12,123 research outputs found

    Short-term estimates of euro area real GDP by means of monthly data

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    The first official data releases of quarterly real GDP for the euro area are published about eight weeks after the end of the reference quarters. Meanwhile, ongoing economic developments must be assessed from various, more readily available, monthly indicators. We examine in the context of univariate forecasting equations to what extent monthly indicators provide useful information for predicting euro area real GDP growth over the current and the next quarter. In particular, we investigate the performance of the equations under the case that the monthly indicators are only partially available within the quarter. For this purpose, we use time series models to forecast the missing observations of monthly indicators. We then examine GDP forecasts under different amounts of monthly information. We find that already a limited amount of monthly information improves the predictions for current-quarter GDP growth to a considerable extent, compared with ARIMA forecasts. JEL Classification: C22, C53bridge equations, Conjunctural analysis, incomplete monthly information

    VAR estimation and forecasting when data are subject to revision

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    Conventional VAR estimation and forecasting ignores the fact that economic data are often subject to revision many months or years after their initial release. This paper shows how VAR analysis can be modified to account for such revisions. The proposed approach assumes that government statistical releases are efficient with a finite lag. It takes no stand on whether earlier revisions are “noise” or “news.” The technique is illustrated using data on employment and the unemployment rate, real GDP and the unemployment rate, and real GDP and the GDP/consumption ratio. In each case, the proposed procedure outperforms conventional VAR analysis and the more-restrictive methods for handling the data-revision problem that are found in the existing literature.

    Real-time representations of the output gap

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    Methods are described for the appropriate use of data obtained and analysed in real time to represent the output gap. The methods employ cointegrating VAR techniques to model real-time measures and realizations of output series jointly. The model is used to mitigate the impact of data revisions; to generate appropriate forecasts that can deliver economically meaningful output trends and that can take into account the end-of-sample problems encountered in measuring these trends; and to calculate probability forecasts that convey in a clear way the uncertainties associated with the gap measures. The methods are applied to data for the United States 1965q4–2004q4, and the improvements over standard methods are illustrated

    The New York Stock Market in the 1920s and 1930s: Did Stock Prices Move Together Too Much?

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    In this paper, we re-examine the stock market of the 1920s and 1930s for evidence of a bubble, a 'fad' or 'herding' behavior by studying individual stock returns. One story often advanced for the boom of 1928 and 1929 is that it was driven by the entry into the market of largely uninformed investors, who followed the fortunes of and invested in 'favorite' stocks. The recent theoretical literature on how 'noise traders' perturb financial markets is consistent with this description. The result of this behavior would be a tendency for the favorite stocks' prices to move together more than would be predicted by their shared fundamentals. Our results suggest that there was excess comovement in returns even before the boom began, but comovement increased significantly during the boom and was a signal characteristic of the tumultuous market of the early 1930s. These results are thus consistent with the possibility that a fad or crowd psychology played a role in the rise of the market, its crash and subsequent volatility.

    Comparison of NASTRAN analysis with ground vibration results of UH-60A NASA/AEFA test configuration

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    Preceding program flight tests, a ground vibration test and modal test analysis of a UH-60A Black Hawk helicopter was conducted by Sikorsky Aircraft to complement the UH-60A test plan and NASA/ARMY Modern Technology Rotor Airloads Program. The 'NASA/AEFA' shake test configuration was tested for modal frequencies and shapes and compared with its NASTRAN finite element model counterpart to give correlative results. Based upon previous findings, significant differences in modal data existed and were attributed to assumptions regarding the influence of secondary structure contributions in the preliminary NASTRAN modeling. An analysis of an updated finite element model including several secondary structural additions has confirmed that the inclusion of specific secondary components produces a significant effect on modal frequency and free-response shapes and improves correlations at lower frequencies with shake test data

    Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs

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    Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.Comment: 13 figures, 35 reference

    A New Method for Combining Detrending Techniques with Application to Business Cycle Synchronization of the New EU Members

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    Decomposing output into trend and cyclical components is an uncertain exercise and depends on the method applied. It is an especially dubious task for countries undergoing large structural changes, such as transition countries. Despite their deficiencies, however, univariate detrending methods are frequently adopted for both policy oriented and academic research. This paper proposes a new procedure for combining univariate detrending techniques which is based on revisions of the estimated output gaps adjusted by the variance of and the correlation among output gaps. The procedure is applied to the study of the similarity of business cycles between the euro area and new EU Member States.combination, detrending, new EU members, OCA, output gap, revision

    Investment-specific technology growth: concepts and recent estimates

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    The strength of U.S. productivity growth in recent years has been attributed to technological improvements that are, in some sense, embodied in new types of capital equipment. However, traditional growth theory and growth accounting techniques—which emphasize the role of disembodied, neutral technological progress—are deficient in explaining this phenomenon. In this article, Michael R. Pakko outlines a model of investment-specific technological change that has become popular for describing the notion of capital-embodied growth and summarizes some recent estimates of the importance of this type of technological progress for assessing U.S. productivity trends.Technology ; Productivity

    Generalised Measures of Useful Directed Divergence and Information Improvement with Applications

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    The present communication describes a new generalised measure of useful directed divergence based on m-l probability distributions, and a probability distribution closest to these probability distributions has been proposed. The technique has been applied in solving problems related to crops production, export, and industries. Further, a generalised measure of useful information improvement has been developed and its applications in the assessment of balanced military requirements for a country, in ranking and pattern recognition, have been discussed
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