计量经济分析 = Econometric analysis

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Nuclear Instruments and Methods in Physics Research. Section B; Microstructural Characterization of Semi-Interpenetrating Polymer Networks by Positron Lifetime Spectroscopy

Following the publication of the Task Force document on heart rate variability (HRV) in 1996, a number of articles have been published to describe new HRV methodologies and their application in different physiological and clinical studies. This document presents a critical review of the new methods. A particular attention has been paid to methodologies that have not been reported in the 1996 standardization document but have been more recently tested in sufficiently sized populations. The following methods were considered: Long-range correlation and fractal analysis; Short-term complexity; Entropy and regularity; and Nonlinear dynamical systems and chaotic behaviour. For each of these methods, technical aspects, clinical achievements, and suggestions for clinical application were reviewed. While the novel approaches have contributed in the technical understanding of the signal character of HRV, their success in developing new clinical tools, such as those for the identification of high-risk patients, has been rather limited. Available results obtained in selected populations of patients by specialized laboratories are nevertheless of interest but new prospective studies are needed. The investigation of new parameters, descriptive of the complex regulation mechanisms of heart rate, has to be encouraged because not all information in the HRV signal is captured by traditional methods. The new technologies thus could provide after proper validation, additional physiological, and clinical meaning. Multidisciplinary dialogue and specialized courses in the combination of clinical cardiology and complex signal processing methods seem warranted for further advances in studies of cardiac oscillations and in the understanding normal and abnormal cardiac control processes.

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Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society

MR. KEYNES'S “Treatise on Money”, the fruit of his study and reflection up to date on the part played by currency and credit in contemporary civilisation, is the most important work on monetary theory published since the War, certainly in England and probably throughout the world. Its two volumes, entitled respectively “The Pure Theory of Money” and “The Applied Theory of Money”, are divided into seven books. The first book is concerned with essential definitions; the second investigates what the value of money really means, and how its fluctuations may best be measured; the third and fourth books—which contain the heart of Mr. Keynes's theory—deal with the factors which determine the value of money and the dynamics of changing price-levels; the fifth and sixth are taken up with a study of the statistical and non-statistical data bearing on changes in the quantity of money and its velocity of circulation, in the volume of production and trade, and in the rate of investment; while the seventh and last book contains Mr. Keynes's proposals for the practical reforms which he would like to see the ultimate monetary authorities of the world adopt in order to achieve the objectives at which, in his opinion, monetary policy should aim. A Treatise, on Money. John Maynard Keynes. Vol. I: The Pure Theory of Money. Pp. xvii + 363. Vol. 2: The Applied Theory of Money. Pp. viii + 424. (London: Macmillan and Co., Ltd., 1930.) 15s. net each vol.

A Treatise, on Money

jennifer.brown@cantebury.ac.nz:We present the results of a simulation study into the properties of 12diﬀerent estimators of the Hurst parameter, H, or the fractional integra-tion parameter, d, in long memory time series. We compare and contrasttheir performance on simulated Fractional Gaussian Noises and fractionallyintegrated series with lengths between 100 and 10,000 data points and Hvalues between 0.55 and 0.90 or d values between 0.05 and 0.40. We applyall 12 estimators to the Campito Mountain data and estimate the accuracyof their estimates using the Beran goodness of ﬁt test for long memory timeseries.MCS code: 37M10Keywords and phrases: Strong dependence, Global dependence, Longrange dependence, Hurst parameter estimators.

Estimators for Long Range Dependence: An Empirical Study

A flexible extreme value mixture model

Increased regulation of chemical pesticides and rapid evolution of pesticide resistance have increased calls for sustainable pest management. Biological control offers sustainable pest suppression, partly because evolution of resistance to predators and parasitoids is prevented by several factors (e.g., spatial or temporal refuges from attacks, reciprocal evolution by control agents, and contrasting selection pressures from other enemy species). However, evolution of resistance may become more probable as agricultural intensification reduces the availability of refuges and diversity of enemy species, or if control agents have genetic barriers to evolution. Here we use 21 y of field data from 196 sites across New Zealand to show that parasitism of a key pasture pest (Listronotus bonariensis; Argentine stem weevil) by an introduced parasitoid (Microctonus hyperodae) was initially nationally successful but then declined by 44% (leading to pasture damage of c. 160 million New Zealand dollars per annum). This decline was not attributable to parasitoid numbers released, elevation, or local climatic variables at sample locations. Rather, in all locations the decline began 7 y (14 host generations) following parasitoid introduction, despite releases being staggered across locations in different years. Finally, we demonstrate experimentally that declining parasitism rates occurred in ryegrass Lolium perenne, which is grown nationwide in high-intensity was significantly less than in adjacent plots of a less-common pasture grass (Lolium multiflorum), indicating that resistance to parasitism is host plant-dependent. We conclude that low plant and enemy biodiversity in intensive large-scale agriculture may facilitate the evolution of host resistance by pests and threaten the long-term viability of biological control.

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Intensified agriculture favors evolved resistance to biological control

Lead-cooled system design and challenges in the frame of Generation IV International Forum

In canonical vector time series autoregressions, which permit dependence only on past values, the errors generally show contemporaneous correlation. By contrast structural vector autoregressions allow contemporaneous series dependence and assume errors with no contemporaneous correlation. Such models having a recursive structure can be described by a directed acyclic graph. We show, with the use of a real example, how the identification of these models may be assisted by examination of the conditional independence graph of contemporaneous and lagged variables. In this example we identify the causal dependence of monthly Italian bank loan interest rates on government bond and repurchase agreement rates. When the number of series is larger, the structural modelling of the canonical errors alone is a useful initial step, and we first present such an example to demonstrate the general approach to identifying a directed graphical model.

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Marco Reale

Papers

Estimators for Long Range Dependence: An Empirical Study

A flexible extreme value mixture model

Intensified agriculture favors evolved resistance to biological control

Lead-cooled system design and challenges in the frame of Generation IV International Forum

Identification of vector AR models with recursive structural errors using conditional independence graphs