conditions, and transcriptomic analysis has been extensively applied to various diseases for this purpose. However, the way in which the transcriptomic data of pathological cells relate to the transcriptomes of normal cellular counterparts has not been fully explored, and may provide new and unbiased insights into the mechanisms of these diseases. To achieve this, it is necessary to develop a method to simultaneously analyse components across different levels, namely genes, normal cells, and diseases. Here we propose a multidimensional method that visualises the cross-level relationships between these components at three different levels based on transcriptomic data of physiological and pathological processes, by adapting Canonical Correspondence Analysis, which was developed in ecology and sociology, to microarray data (CCA on Microarray data, CCAM). Using CCAM, we have analysed transcriptomes of haematological disorders and those of normal haematopoietic cell differentiation. First, by analysing leukaemia data, CCAM successfully visualised known relationships between leukaemia subtypes and cellular differentiation, and their characteristic genes, which confirmed the relevance of CCAM. Next, by analysing transcriptomes of myelodysplastic syndromes (MDS), we have shown that CCAM was effective in both generating and testing hypotheses. CCAM showed that among MDS patients, high-risk patients had transcriptomes that were more similar to those of both haematopoietic stem cells (HSC) and megakaryocyte-erythroid progenitors (MEP) than low-risk patients, and provided a prognostic model. Collectively, CCAM reveals hidden relationships between pathological and physiological processes and gene expression, providing meaningful clinical insights into haematological diseases, and these could not be revealed by other univariate and multivariate methods. Furthermore, CCAM was effective in identifying candidate genes that are correlated with cellular phenotypes of interest. We expect that CCAM will benefit a wide range of medical fields

K1,3K_{1,3}-covering red and blue points in the plane

We say that a finite set of red and blue points in the plane in general position can be $K_{1,3}$-covered if the set can be partitioned into subsets of size $4$, with $3$ points of one color and $1$ point of the other color, in such a way that, if at each subset the fourth point is connected by straight-line segments to the same-colored points, then the resulting set of all segments has no crossings. We consider the following problem: Given a set $R$ of $r$ red points and a set $B$ of $b$ blue points in the plane in general position, how many points of $R\cup B$ can be $K_{1,3}$-covered? and we prove the following results: (1) If $r=3g+h$ and $b=3h+g$, for some non-negative integers $g$ and $h$, then there are point sets $R\cup B$, like $\{1,3\}$-equitable sets (i.e., $r=3b$ or $b=3r$) and linearly separable sets, that can be $K_{1,3}$-covered. (2) If $r=3g+h$, $b=3h+g$ and the points in $R\cup B$ are in convex position, then at least $r+b-4$ points can be $K_{1,3}$-covered, and this bound is tight. (3) There are arbitrarily large point sets $R\cup B$ in general position, with $r=b+1$, such that at most $r+b-5$ points can be $K_{1,3}$-covered. (4) If $b\le r\le 3b$, then at least $\frac{8}{9}(r+b-8)$ points of $R\cup B$ can be $K_{1,3}$-covered. For $r>3b$, there are too many red points and at least $r-3b$ of them will remain uncovered in any $K_{1,3}$-covering. Furthermore, in all the cases we provide efficient algorithms to compute the corresponding coverings.Comment: 29 pages, 10 figures, 1 tabl