Learning, capital-embodied technology and aggregate fluctuations

Business cycles in the U.S. and G-7 economies are asymmetric: recoveries and expansions tend to be long and gradual and busts tend to be short and sharp. Moreover, this type of asymmetry appears more pronounced in the last two cyclical episodes in the G-7. A large body of work views the last two cyclical U.S. episodes, namely, the``new economy" boom in the late 1990s, and the 2000s housing boom-bust as episodes where over-optimistic beliefs have played a significant role. These episodes have revived interest in expectations driven business cycles models. However, previous work in this area has not addressed the important asymmetry feature of business cycles. This paper takes a step towards addressing this limitation of expectations driven business cycle models. We propose a generalization of the Greenwood et al. (1988) model with vintage capital and learning about capital embodied productivity and show it can deliver fluctuations that are asymmetric as in the U.S. data. Learning, calibrated to match the procyclical forecast precision from the Survey of Professional Forecasters, is crucial for the model's ability to generate asymmetries. Forecast errors generated by the model are shown to: (a) amplify fluctuations, and (b) trigger recessions that mimic in magnitude, duration and depth the typical post WW II U.S. recession.News shocks, expectations, growth asymmetry, Bayesian learning, business cycles

Experiencias sobre automación de hornos rotatorios con intercambiadores de calor en suspensión gaseosa

Not availableLos hornos con intercambiadores de calor en suspensión gaseosa reúnen, respecto a la automación, unas condiciones previas como el precalentamiento y la descarbonatación de la materia prima, las cuales se realizan en un aparato que no permite mando ni regulación algunos. El intercambiador de calor en suspensión gaseosa no ofrece ninguna posibilidad de intervención sobre el material que pasa por él

Disnatremias na admissão hospitalar : prevalência, características clínicas e morbimortalidade: um estudo retrospectivo e multicêntrico em hospitais brasileiros

Orientador: Prof. Dr. Mauricio de CarvalhoCoorientador: Prof. Me. Hugo Manuel Paz MoralesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências da Saúde, Programa de Pós-Graduação em Medicina Interna e Ciências da Saúde. Defesa : Curitiba, 03/09/2021Inclui referências: p. 61-65Resumo: Introdução: Disnatremias são distúrbios eletrolíticos comuns em pacientes hospitalizados e têm sido associadas a piores desfechos clínicos. No entanto, o surgimento das disnatremias em relação à hospitalização é escassamente descrito e poucos estudos abordam a prevalência de disnatremias na admissão hospitalar. Neste estudo, objetivou-se descrever a prevalência de disnatremias adquiridas na comunidade e explorar suas associações com características e desfechos clínicos, incluindo sinais vitais, em uma coorte retrospectiva multicêntrica. Métodos: Os dados de registros eletrônicos de saúde de pacientes hospitalizados no Brasil foram fornecidos por Laura® (Curitiba, PR, Brasil). Foram incluídos pacientes com sódio sérico disponível no período de 24h a partir da admissão hospitalar. A prevalência de disnatremias foi definida como sódio sérico 145 mEq/mL (hipernatremia). As associações entre sinais vitais nas primeiras 24h da admissão hospitalar e disnatremias foram exploradas em modelos logísticos. Desfechos de sobrevivência foram descritos em curvas de Kaplan-Meier. Resultados: De janeiro de 2019 a dezembro de 2020, 19.606 pacientes foram selecionados e 3.933 (20,1%) foram admitidos com disnatremias. A prevalência de hipo e hipernatremia foi de 17,2 e 2,9%, respectivamente. Pacientes com disnatremias eram mais idosos, com menor taxa de filtração glomerular, e níveis mais elevados de ureia. A permanência hospitalar, a taxa de transferência para a UTI e a mortalidade foram maiores no grupo com disnatremias (P145 mEq/mL (hypernatremia). The associations between vital signs in the first 24h of hospital admission and dysnatremias were explored in logistic models. Survival outcomes were described in Kaplan-Meier curves. Results: From January 2019 to December 2020, 19,606 patients were selected and 3,933 (20.1%) were admitted with dysnatremias. Prevalence of hypo and hypernatremia was 17.2 and 2.9%, respectively. Patients with dysnatremias were older, had worse glomerular filtration rate, and increased urea levels. Hospital length of stay, ICU transfer rate, and mortality were higher in the group with dysnatremias (P<0.0001). Mean heart rate and temperature in the first 24h of admission were associated with hyponatremia, while mean heart rate and respiratory rate were associated with hypernatremia. Conclusion: We demonstrate that dysnatremias are common at hospital admission and are associated with unfavorable clinical outcomes. They are also associated with abnormal vital signs in the first 24h of hospitalization. These results suggest that dysnatremias may be potential predictors of early clinical deterioration at hospital admission

