Representations of wreath products of algebras

Filtrations of modules over wreath products of algebras are studied and corresponding multiplicity formulas are given in terms of Littlewood–Richardson coefficients. An example relevant to Jantzen filtrations in Schur algebras is presented

Long Memory in Import and Export Price Inflation and Persistence of Shocks to the Terms of Trade

Long memory models have been successfully used to investigate the dynamic time-series behavior of inflation rates based on the CPI and WPI. However, almost no attention has been paid to import and export price inflation, nor to the terms of trade which they make up. This article investigates the dynamics of the terms of trade by focusing first on the time-series characteristics of these price series. It tests for long memory in export and import price inflation series and estimates the fractional differencing parameter using a number of approaches. To give a better idea of the degree of persistence of each series, estimates of the impulse responses are computed which take into account possible fractional integration. The dynamic behavior in changes in the terms of trade is then related to the long memory behavior of the import and export price inflation series. In a sample of eleven economies for which data is available, evidence of long memory in import and export price inflation occurs in about half the cases. Granger (1980) points out that the natural occurrence of long memory may be attributed to aggregation in macroeconomic series. Our analysis provides evidence of an alternative explanation, namely that long-memory may result from the differencing of a linear relationship between non-cointegrating variables. Specifically, the results from our analysis of eleven economies reveal that shocks to the terms of trade will persist if the constituent price inflation series are not cointegratedlong memory, terms of trade, imported inflation

Estimating Signals with Finite Rate of Innovation from Noisy Samples: A Stochastic Algorithm

As an example of the recently-introduced concept of rate of innovation, signals that are linear combinations of a finite number of Diracs per unit time can be acquired by linear filtering followed by uniform sampling. However, in reality, samples are rarely noiseless. In this paper, we introduce a novel stochastic algorithm to reconstruct a signal with finite rate of innovation from its noisy samples. Even though variants of this problem has been approached previously, satisfactory solutions are only available for certain classes of sampling kernels, for example kernels which satisfy the Strang-Fix condition. In this paper, we consider the infinite-support Gaussian kernel, which does not satisfy the Strang-Fix condition. Other classes of kernels can be employed. Our algorithm is based on Gibbs sampling, a Markov chain Monte Carlo (MCMC) method. Extensive numerical simulations demonstrate the accuracy and robustness of our algorithm.Comment: Submitted to IEEE Transactions on Signal Processin