相关时间序列样本数据(Relevant Time series Sample)_算法理论_科研数据集

相关时间序列样本数据(Relevant Time series Sample)
数据介绍：
The Data Set is in File DataSet.txt
It includes 7 time series. The sample runs from the first quarter of 1967 to the second quarter of 2008 (latest vintage in this period).
关键词：
算法,统计,样本,时间序列, algorithm,statistic,sample,time series,
数据格式：
TEXT
数据详细介绍：
Relevant Time series Sample
Abstract:
The Data Set is in File DataSet.txt
It includes 7 time series. The sample runs from the first quarter of 1967 to the second quarter of 2008 (latest vintage in this period).
Data Set Information:
In addition to real US GDP, the following indicators were selected:
Industrial Production Index (IPI), monthly series - quarterly series is constructed as average of the three months of each quarter.
Available from the Board of Governors of the Federal Reserve system. Series ID: INDPRO
Capacity Utilization (Total Industry), monthly series - quarterly series is constructed as average of the three months of each quarter.
Available from the Board of Governors of the Federal Reserve system. Series ID: TCU
Non-Farm Output, quarterly series, seasonally adjusted.
Available from the U.S. Department of Labor: Bureau of Labor Statistics. Series ID:OUTNFB
Business Sector: Hours of All Persons, quarterly series, seasonally adjusted. Available from the U.S. Department of Labor: Bureau of Labor Statistics. Series ID:HOABS
Average weekly hours, monthly series, seasonally adjusted - quarterly series is constructed as average of the three months of each quarter.
Available from the U.S. Department of Labor: Bureau of Labor Statistics. Series ID:AWHNONAG
Housing Starts (Total), monthly series, seasonally adjusted - quarterly series is constructed as average of the three months of each quarter.
Available from the U.S. Department of Commerce. Series ID:HOUST
Mathematica functions and code used to produce the results in the paper are in the file Mathematica_Code.txt. Just need to run the functions and the remaining code with the data loaded and transformed as described above. Functions and code needed for the calculations in the paper are commented in this file. Please contact me for a notebook version of the code.
Function to calculate filtered series using the whole sample is:
BandpassMultivariate[series, indicators, lowerperiod, upperperiod, Mtruncate] returns a filtered series corresponding to the band [lowerperiod, upperperiod], assuming "series" is integrated of order 1, assuming p=t-1 and f=T-t, so uses information from the whole sample
Assuming data is loaded into Mathematica:
"series" is a list with the series of interest
"indicators" is a list of lists, each of these containing an indicator, e.g.
indicators={indicator1, indicator2}, where indicator1 and indicator2 are series with the same size as "series"
"Mtruncate" is the M in the estimation of the spectrum.
For the real-time calculations the function is the following:
BandpassMultivariateRealTime[series, indicators, lowerperiod, upperperiod, Mtruncate, past, forward] returns a filtered series corresponding to the band [lowerperiod, upperperiod], assuming "series" is integrated of order 1, assuming p=t-1 and f=T-t, so uses information from the whole sample
"series" is a list with the series of interest
"indicators" is a list of lists, each of these containing an indicator, e.g. indicators={indicator1, indicator2}, where indicator1 and indicator2 are series with the same size as "series"
"Mtruncate" is the M in the estimation of the spectrum.
"past" is the parameter p in the filter
"forward" is the parameter f in the filter
Source
João Valle e Azevedo
Economic Research
Banco de Portugal
Av. Almirante Reis
71, 6th Floor
1150-012 Lisbon
Portugal
Relevant Papers:
A multivariate band-pass filter for economic time series, by J. Valle e Azevedo, pages 1–30;
数据预览：
点此下载完整数据集。