News and financial intermediation in aggregate fluctuations

An important disconnect in the news view of fluctuations is the lack of consistent evidence suggestive of significant macroeconomic effects of news shocks. Findings from estimated DSGE models that, in theory, allow news shocks to matter quantitatively, suggest they do not. This disconnect can be resolved once we augment a DSGE model with a financial channel that provides amplification to news shocks. Our results suggest news shocks to the future growth prospects of the economy to be significant drivers of U.S. fluctuations, explaining as much as 50% and 37% of the variance in hours worked and output respectively, in cyclical frequencies

Learning, capital-embodied technology and aggregate fluctuations

Business cycles in the U.S. and G-7 economies are asymmetric: recoveries and expansions tend to be long and gradual and busts tend to be short and sharp. Moreover, this type of asymmetry appears more pronounced in the last two cyclical episodes in the G-7. A large body of work views the last two cyclical U.S. episodes, namely, the``new economy" boom in the late 1990s, and the 2000s housing boom-bust as episodes where over-optimistic beliefs have played a significant role. These episodes have revived interest in expectations driven business cycles models. However, previous work in this area has not addressed the important asymmetry feature of business cycles. This paper takes a step towards addressing this limitation of expectations driven business cycle models. We propose a generalization of the Greenwood et al. (1988) model with vintage capital and learning about capital embodied productivity and show it can deliver fluctuations that are asymmetric as in the U.S. data. Learning, calibrated to match the procyclical forecast precision from the Survey of Professional Forecasters, is crucial for the model's ability to generate asymmetries. Forecast errors generated by the model are shown to: (a) amplify fluctuations, and (b) trigger recessions that mimic in magnitude, duration and depth the typical post WW II U.S. recession

The Tree Inclusion Problem: In Linear Space and Faster

Given two rooted, ordered, and labeled trees $P$ and $T$ the tree inclusion problem is to determine if $P$ can be obtained from $T$ by deleting nodes in $T$. This problem has recently been recognized as an important query primitive in XML databases. Kilpel\"ainen and Mannila [\emph{SIAM J. Comput. 1995}] presented the first polynomial time algorithm using quadratic time and space. Since then several improved results have been obtained for special cases when $P$ and $T$ have a small number of leaves or small depth. However, in the worst case these algorithms still use quadratic time and space. Let $n_S$, $l_S$, and $d_S$ denote the number of nodes, the number of leaves, and the %maximum depth of a tree $S \in \{P, T\}$. In this paper we show that the tree inclusion problem can be solved in space $O(n_T)$ and time: O(\min(l_Pn_T, l_Pl_T\log \log n_T + n_T, \frac{n_Pn_T}{\log n_T} + n_{T}\log n_{T})). This improves or matches the best known time complexities while using only linear space instead of quadratic. This is particularly important in practical applications, such as XML databases, where the space is likely to be a bottleneck.Comment: Minor updates from last tim

The anatomy of small open economy productivity trends

We estimate a novel empirical (state-space) model to study the effects of international and domes- tic technology trend shocks on the UK economy. We jointly identify anticipated and unanticipated domestic and international technological innovations arising from changes in total factor productivity (TFP) and investment specific technology (IST). The long-run restrictions used to jointly identify the structural trends in the data are informed by a standard two-country structural model. Our results point to large and persistent swings in productivity. International non-stationary TFP and IST shocks explain about 30% and 24% of the variance of UK GDP, respectively. UK-specific TFP and IST shocks are somewhat less important, but still a relevant factor. Notably, it is the anticipated components of these international and domestic productivity shocks, rather than their unanticipated counterparts, which account for the bulk of the volatility in the data. We dissect the historical role of different shocks as drivers of UK labor productivity growth. We find that a decline in the contribution of international IST shocks, combined with weak domestic TFP growth, can explain the widely documented slowdown in UK labor productivity after the financial crisis. A standard two-country model implies widely-used restrictions on the relative price of investment which we find to be inconsistent with our empirical evidence that relies on a minimum of structure. We show that a two-sector version of this model with adjustment cost in investment and costly sectoral labor reallocation can capture the empirical dynamics