Testing for Granger causality in large mixed-frequency VARs

In this paper we analyze Granger causality testing in a mixed-frequency VAR, originally proposed by Ghysels (2012), where the difference in sampling frequencies of the variables is large. In particular, we investigate whether past information on a low-frequency variable help in forecasting a high-frequency one and vice versa. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose two approaches to solve this problem, reduced rank restrictions and a Bayesian mixed-frequency VAR. For the latter, we extend the approach in Banbura et al. (2010) to a mixed-frequency setup, which presents an alternative to classical Bayesian estimation techniques. We compare these methods to a common aggregated low-frequency model as well as to the unrestricted VAR in terms of their Granger non-causality testing behavior using Monte Carlo simulations. The techniques are illustrated in an empirical application involving daily realized volatility and monthly business cycle fluctuations

Testing for Granger Causality in Large Mixed-Frequency VARs

We analyze Granger causality testing in a mixed-frequency VAR, where the difference in sampling frequencies of the variables is large. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose several tests based on reduced rank restrictions, and implement bootstrap versions to account for the uncertainty when estimating factors and to improve the finite sample properties of these tests. We also consider a Bayesian VAR that we carefully extend to the presence of mixed frequencies. We compare these methods to an aggregated model, the max-test approach introduced by Ghysels et al. (2015a) as well as to the unrestricted VAR using Monte Carlo simulations. The techniques are illustrated in an empirical application involving daily realized volatility and monthly business cycle fluctuations

Combining distributions of real-time forecasts: An application to U.S. growth

We extend the repeated observations forecasting (ROF) analysis of Croushore and Stark (2002) to allow for regressors of possibly higher sampling frequencies than the regressand. For the U.S. GNP quarterly growth rate, we compare the forecasting performances of an AR model with several mixed-frequency models among which is the MIDAS approach. Using the additional dimension provided by different vintages we compute several forecasts for a given calendar date and subsequently approximate the corresponding distribution of forecasts by a continuous density. Scoring rules are then employed to construct combinations of them and analyze the composition and evolvement of the implied weights over time. Using this approach, we not only investigate the sensitivity of model selection to the choice of which data release to consider, but also illustrate how to incorporate revision process information into real-time studies. As a consequence of these analyses, we introduce a new weighting scheme that summarizes information contained in the revision process of the variables under consideration

Testing for common cycles in non-stationary VARs with varied frecquency data

This paper proposes a new way for detecting the presence of common cyclical features when several time series are observed/sampled at different frequencies, hence generalizing the common-frequency approach introduced by Engle and Kozicki (1993) and Vahid and Engle (1993). We start with the mixed-frequency VAR representation investigated in Ghysels (2012) for stationary time series. For non-stationary time series in levels, we show that one has to account for the presence of two sets of long-run relationships. The First set is implied by identities stemming from the fact that the differences of the high-frequency I(1) regressors are stationary. The second set comes from possible additional long-run relationships between one of the high-frequency series and the low-frequency variables. Our transformed VECM representations extend the results of Ghysels (2012) and are very important for determining the correct set of variables to be used in a subsequent common cycle investigation. This has some empirical implications both for the behavior of the test statistics as well as for forecasting. Empirical analyses with the quarterly real GNP and monthly industrial production indices for, respectively, the U.S. and Germany illustrate our new approach. This is also investigated in a Monte Carlo study, where we compare our proposed mixed-frequency models with models stemming from classical temporal aggregation methods

Thermodynamic Properties of the Dimerised and Frustrated S=1/2 Chain

By high temperature series expansion, exact diagonalisation and temperature density-matrix renormalisation the magnetic susceptibility $\chi(T)$ and the specific heat $C(T)$ of dimerised and frustrated $S=1/2$ chains are computed. All three methods yield reliable results, in particular for not too small temperatures or not too small gaps. The series expansion results are provided in the form of polynomials allowing very fast and convenient fits in data analysis using algebraic programmes. We discuss the difficulty to extract more than two coupling constants from the temperature dependence of $\chi(T)$.Comment: 14 pages, 13 figures, 4 table

Computational Physics on Graphics Processing Units

The use of graphics processing units for scientific computations is an emerging strategy that can significantly speed up various different algorithms. In this review, we discuss advances made in the field of computational physics, focusing on classical molecular dynamics, and on quantum simulations for electronic structure calculations using the density functional theory, wave function techniques, and quantum field theory.Comment: Proceedings of the 11th International Conference, PARA 2012, Helsinki, Finland, June 10-13, 201

Spectral properties of the dimerized and frustrated S=1/2S=1/2 chain

Spectral densities are calculated for the dimerized and frustrated S=1/2 chain using the method of continuous unitary transformations (CUTs). The transformation to an effective triplon model is realized in a perturbative fashion up to high orders about the limit of isolated dimers. An efficient description in terms of triplons (elementary triplets) is possible: a detailed analysis of the spectral densities is provided for strong and intermediate dimerization including the influence of frustration. Precise predictions are made for inelastic neutron scattering experiments probing the S=1 sector and for optical experiments (Raman scattering, infrared absorption) probing the S=0 sector. Bound states and resonances influence the important continua strongly. The comparison with the field theoretic results reveals that the sine-Gordon model describes the low-energy features for strong to intermediate dimerization only at critical frustration.Comment: 21 page

Spatial processing of visual information in the movement-detecting pathway of the fly

1. Spatial processing of visual signals in the fly's movement-detecting pathway was studied by recording the responses of directionally-selective movement-detecting (DSMD) neurons in the lobula plate. The summarized results pertain to a type of neuron which preferentially responds to horizontal movement directed toward the animal's midline. Three kinds of visual stimuli were used: moving gratings, reversing-contrast gratings and reversing-contrast bars.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47087/1/359_2004_Article_BF00613743.pd