نتایج جستجو برای: 2008 modern time series econometric analysis methods

تعداد نتایج: 5637901  

Journal: :Technometrics 2001
Michael R. Chernick

• wavelets are analysis tools for time series and images • as a subject, wavelets are − relatively new (1983 to present) − a synthesis of old/new ideas − keyword in 29, 826+ articles and books since 1989 (4032 more since 2005: an inundation of material!!!) • broadly speaking, there have been two waves of wavelets − continuous wavelet transform (1983 and on) − discrete wavelet transform (1988 an...

1989
William W. S. Wei

Spend your time even for only few minutes to read a book. Reading a book will never reduce and waste your time to be useless. Reading, for some people become a need that is to do every day such as spending time for eating. Now, what about you? Do you like to read a book? Now, we will show you a new book enPDFd time series analysis univariate and multivariate methods that can be a new way to exp...

Journal: :Technometrics 2005
Melvin J. Hinich

The purpose of this document is to summarize Part I of the book “Time Series Analysis by State Space Methods ”, written by James Durbin and Siem Jan Koopman.

2010

The methods which are to be discussed in this review are designed for the purpose of analysing series of statistical observations taken at regular intervals in time. The methods have a wide range of applications. We can cite astronomy [18], meteorology [9], seismology [21], oceanography [10], [12], communications engineering and signal processing [17], the control of continuous process plants [...

2001
Giovanni Urga

This review offers a guided tour to PcGive 10 modules for econometrics analysis of time series (PcGive), limited dependent variable (LogitJD) and static and dynamic panel data analyses (DPD), financial econometric (GARCH) and time series (ARFIMA) modelling. Several empirical applications are reported to illustrate the package.

2016
Siddharth Patel Sam Borgeson Ram Rajagopal Anna Spurlock Ling Jin Annika Todd

The recent nationwide adoption of smart meters provides a new source of rich data about individual household electricity consumption. Data science techniques can extract a variety of high temporal resolution, household-specific features from the hourly electricity time series itself and in combination with other readily available relevant information, like weather or census data. This allows us...

Journal: :Information Technology and Management Science 2012

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