Bayesian Inference for Periodic Regime-Switching Models

Nous présentons une classe générale de modèles non-linéaires avec changement de régime Markovienne. Les modèles proposés permettent d'avoir une structure périodique pour la chaîne de Markov ainsi que des effets saisonniers dans chaqu'un des régimes. La classe de structure proposée permet d'avoir des interdépendences entre les fluctuationssaisonnières, les cycles d'affaire et la composante de croissance. Une méthode Baysienne basée sur le principe de l'échantillonage de Gibbs est utilisée pour estimation et interférence. Deux exemples empiriques sont fournis, un premier utilisant des séries de mise en chantier de0501sons, tandis que le second couvre la production industrielle aux États-Unis.We present a general class of nonlinear time series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for nontrivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the competitional burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical examples as illustration, one using housing starts data while the other covers U.S. post WWII individual production

Bayesian Inference for Periodic Regime-Switching Models

We present a general class of nonlinear time series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for nontrivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the competitional burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical examples as illustration, one using housing starts data while the other covers U.S. post WWII individual production. Nous présentons une classe générale de modèles non-linéaires avec changement de régime Markovienne. Les modèles proposés permettent d'avoir une structure périodique pour la chaîne de Markov ainsi que des effets saisonniers dans chaqu'un des régimes. La classe de structure proposée permet d'avoir des interdépendences entre les fluctuationssaisonnières, les cycles d'affaire et la composante de croissance. Une méthode Baysienne basée sur le principe de l'échantillonage de Gibbs est utilisée pour estimation et interférence. Deux exemples empiriques sont fournis, un premier utilisant des séries de mise en chantier de0501sons, tandis que le second couvre la production industrielle aux États-Unis.Markov switching; Periodic models; Seasonality; Gibbs sampler, Modèles à changement de régime ; Structure périodique ; Saisonnalité ; Échantillonage de Gibbs

Permeability of membranes in the liquid ordered and liquid disordered phases

The functional significance of ordered nanodomains (or rafts) in cholesterol rich eukaryotic cell membranes has only begun to be explored. This study exploits the correspondence of cellular rafts and liquid ordered (L-o) phases of three-component lipid bilayers to examine permeability. Molecular dynamics simulations of L-o phase dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylcholine (DOPC), and cholesterol show that oxygen and water transit a leaflet through the DOPC and cholesterol rich boundaries of hexagonally packed DPPC microdomains, freely diffuse along the bilayer midplane, and escape the membrane along the boundary regions. Electron paramagnetic resonance experiments provide critical validation: the measured ratio of oxygen concentrations near the midplanes of liquid disordered (L-d) and L-o bilayers of DPPC/DOPC/cholesterol is 1.75 +/- 0.35, in very good agreement with 1.3 +/- 0.3 obtained from simulation. The results show how cellular rafts can be structurally rigid signaling platforms while remaining nearly as permeable to small molecules as the L-d phase

Economic Fluctuations and Diffusion

Stock price changes occur through transactions, just as diffusion in physical systems occurs through molecular collisions. We systematically explore this analogy and quantify the relation between trading activity - measured by the number of transactions $N_{\Delta t}$ - and the price change $G_{\Delta t}$, for a given stock, over a time interval $[t, t+\Delta t]$. To this end, we analyze a database documenting every transaction for 1000 US stocks over the two-year period 1994-1995. We find that price movements are equivalent to a complex variant of diffusion, where the diffusion coefficient fluctuates drastically in time. We relate the analog of the diffusion coefficient to two microscopic quantities: (i) the number of transactions $N_{\Delta t}$ in $\Delta t$, which is the analog of the number of collisions and (ii) the local variance $w^2_{\Delta t}$ of the price changes for all transactions in $\Delta t$, which is the analog of the local mean square displacement between collisions. We study the distributions of both $N_{\Delta t}$ and $w_{\Delta t}$, and find that they display power-law tails. Further, we find that $N_{\Delta t}$ displays long-range power-law correlations in time, whereas $w_{\Delta t}$ does not. Our results are consistent with the interpretation that the pronounced tails of the distribution of $G_{\Delta t} are due to$w_{\Delta t}$, and that the long-range correlations previously found for$| G_{\Delta t} |$are due to$N_{\Delta t}$.Comment: RevTex 2 column format. 6 pages, 36 references, 15 eps figure

Unit roots in periodic autoregressions

Abstract. This paper analyzes the presence and consequences of a unit root in periodic autoregressive models for univariate quarterly time series. First, we consider various representations of such models, including a new parametrization which facilitates imposing a unit root restriction. Next, we propose a class of likelihood ratio tests for a unit root, and we derive their asymptotic null distributions. Likelihood ratio tests for periodic parameter variation are also proposed. Finally, we analyze the impact on unit root inference of misspecifying a periodic process by a constant-parameter model

The merit of high-frequency data in portfolio allocation

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown