A monthly indicator of employment in the euro area: real time analysis of indirect estimates

The paper presents the results of an extensive real time analysis of alternative model-based approaches to derive a monthly indicator of employment for the euro area. In the experiment the Eurostat quarterly national accounts series of employment is temporally disaggregated using the information coming from the monthly series of unemployment. The strategy benefits of the contribution of the information set of the euro area and its 6 larger member states, as well as the split into the 6 sections of economic activity. The models under comparison include univariate regressions of the Chow and Lin' type where the euro area aggregate is directly and indirectly derived, as well as multivariate structural time series models of small and medium size. The specification in logarithms is also systematically assessed. The largest multivariate setups, up to 49 series, are estimated through the EM algorithm. Main conclusions are the following: mean revision errors of disaggregated estimates of employment are overall small; a gain is obtained when the model strategy takes into account the information by both sector and member state; the largest multivariate setups outperforms those of small size and the strategies based on classical disaggregation methods.temporal disaggregation methods, multivariate structural time series models, mixed-frequency models, EM algorithm, Kalman filter and smoother

Spinoza on Freedom, Feeling Free, and Acting for the Good

In the Ethics, Spinoza famously rejects freedom of the will. He also offers an error theory for why many believe, falsely, that the will is free. Standard accounts of his arguments for these claims focus on their efficacy against incompatibilist views of free will. For Spinoza, the will cannot be free since it is determined by an infinite chain of external causes. And the pervasive belief in free will arises from a structural limitation of our self-knowledge: because we are aware of our actions but unaware of their causes, we suppose that we alone must be responsible for them. Yet I argue that the standard accounts miss a further element of Spinoza’s arguments that also targets compatibilist views on which free will is consistent with a specific kind of determination—namely, self-determination in accordance with our value judgments. For Spinoza, we are misled not only in supposing that our actions lack external determination but also in thinking that they are determined by our representations of value. In fact, our actions are determined by our appetites, which are blind to our value judgments. And the pervasive belief that our actions are determined by such judgments arises from the projection of value onto the objects we seek. As he denies us free will, then, Spinoza also denies us a capacity central to agency—the capacity to determine our actions in accordance with our ideas of the good. This makes his arguments against free will more consequential, and more radical, than commonly assumed

A monthly indicator of employment in the euro area: real time analysis of indirect estimates

The paper presents the results of an extensive real time analysis of alternative model-based approaches to derive a monthly indicator of employment for the euro area. In the experiment the Eurostat quarterly national accounts series of employment is temporally disaggregated using the information coming from the monthly series of unemployment. The strategy benefits of the contribution of the information set of the euro area and its 6 larger member states, as well as the split into the 6 sections of economic activity. The models under comparison include univariate regressions of the Chow and Lin' type where the euro area aggregate is directly and indirectly derived, as well as multivariate structural time series models of small and medium size. The specification in logarithms is also systematically assessed. The largest multivariate setups, up to 49 series, are estimated through the EM algorithm. Main conclusions are the following: mean revision errors of disaggregated estimates of employment are overall small; a gain is obtained when the model strategy takes into account the information by both sector and member state; the largest multivariate setups outperforms those of small size and the strategies based on classical disaggregation methods

A monthly indicator of employment in the euro area: real time analysis of indirect estimates

The paper presents the results of an extensive real time analysis of alternative model-based approaches to derive a monthly indicator of employment for the euro area. In the experiment the Eurostat quarterly national accounts series of employment is temporally disaggregated using the information coming from the monthly series of unemployment. The strategy benefits of the contribution of the information set of the euro area and its 6 larger member states, as well as the split into the 6 sections of economic activity. The models under comparison include univariate regressions of the Chow and Lin' type where the euro area aggregate is directly and indirectly derived, as well as multivariate structural time series models of small and medium size. The specification in logarithms is also systematically assessed. The largest multivariate setups, up to 49 series, are estimated through the EM algorithm. Main conclusions are the following: mean revision errors of disaggregated estimates of employment are overall small; a gain is obtained when the model strategy takes into account the information by both sector and member state; the largest multivariate setups outperforms those of small size and the strategies based on classical disaggregation methods

Multitasking associative networks

We introduce a bipartite, diluted and frustrated, network as a sparse restricted Boltzman machine and we show its thermodynamical equivalence to an associative working memory able to retrieve multiple patterns in parallel without falling into spurious states typical of classical neural networks. We focus on systems processing in parallel a finite (up to logarithmic growth in the volume) amount of patterns, mirroring the low-level storage of standard Amit-Gutfreund-Sompolinsky theory. Results obtained trough statistical mechanics, signal-to-noise technique and Monte Carlo simulations are overall in perfect agreement and carry interesting biological insights. Indeed, these associative networks pave new perspectives in the understanding of multitasking features expressed by complex systems, e.g. neural and immune networks.Comment: to appear on Phys.Rev.Let

Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts

In this paper we discuss the most recent developments of temporal disaggregation techniques carried out at ISTAT. They concern the extension from static to dynamic autoregressive distributed lag ADL regressions and the change to a state-space framework for the statistical treatment of temporal disaggregation. Beyond the development of a unified procedure for both static and dynamic methods from one side and the treatment of the logarithmic transformation from the other, we provide short guidelines for model selection. From the empirical side we evaluate the new dynamic methods by implementing a large scale temporal disaggregation exercise using ISTAT annual value added data jointly with quarterly industrial production by branch of economic activity over the period 1995-2013. The main finding of this application is that ADL models either in levels and logarithms can reduce the errors due to extrapolating disaggregated data in last quarters before the annual benchmarks become available. When the attention moves to the correlations with the high-frequency indicators the ADL disaggregations are also generally in line with those produced by the static Chow-Lin variants, with problematic outcomes limited to few cases

Assessment of appetitive behavior in honey bee dance followers

Honey bees transfer different informational components of the discovered feeding source to their nestmates during the waggle dance. To decode the multicomponent information of this complex behavior, dance followers have to attend to the most relevant signal elements while filtering out less relevant ones. To achieve that, dance followers should present improved abilities to acquire information compared with those bees not engaged in this behavior. Through proboscis extension response assays, sensory and cognitive abilities were tested in follower and non-follower bees. Individuals were captured within the hive, immediately after following waggle runs or a bit further from the dancer. Both behavioral categories present low and similar spontaneous odor responses (SORs). However, followers exhibit differences in responsiveness to sucrose and odor discrimination: followers showed increased gustatory responsiveness and, after olfactory differential conditioning, better memory retention than non-followers. Thus, the abilities of the dance followers related to appetitive behavior would allow them to improve the acquisition of the dance surrounding information.Fil: Moauro, Mariel Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Balbuena, María Sol. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Farina, Walter Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentin

Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts

In this paper we discuss the most recent developments of temporal disaggregation techniques carried out at ISTAT. They concern the extension from static to dynamic autoregressive distributed lag ADL regressions and the change to a state-space framework for the statistical treatment of temporal disaggregation. Beyond the development of a unified procedure for both static and dynamic methods from one side and the treatment of the logarithmic transformation from the other, we provide short guidelines for model selection. From the empirical side we evaluate the new dynamic methods by implementing a large scale temporal disaggregation exercise using ISTAT annual value added data jointly with quarterly industrial production by branch of economic activity over the period 1995-2013. The main finding of this application is that ADL models either in levels and logarithms can reduce the errors due to extrapolating disaggregated data in last quarters before the annual benchmarks become available. When the attention moves to the correlations with the high-frequency indicators the ADL disaggregations are also generally in line with those produced by the static Chow-Lin variants, with problematic outcomes limited to few cases

Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts

In this paper we discuss the most recent developments of temporal disaggregation techniques carried out at ISTAT. They concern the extension from static to dynamic autoregressive distributed lag ADL regressions and the change to a state-space framework for the statistical treatment of temporal disaggregation. Beyond the development of a unified procedure for both static and dynamic methods from one side and the treatment of the logarithmic transformation from the other, we provide short guidelines for model selection. The inclusion in the regressions of stochastic trends has been also discussed. From the empirical side we evaluate the new dynamic methods by implementing a large scale temporal disaggregation exercise using ISTAT annual value added data jointly with quarterly industrial production by branch of economic activity over the period 1995-2013. The main finding of this application is that ADL models either in levels and logarithms can reduce the errors due to extrapolating disaggregated data in last quarters before the annual benchmarks become available. When the attention moves to the correlations with the high-frequency indicators the ADL disaggregations are also generally in line with those produced by the static Chow-Lin variants, with problematic outcomes limited to few cases

Necesidad de ecologizar la enseñanza de las Ciencias Naturales : una reflexión desde la Química

La enseñanza de las Ciencias Naturales en las últimas décadas ha enfocado el desarrollo de sus contenidos de manera extremadamente específica aislados de una realidad y con poca vinculación entre el presente y futuro de una sociedad, creando áreas de conocimiento netamente especializadas sin ninguna relación entre ellas, ocasionando el poco desarrollo del aprendizaje significativo en el alumnado. El presente trabajo tiene como propósito, reflexionar sobre la necesidad de ecologizar la enseñanza de las Ciencias Naturales, a partir de la revisión y análisis del enfoque interdisciplinar, para lo cual se revisaron principios teóricos que fundamentan la Química que puedan servir de base para el logro del mismo. Así pues, la presente investigación tiene gran pertinencia entre las nuevas concepciones de la Transformación curricular que demanda nuestra sociedad actual