The Great Moderation and the Relationship between Output Growth and Its Volatility

This study examines the effect of the Great Moderation on the relationship between U.S. output growth and its volatility over the period 1947 to 2006. First, we consider the possible effects of structural change in the volatility process. In so doing, we employ GARCH-M and ARCH-M specifications of the process describing output growth rate and its volatility with and without a one-time structural break in volatility. Second, our data analyses and empirical results suggest no significant relationship between the output growth rate and its volatility, favoring the traditional wisdom of dichotomy in macroeconomics. Moreover, the evidence shows that the time-varying variance falls sharply or even disappears once we incorporate a one-time structural break in the unconditional variance of output starting 1982 or 1984. That is, the integrated GARCH effect proves spurious. Finally, a joint test of a trend change and a one-time shift in the volatility process finds that the one-time shift dominates.

Modeling the Volatility of Real GDP Growth: The Case of Japan Revisited

Previous studies (e.g., Hamori, 2000; Ho and Tsui, 2003; Fountas et al., 2004) find high volatility persistence of economic growth rates using generalized autoregressive conditional heteroskedasticity (GARCH) specifications. This paper reexamines the Japanese case, using the same approach and showing that this finding of high volatility persistence reflects the Great Moderation, which features a sharp decline in the variance as well as two falls in the mean of the growth rates identified by Bai and Perronâs (1998, 2003) multiple structural change test. Our empirical results provide new evidence. First, excess kurtosis drops substantially or disappears in the GARCH or exponential GARCH model that corrects for an additive outlier. Second, using the outlier-corrected data, the integrated GARCH effect or high volatility persistence remains in the specification once we introduce intercept-shift dummies into the mean equation. Third, the time-varying variance falls sharply, only when we incorporate the break in the variance equation. Fourth, the ARCH in mean model finds no effects of our more correct measure of output volatility on output growth or of output growth on its volatility.

Total Factor Productivity and Monetary Policy: Evidence from Conditional Volatility

This paper empirically assesses whether monetary policy affects real economic activity through its affect on the aggregate supply side of the macroeconomy. Analysts typically argue that monetary policy either does not affect the real economy, the classical dichotomy, or only affects the real economy in the short run through aggregate demand new Keynesian or new classical theories. Real business cycle theorists try to explain the business cycle with supply-side productivity shocks. We provide some preliminary evidence about how monetary policy affects the aggregate supply side of the macroeconomy through its affect on total factor productivity, an important measure of supply-side performance. The results show that monetary policy exerts a positive and statistically significant effect on the supply-side of the macroeconomy. Moreover, the findings buttress the importance of countercyclical monetary policy as well as support the adoption of an optimal money supply rule. Our results also prove consistent with the effective role of monetary policy in the Great Moderation as well as the more recent rise in productivity growth.

Cross-Country Evidence on Output Growth Volatility: Nonstationary Variance and GARCH Models

This paper revisits the issue of conditional volatility in real GDP growth rates for Canada, Japan, the United Kingdom, and the United States. Previous studies find high persistence in the volatility. This paper shows that this finding largely reflects a nonstationary variance. Output growth in the four countries became noticeably less volatile over the past few decades. In this paper, we employ the modified ICSS algorithm to detect structural change in the unconditional variance of output growth. One structural break exists in each of the four countries. We then use generalized autoregressive conditional heteroskedasticity (GARCH) specifications modeling output growth and its volatility with and without the break in volatility. The evidence shows that the time-varying variance falls sharply in Canada, Japan, and the U.K. and disappears in the U.S., excess kurtosis vanishes in Canada, Japan, and the U.S. and drops substantially in the U.K., once we incorporate the break in the variance equation of output for the four countries. That is, the integrated GARCH (IGARCH) effect proves spurious and the GARCH model demonstrates misspecification, if researchers neglect a nonstationary unconditional variance.

