Fiscal rules, inertia and discretionary fiscal policy

In current practice, changes in the cyclically-adjusted budget balance (CAB) are generally interpreted as reflecting the effort of discretionary fiscal policy. This paper shows that such an interpretation is not a sufficiently accurate description of the behaviour of fiscal policy, and, in some cases, it may even conceal an important deficit bias. Specifically, as growth projections are an important building block of budgetary plans, optimism in forecasting growth, coupled with pervasive lags and inertia in the implementation phase of the budget, will result in a fiscal expansion, even in the absence of discretionary measures.fiscal policy, cyclically-adjusted budget balance, potential output, forecast accuracy, policy inaction, Larch, Salto

Better Word Embeddings by Disentangling Contextual n-Gram Information

Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings. We claim that training word embeddings along with higher n-gram embeddings helps in the removal of the contextual information from the unigrams, resulting in better stand-alone word embeddings. We empirically show the validity of our hypothesis by outperforming other competing word representation models by a significant margin on a wide variety of tasks. We make our models publicly available.Comment: NAACL 201

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed representations of sentences. Our method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings.Comment: NAACL 201

The Lyman-alpha forest and WMAP year three

A combined analysis of Cosmic Microwave Background (CMB) and Lyman-a forest data allows to constrain the matter power spectrum from small scales of about 1 Mpc/h all the way to the horizon scale. The long lever arm and complementarity provided by such an analysis has previously led to a significant tightening of the constraints on the shape and the amplitude of the power spectrum of primordial density fluctuations. We present here a combined analysis of the WMAP three year results with Lyman-a forest data. The amplitude of the matter power spectrum sigma_8 and the spectral index ns inferred from the joint analysis with high resolution Lyman-a forest data and low resolution Lyman-a forest data as analyzed by Viel & Haehnelt (2006) are consistent with the new WMAP results to within 1 sigma. The joint analysis with the mainly low resolution data as analysed by McDonald et al. (2005) suggests a value of sigma_8 which is ~ 2 sigma higher than that inferred from the WMAP three year data alone. The joint analysis of the three year WMAP and the Lyman-a forest data also does not favour a running of the spectral index. The best fit values for a combined analysis of the three year WMAP data, other CMB data, 2dF and the Lyman-a forest data are (sigma_8, ns) = (0.78\pm 0.03,0.96 \pm 0.01).Comment: 5 pages, 4 figs, 2 tables. MNRAS letters in pres

Inverse Statistical Physics of Protein Sequences: A Key Issues Review

In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e.~evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.Comment: 18 pages, 7 figure