The Impacts of High-Frequency US Uncertainty Shocks on China’s Investment and Bank Loans: Evidence From Mixed-Frequency VAR

2020 ◽  
Author(s):  
Meng Yan
Keyword(s):  
High Frequency ◽  
Bank Loans ◽  
Mixed Frequency ◽  
Uncertainty Shocks

scholarly journals 6.2. MACROECONOMIC AND MARKET FACTORS FOR PREDICTING REAL ESTATE PRICES IN THE EURO ZONE ON MIXED FREQUENCY DATA

2020 ◽  
Author(s):  
А.А. Виноградов
Keyword(s):  
Real Estate ◽  
Euro Area ◽  
High Frequency ◽  
Real Estate Market ◽  
Frequency Data ◽  
The Real Estate ◽  
Mixed Frequency ◽  
The Real Estate Market ◽  
Real Estate Prices ◽  
Mixed Frequency Data

В данной работе исследуется динамика цен на недвижимость в зоне евро. Особенностями рынка недвижимости в зоне евро является разнородность стран, высокие объемы ипотечного рынка. Недвижимость является относительно неликвидным активом, а оценки ее стоимости публикуются реже, чем другие показатели. Актуальность работы заключается в построении модели для цены на недвижимость в зоне евро, которая позволяет построить прогноз и справедливую оценку для динамики цены на недвижимость. Новизной данной статьи является использование модель для данных смешанной частоты (MIDAS), которая позволяет совмещать высокочастотные рыночные показатели и низкочастотные данные по цене недвижимости для прогнозирования цен на жилую и коммерческую недвижимость. Среди факторов, влияющих на рынок недвижимости, были выделены ставки, отражающие состояние денежно-кредитной политики Европейского центрального банка (ЕЦБ) и объем активов ЕЦБ, отражающий меры нестандартной денежно-кредитной политики. В результате на основе высокочастотных данных была построена модель для цен на недвижимость, которая дает более точный прогноз, чем линейная модель, основанная только на квартальном росте валового внутреннего продукта зоны евро. Полученная модель может быть использована как для принятия управленческих решений, исходя из прогноза динамики цен на недвижимость, так и оценки справедливой динамики цен на недвижимость в зоне евро на основе фундаментальных факторов. This paper examines the dynamics of real estate prices in the euro area. The features of the real estate market in the euro area is the heterogeneity of countries, high volumes of the mortgage market. Real estate is a relatively illiquid asset, and estimates of its value are published less frequently than other indicators. The relevance of the work is to build a model for real estate prices in the euro area, which allows one to build a forecast and a fair assessment for the price dynamics of real estate. The novelty of this article is the use of the mixed frequency data sampling model (MIDAS), which allows one to combine high-frequency market indicators and low-frequency data on the price of real estate, to predict the prices of residential and commercial real estate. Among the factors affecting the real estate market, the rates that reflect the state of the ECB's monetary policy and the volume of the ECB's assets reflecting the measures of a non-standard monetary policy were identified. As a result, based on high-frequency data, a model for real estate prices was built, which gives a more accurate forecast than a linear model based on only quarterly GDP growth in the euro area. The resulting model can be used both for making managerial decisions based on the forecast of real estate price dynamics, and for assessing the fair dynamics of real estate prices in the euro area based on fundamental factors.

scholarly journals MULTIVARIATE AR SYSTEMS AND MIXED FREQUENCY DATA: G-IDENTIFIABILITY AND ESTIMATION

2015 ◽  
Vol 32 (4) ◽  
pp. 793-826 ◽  
Author(s):  
Brian D.O. Anderson ◽  
Manfred Deistler ◽  
Elisabeth Felsenstein ◽  
Bernd Funovits ◽  
Lukas Koelbl ◽  
...  
Keyword(s):  
High Frequency ◽  
Time Series Data ◽  
Series Data ◽  
Frequency Data ◽  
Linear Transformations ◽  
Mixed Frequency ◽  
Second Moments ◽  
Flow Variables ◽  
Parameter Values ◽  
Mixed Frequency Data

