scholarly journals Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction

2018 ◽  
Author(s):  
Mengheng Li ◽  
Siem Jan Koopman
Keyword(s):  
Stochastic Volatility ◽  
Signal Extraction ◽  
Unobserved Components

scholarly journals Seasonal adjustment of the Spanish sales daily data

SERIEs ◽  
2021 ◽  
Author(s):  
Ángel Cuevas ◽  
Ramiro Ledo ◽  
Enrique M. Quilis
Keyword(s):  
Value Added ◽  
Signal Extraction ◽  
Economic Conditions ◽  
Seasonal Adjustment ◽  
Unobserved Components ◽  
Spanish Economy ◽  
Short Term ◽  
Daily Data ◽  
Daily Frequency ◽  
Unobserved Components Model

AbstractWe present a procedure to perform seasonal adjustment over daily sales data. The model adjusts daily information from the Immediate Supply of Information System for Value Added Tax declaration forms compiled by the Spanish Tax Agency. The procedure performs signal extraction and forecasting at the daily frequency, by means of an unobserved components model. The daily information allows a permanently updated monitoring of the short-term economic conditions of the Spanish economy.

scholarly journals Signal extraction and the formulation of unobserved components models

2000 ◽  
Vol 3 (1) ◽  
pp. 84-107 ◽  
Author(s):  
Andrew Harvey ◽  
Siem Jan Koopman
Keyword(s):  
Signal Extraction ◽  
Unobserved Components

SIGNAL EXTRACTION IN LONG MEMORY STOCHASTIC VOLATILITY

2014 ◽  
Vol 31 (6) ◽  
pp. 1382-1402 ◽  
Author(s):  
Josu Arteche
Keyword(s):  
Stochastic Volatility ◽  
Long Memory ◽  
Financial Time Series ◽  
Signal Extraction ◽  
Typical Characteristic ◽  
Nonstationary Signals ◽  
Financial Time ◽  
Daily Series ◽  
Parametric Specification ◽  
A New Technique

Long memory in stochastic volatility (LMSV) models are flexible tools for the modeling of persistent dynamic volatility, which is a typical characteristic of financial time series. However, their empirical applicability is limited because of the complications inherent in the estimation of the model and in the extraction of the volatility component. This paper proposes a new technique for volatility extraction, based on a semiparametric version of the optimal Wiener–Kolmogorov filter in the frequency domain. Its main characteristics are its simplicity and generality, because no parametric specification is needed for the volatility component and it remains valid for both stationary and nonstationary signals. The applicability of the proposal is shown in a Monte Carlo and in a daily series of returns from the Dow Jones Industrial index.

A new method of fluxgate signal extraction

2017 ◽  
Vol 2017 (45) ◽  
pp. 83-89
Author(s):  
A.A. Marusenkov ◽  
Keyword(s):  
High Frequency ◽  
Second Harmonic ◽  
Excitation Frequency ◽  
Low Noise ◽  
Measuring System ◽  
Signal Extraction ◽  
Fluxgate Magnetometer ◽  
New Approach ◽  
Average Value ◽  
The Time Domain

Using dedicated high-frequency measuring system the distribution of the Barkhausen jumps intensity along a reversal magnetization cycle was investigated for low noise fluxgate sensors of various core shapes. It is shown that Barkhausen (reversal magnetization) noise intensity is strongly inhomogeneous during an excitation cycle. In the traditional second harmonic fluxgate magnetometers the signals are extracted in the frequency domain, as a result, some average value of reversal magnetization noises is contributed to the output signals. In order to fit better the noise shape and minimize its transfer to the magnetometer output the new approach for demodulating signals of these sensors is proposed. The new demodulating method is based on information extraction in the time domain taking into account the statistical properties of cyclic reversal magnetization noises. This approach yields considerable reduction of the fluxgate magnetometer noise in comparison with demodulation of the signal filtered at the second harmonic of the excitation frequency.

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