The Cost Channel Reconsidered: A Comment Using an Identification-Robust Approach

We re-examine the empirical relevance of the cost channel of monetary policy (e.g. Ravenna and Walsh, 2006), employing recently developed moment-conditions inference methods, including identiÂ…cation-robust procedures. Using US data, our results suggest that the cost channel effect is poorly identiÂ…ed and we are thus unable to corroborate the previous results in the literatureCost channel, Phillips curve, GMM, Generalized Empirical Likelihood, Weak IdentiÂ…cation

Cointegration Tests under Multiple Regime Shifts: An Application to the Stock Price-Dividend Relationship

We examine the properties of several residual-based cointegration tests when long run parameters are subject to multiple shifts driven by an unobservable Markov process. Unlike earlier work, which considered one-off deterministic breaks, our approach has the advantage of allowing for an unspeci?ed number of stochastic breaks. We illustrate this issue by exploring the possibility of Markov switching cointegration in the stock-price dividend relationship and showing that this case is empirically relevant. Our subsequent Monte Carlo analysis reveals that standard cointegration tests are generally reliable, their performance often being robust for a number of plausible regime shift parameterizations.Present value model, Cointegration tests, Markov switching

Instance and feature weighted k-nearest-neighbors algorithm

We present a novel method that aims at providing a more stable selection of feature subsets when variations in the training process occur. This is accomplished by using an instance-weighting process -assigning different importances to instances as a preprocessing step to a feature weighting method that is independent of the learner, and then making good use of both sets of computed weigths in a standard Nearest-Neighbours classifier. We report extensive experimentation in well-known benchmarking datasets as well as some challenging microarray gene expression problems. Our results show increases in stability for most subset sizes and most problems, without compromising prediction accuracy.Peer ReviewedPostprint (published version

Exploiting the accumulated evidence for gene selection in microarray gene expression data

Machine Learning methods have of late made signicant efforts to solving multidisciplinary problems in the field of cancer classification using microarray gene expression data. Feature subset selection methods can play an important role in the modeling process, since these tasks are characterized by a large number of features and a few observations, making the modeling a non-trivial undertaking. In this particular scenario, it is extremely important to select genes by taking into account the possible interactions with other gene subsets. This paper shows that, by accumulating the evidence in favour (or against) each gene along the search process, the obtained gene subsets may constitute better solutions, either in terms of predictive accuracy or gene size, or in both. The proposed technique is extremely simple and applicable at a negligible overhead in cost.Postprint (published version

Kindness in memorable university teachers

The article presents some insights derived from research on ‘good teaching practices’ in the context of the School of Humanities, Mar del Plata State University, Argentina. He professor in charge of the ‘Introduction to Philosophy’ course has been signaled as ‘memorable’ by her advanced students, and thus become part of the investigation. In her classes, the relationship with the students entails peculiar ethical dimensions, and hospitality is one of the categories involved in her didactic proposal. Much data has been gathered through ethnographic reports of classwork, interviews, surveys and analysis of reference materials, which aims at throwing light into good teaching at university, as part of the investigation conducted by the Research Team on Education and Cultural Studies (GIEEC) in this University.Fil: Porta Vazquez, Luis Gabriel. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Flores, Graciela Nelida. Universidad Nacional de Mar del Plata; Argentin

Phase transitions in multi-cut matrix models and matched solutions of Whitham hierarchies

We present a method to study phase transitions in the large N limit of matrix models using matched solutions of Whitham hierarchies. The endpoints of the eigenvalue spectrum as functions of the temperature are characterized both as solutions of hodograph equations and as solutions of a system of ordinary differential equations. In particular we show that the free energy of the matrix model is the quasiclassical tau-function of the associated hierarchy, and that critical processes in which the number of cuts changes in one unit are third-order phase transitions described by C1 matched solutions of Whitham hierarchies. The method is illustrated with the Bleher-Eynard model for the merging of two cuts. We show that this model involves also a birth of a cut

Fine structure in the large n limit of the non-hermitian Penner matrix model

In this paper we apply results on the asymptotic zero distribution of the Laguerre polynomials to discuss generalizations of the standard large $n$ limit in the non-hermitian Penner matrix model. In these generalizations $g_n n\to t$, but the product $g_n n$ is not necessarily fixed to the value of the 't Hooft coupling $t$. If $t>1$ and the limit $l = \lim_{n\rightarrow \infty} |\sin(\pi/g_n)|^{1/n}$ exists, then the large $n$ limit is well-defined but depends both on $t$ and on $l$. This result implies that for $t>1$ the standard large $n$ limit with $g_n n=t$ fixed is not well-defined. The parameter $l$ determines a fine structure of the asymptotic eigenvalue support: for $l\neq 0$ the support consists of an interval on the real axis with charge fraction $Q=1-1/t$ and an $l$-dependent oval around the origin with charge fraction $1/t$. For $l=1$ these two components meet, and for $l=0$ the oval collapses to the origin. We also calculate the total electrostatic energy $\mathcal{E}$, which turns out to be independent of $l$, and the free energy $\mathcal{F}=\mathcal{E}-Q\ln l$, which does depend of the fine structure parameter $l$. The existence of large $n$ asymptotic expansions of $\mathcal{F}$ beyond the planar limit as well as the double-scaling limit are also discussed

From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing

In recent studies [1][13][12] Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, data compression is also based on predictions. What the problem comes down to is whether a data compressor could be used to perform as well as recurrent neural networks in natural language processing tasks. If this is possible,then the problem comes down to determining if a compression algorithm is even more intelligent than a neural network in specific tasks related to human language. In our journey we discovered what we think is the fundamental difference between a Data Compression Algorithm and a Recurrent Neural Network