T cell adhesion regulation from clustering GM1 lipid rafts

Lipid rafts are small laterally mobile cell membrane structures that are highly enriched in lymphocyte signaling molecules. Lipid rafts can form from the assembly of specialized lipids and proteins through hydrophobic associations from saturated acyl chains. GM1 gangliosides are a common lipid raft component and have been shown to be essential in many T cell functions. Current lipid raft theory hypothesizes that certain aspects of T cell signaling can be initiated from the coalescence of these signaling-enriched lipid rafts to sites of receptor engagement. We have described how the specific aggregation of GM1 lipid rafts can cause a reorganization of cell surface molecular associations which include dynamic associations of β1 integrins with GM1 lipid rafts. These associations had pronounced effects on T cell adhesive and migratory states. We show that GM1 lipid raft aggregation can dramatically inhibit T cell migration and chemotaxis on the extracellular matrix constituent fibronectin. This inhibition of migration function was shown to be dependent on the src kinase Lck and PKC-regulated F-actin polymerization to extending pseudopods. Furthermore, GM1 lipid raft clustering could activate T cell adhesion-strengthening mechanisms. These include an increase in cellular rigidity, the creation of polymerized cortical F-actin structures, the induction of high affinity integrin states, an increase in surface area and symmetry of the contact plane, and resistance to shear flow detachment while adherent to fibronectin. This indicates that GM1 lipid raft aggregation defines a novel stimulus to regulate lymphocyte motility and cellular adhesion which could have important implications in T cell homing mechanisms. ^

An exploratory analysis of variance and volatility in epileptic electroencephalograms

The electroencephalogram (EEG) is a physiological time series that measures electrical activity at different locations in the brain, and plays an important role in epilepsy research. Exploring the variance and/or volatility may yield insights for seizure prediction, seizure detection and seizure propagation/dynamics.^ Maximal Overlap Discrete Wavelet Transforms (MODWTs) and ARMA-GARCH models were used to determine variance and volatility characteristics of 66 channels for different states of an epileptic EEG – sleep, awake, sleep-to-awake and seizure. The wavelet variances, changes in wavelet variances and volatility half-lives for the four states were compared for possible differences between seizure and non-seizure channels.^ The half-lives of two of the three seizure channels were found to be shorter than all of the non-seizure channels, based on 95% CIs for the pre-seizure and awake signals. No discernible patterns were found the wavelet variances of the change points for the different signals. ^

Gene clustering of the small leucine -rich repeat gene family

The small leucine-rich repeat proteoglycans (or SLRPs) are a group of extracellular proteins (ECM) that belong to the leucine-rich repeat (LRR) superfamily of proteins. The LRR is a protein folding motif composed of 20–30 amino acids with leucines in conserved positions. LRR-containing proteins are present in a broad spectrum of organisms and possess diverse cellular functions and localization. In mammals, the SLRPs are abundant in connective tissues, such as bones, cartilage, tendons, skin, and blood vessels. We have discovered a new member of the class I small leucine rich repeat proteoglycan (SLRP) family which is distinct from the other class I SLRPs since it possesses a unique stretch of aspartate residues at its N-terminus. For this reason, we called the molecule asporin. The deduced amino acid sequence is about 50% identical (and 70% similar) to decorin and biglycan. However, asporin does not contain a serine/glycine dipeptide sequence required for the assembly of O-linked glycosaminoglycans and is probably not a proteoglycan. The tissue expression of asporin partially overlaps with the expression of decorin and biglycan. During mouse embryonic development, asporin mRNA expression was detected primarily in the skeleton and other specialized connective tissues; very little asporin message was detected in the major parenchymal organs. The mouse asporin gene structure is similar to that of biglycan and decorin with 8 exons. The asporin gene is localized to human chromosome 9q22-9g21.3 where asporin is part of a SLRP gene cluster that includes ECM2, osteoadherin, and osteoglycin. This gene cluster of four LRR-encoding genes is embedded in a 238 kilobase intron of another novel gene named Tes9orf that is expressed primarily in the testes of the adult mouse. The SLRP genes are not present in Drosophila or C. elegans , but reside in three separate gene clusters in the puffer fish, mice and humans. Targeted disruption of individual mouse SLRP genes display minor connective tissue defects such as skin fragility, tendon laxity, minor growth plate defects, and mild osteoporosis. However, double and triple knockouts of SLRP genes exacerbate these phenotypes. Both the double epiphycan/biglycan and the triple PRELP/fibromodulin/biglycan knockout mice exhibit premature osteoarthritis. ^

