500 research outputs found
A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates
No full textIn this paper, we focus on the different methods which have been proposed in the literature to date for dealing with mixed-frequency and ragged-edge datasets: bridge equations, mixed-data sampling (MIDAS), and mixed-frequency VAR (MF-VAR) models. We discuss their performances for nowcasting the quarterly growth rate of the Euro area GDP and its components, using a very large set of monthly indicators. We investigate the behaviors of single indicator models, forecast combinations and factor models, in a pseudo real-time framework. MIDAS with an AR component performs quite well, and outperforms MF-VAR at most horizons. Bridge equations perform well overall. Forecast pooling is superior to most of the single indicator models overall. Pooling information using factor models gives even better results. The best results are obtained for the components for which more economically related monthly indicators are available. Nowcasts of GDP components can then be combined to obtain nowcasts for the total GDP growth
Mixed‐frequency structural models : identification, estimation, and policy analysis
No full textThe mismatch between the timescale of DSGE (dynamic stochastic general equilibrium) models and the data used in their estimation translates into identification problems, estimation bias, and distortions in policy analysis. We propose an estimation strategy based on mixed-frequency data to alleviate these shortcomings. The virtues of our approach are explored for two monetary policy models
U-MIDAS: MIDAS regressions with unrestricted lag polynomial
No full textMixed data sampling (MIDAS) regressions allow us to estimate dynamic equations that explain a low frequency variables and their lags. When the difference in sampling frequencies between the regressand and the regressors is large, distributed lag functions are typically employed to model dynamics avoiding parameter proliferation. In macroeconomic applications, however, differences in sampling frequencies are ofetn small. In such a case, it might not be necessary to employ distributed lag functions. We discuss the pros and cons of unrestricted lag polynomials in MIDAS regressions. We derive unrestricted-MIDAS (U-MIDAS) regressions from linear high frequency models, duscuss identification issues and show that their parameters can be estimated by ordinary least squares. In Monte Carlo experiments, we compare U-MIDAS with MIDAS fuctional distributed lags estimated by non-linear least squares. We show that U-MIDAS performs better than MIDAS for small differences in smal frequencies. However, with large differing sampling frequencies, distributed lag functions outperfom unrestricted polynomials. The good perfomarce of U-MIDAS for small differences in frequency is confirmed in empirical applications on nowcasting and short-term forecasting euro area and US gross domestic product growth by using monthly indicators
Perioperative myocardial infarction in noncardiac surgery: the diagnostic and prognostic role of cardiac troponins
No full textDespite the number of technologies used, the diagnosis of perioperative myocardial infarction is still a challenge. Studies conducted in surgical series have demonstrated that cardiac troponins (cTns) have both a superior diagnostic sensitivity and specificity, compared with other traditional techniques, and an independent power to predict short- and long-term prognosis. Nevertheless, some points need to be clarified. They include the usefulness of cTns in patients with end-stage renal failure; the standardization of the cTns cut-off for the diagnosis of myocardial injury; the timing of postoperative blood samplings; the cost-effectiveness of a screening in asymptomatic patients; and the possible therapeutic strategies
On particular foliation associated with a polynominal family of noninvertible maps of the plane
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Food perception and categorization: From food/no-food to different types of food
No full textThe ability to categorize food and nonfood correctly and to distinguish between different foods is essential for our survival. Because of our omnivore nature and because of the food-rich environment in which we live, categorization processes involving food are particularly complex. The extent of the literature on this subject is an indication of our limited understanding of the mental processes underlying food perception, categorization, and choice. The ability to categorize food requires integration of multisensory information and semantic memory with varying contextual information and is modulated by numerous factors. On the one hand, food features (e.g., energy content, level of transformation) modulate our perceptual and categorization processes; on the other hand, categorization processes are also modulated by the perceiver's temporary states (e.g., internal states such as hunger) and more lasting characteristics (e.g., body mass index, gender). Thus, food categorization provides a very rich test-case for any model of categorization
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