This paper is concerned with the problem of identifiability of the parameters of a high frequency multivariate autoregressive model from mixed frequency time series data. We demonstrate identifiability for generic parameter values using the population second moments of the observations. In addition we display a constructive algorithm for the parameter values and establish the continuity of the mapping attaching the high frequency parameters to these population second moments. These structural results are obtained using two alternative tools viz. extended Yule Walker equations and blocking of the output process. The cases of stock and flow variables, as well as of general linear transformations of high frequency data, are treated. Finally, we briefly discuss how our constructive identifiability results can be used for parameter estimation based on the sample second moments.

scholarly journals Temporal changes of neocortical high-frequency oscillations in epilepsy

2013 ◽  
Vol 110 (5) ◽  
pp. 1167-1179 ◽  
Author(s):  
Allison Pearce ◽  
Drausin Wulsin ◽  
Justin A. Blanco ◽  
Abba Krieger ◽  
Brian Litt ◽  
...  
Keyword(s):  
High Frequency ◽  
Statistical Tests ◽  
Relative Rate ◽  
Temporal Analysis ◽  
Temporal Changes ◽  
Patient Specific ◽  
Mixed Frequency ◽  
Potential Biomarker ◽  
Before And After ◽  
Intracranial Electrodes

High-frequency (100–500 Hz) oscillations (HFOs) recorded from intracranial electrodes are a potential biomarker for epileptogenic brain. HFOs are commonly categorized as ripples (100–250 Hz) or fast ripples (250–500 Hz), and a third class of mixed frequency events has also been identified. We hypothesize that temporal changes in HFOs may identify periods of increased the likelihood of seizure onset. HFOs (86,151) from five patients with neocortical epilepsy implanted with hybrid (micro + macro) intracranial electrodes were detected using a previously validated automated algorithm run over all channels of each patient's entire recording. HFOs were characterized by extracting quantitative morphologic features and divided into four time epochs (interictal, preictal, ictal, and postictal) and three HFO clusters (ripples, fast ripples, and mixed events). We used supervised classification and nonparametric statistical tests to explore quantitative changes in HFO features before, during, and after seizures. We also analyzed temporal changes in the rates and proportions of events from each HFO cluster during these periods. We observed patient-specific changes in HFO morphology linked to fluctuation in the relative rates of ripples, fast ripples, and mixed frequency events. These changes in relative rate occurred in pre- and postictal periods up to thirty min before and after seizures. We also found evidence that the distribution of HFOs during these different time periods varied greatly between individual patients. These results suggest that temporal analysis of HFO features has potential for designing custom seizure prediction algorithms and for exploring the relationship between HFOs and seizure generation.

The Microstructure of Steatite (Protoenstatite)

1985 ◽  
Vol 43 ◽  
pp. 236-237
Author(s):  
W. E. Lee ◽  
A. H. Heuer
Keyword(s):  
High Frequency ◽  
Glass Ceramics ◽  
Magnesium Silicate ◽  
Beam Current ◽  
Glassy Matrix ◽  
Angle Of Incidence ◽  
X Ray ◽  
Mechanical And Electrical Properties ◽  
Argon Ions ◽  
High Temperature Polymorph

IntroductionTraditional steatite ceramics, made by firing (vitrifying) hydrous magnesium silicate, have long been used as insulators for high frequency applications due to their excellent mechanical and electrical properties. Early x-ray and optical analysis of steatites showed that they were composed largely of protoenstatite (MgSiO3) in a glassy matrix. Recent studies of enstatite-containing glass ceramics have revived interest in the polymorphism of enstatite. Three polymorphs exist, two with orthorhombic and one with monoclinic symmetry (ortho, proto and clino enstatite, respectively). Steatite ceramics are of particular interest a they contain the normally unstable high-temperature polymorph, protoenstatite.Experimental3mm diameter discs cut from steatite rods (∼10” long and 0.5” dia.) were ground, polished, dimpled, and ion-thinned to electron transparency using 6KV Argon ions at a beam current of 1 x 10-3 A and a 12° angle of incidence. The discs were coated with carbon prior to TEM examination to minimize charging effects.

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

Sign in / Sign up

Export Citation Format

Share Document