Environmental variables and multivariate seabed classification maps via k means clustering of Potter Cove, Antarctica, link to input and result files in GeoTIFF format

This study subdivides the Potter Cove, King George Island, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis includes in total 42 different environmental variables, interpolated based on samples taken during Australian summer seasons 2010/2011 and 2011/2012. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared and the most reasonable method has been applied. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested and 4, 7, 10 as well as 12 were identified as reasonable numbers for clustering the Potter Cove. Especially the results of 10 and 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.

The Effect of Exchange Rate Volatility on International Trade: The Implication for Production Networks in East Asia

This paper is an empirical investigation of the relationship between exchange rate volatility and international trade, focusing on East Asia. It finds that intra-East Asian trade is discouraged by exchange rate volatility more seriously than trade in other regions because intermediate goods trade in production networks, which is quite sensitive to exchange rate volatility compared with other types of trade, occupies a significant fraction of trade. In addition, this negative effect of volatility is mainly induced by the unanticipated volatility and has an even greater impact than that of tariffs.

The impact of unstable aids on consumption volatility in developing countries

In recent years, a large and expanding literature has examined the properties of developing economies with regard to the macroeconomic cycle.1 One such property that is characteristic of developing economies is large fluctuations in consumption. Meanwhile, aid for the low income countries is extremely volatile, and under certain circumstances, the volatile aid amplifies the consumption volatility. This document examines whether it is possible that the volatile aid yields high consumption volatility in African countries that constitute the majority of the low income countries. Our numerical analysis reveals that the strongly influential aid disbursements yield a considerably large fluctuation in consumption.

Economic voting and electoral volatility in Turkish provinces

Two groups of questions were addressed in this paper: (1) Is voter punishment of the incumbent the primary factor in electoral volatility? Are there any other types of vote swings that underlie volatility? (2) In general, does a decline in economic growth destabilize voter behavior? If so, what kinds of vote swings does an economic downturn tend to generate? Provincial-level panel data analysis yielded the following results: (1) Changes in volatility is primarily due to vote swings from the incumbent to the opposition and also to and from left-wing and right-wing parties. (2) Lower economic growth increases electoral volatility. Economic decline induces vote swings not only from the government to the opposition but also from left-wing to right-wing parties. This is probably because right-wing parties seem more concerned with economic issues and are thus more popular than left-wing parties with lower-income voters.

Generating Scale-free Networks with Adjustable Clustering Coefficient Via Random Walks

This paper presents an algorithm for generating scale-free networks with adjustable clustering coefficient. The algorithm is based on a random walk procedure combined with a triangle generation scheme which takes into account genetic factors; this way, preferential attachment and clustering control are implemented using only local information. Simulations are presented which support the validity of the scheme, characterizing its tuning capabilities.

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection

Image Segmentation Using Ant System-based Clustering Algorithm

Industrial applications of computer vision sometimes require detection of atypical objects that occur as small groups of pixels in digital images. These objects are difficult to single out because they are small and randomly distributed. In this work we propose an image segmentation method using the novel Ant System-based Clustering Algorithm (ASCA). ASCA models the foraging behaviour of ants, which move through the data space searching for high data-density regions, and leave pheromone trails on their path. The pheromone map is used to identify the exact number of clusters, and assign the pixels to these clusters using the pheromone gradient. We applied ASCA to detection of microcalcifications in digital mammograms and compared its performance with state-of-the-art clustering algorithms such as 1D Self-Organizing Map, k-Means, Fuzzy c-Means and Possibilistic Fuzzy c-Means. The main advantage of ASCA is that the number of clusters needs not to be known a priori. The experimental results show that ASCA is more efficient than the other algorithms in detecting small clusters of atypical data.