Which time series analysis is appropriate for pooled time series data analysis?
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I have two data sets one is cross sectional census data with 10 years interval and another one is time series data (monthly) for several years. Now I want to perform statistical time series analysis on this both dataset in a single model. Is it possible?
data-mining time-series
asked Jun 15 '17 at 6:19
Md. Rayhanul IslamMd. Rayhanul Islam
144
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bumped to the homepage by Community♦ 2 days ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have two data sets one is cross sectional census data with 10 years interval and another one is time series data (monthly) for several years. Now I want to perform statistical time series analysis on this both dataset in a single model. Is it possible?
data-mining time-series
asked Jun 15 '17 at 6:19
Md. Rayhanul IslamMd. Rayhanul Islam
144
$endgroup$
bumped to the homepage by Community♦ 2 days ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
I have two data sets one is cross sectional census data with 10 years interval and another one is time series data (monthly) for several years. Now I want to perform statistical time series analysis on this both dataset in a single model. Is it possible?
data-mining time-series
asked Jun 15 '17 at 6:19
Md. Rayhanul IslamMd. Rayhanul Islam
144
$endgroup$
I have two data sets one is cross sectional census data with 10 years interval and another one is time series data (monthly) for several years. Now I want to perform statistical time series analysis on this both dataset in a single model. Is it possible?
data-mining time-series
data-mining time-series
asked Jun 15 '17 at 6:19
Md. Rayhanul IslamMd. Rayhanul Islam
144
asked Jun 15 '17 at 6:19
Md. Rayhanul IslamMd. Rayhanul Islam
144
asked Jun 15 '17 at 6:19
Md. Rayhanul IslamMd. Rayhanul Islam
144
asked Jun 15 '17 at 6:19
Md. Rayhanul IslamMd. Rayhanul Islam
144
asked Jun 15 '17 at 6:19
Md. Rayhanul IslamMd. Rayhanul Islam
144
144
bumped to the homepage by Community♦ 2 days ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 2 days ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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$begingroup$
It really depends on the outcome you want from your analysis.
The most straightforward approaches might be resampling:
Interpolate the 10-year census data to month granularity. The interpolation might be linear, polynomial, etc. This effectively imposes a strong assumption in the dynamics of the data. This may make sense for some variables but not for others. And you are probably introducing a lot of variance in the obtained conclusions.
Aggregate the monthly time series into 10-year granularity. This may not be very useful if you want to predict something with an horizon less than 10 years...
answered Jun 15 '17 at 6:48
ncasasncasas
3,6531130
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1 Answer
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$begingroup$
It really depends on the outcome you want from your analysis.
The most straightforward approaches might be resampling:
Interpolate the 10-year census data to month granularity. The interpolation might be linear, polynomial, etc. This effectively imposes a strong assumption in the dynamics of the data. This may make sense for some variables but not for others. And you are probably introducing a lot of variance in the obtained conclusions.
Aggregate the monthly time series into 10-year granularity. This may not be very useful if you want to predict something with an horizon less than 10 years...
answered Jun 15 '17 at 6:48
ncasasncasas
3,6531130
$endgroup$
add a comment |
$begingroup$
It really depends on the outcome you want from your analysis.
The most straightforward approaches might be resampling:
Interpolate the 10-year census data to month granularity. The interpolation might be linear, polynomial, etc. This effectively imposes a strong assumption in the dynamics of the data. This may make sense for some variables but not for others. And you are probably introducing a lot of variance in the obtained conclusions.
Aggregate the monthly time series into 10-year granularity. This may not be very useful if you want to predict something with an horizon less than 10 years...
answered Jun 15 '17 at 6:48
ncasasncasas
3,6531130
$endgroup$
add a comment |
$begingroup$
It really depends on the outcome you want from your analysis.
The most straightforward approaches might be resampling:
Interpolate the 10-year census data to month granularity. The interpolation might be linear, polynomial, etc. This effectively imposes a strong assumption in the dynamics of the data. This may make sense for some variables but not for others. And you are probably introducing a lot of variance in the obtained conclusions.
Aggregate the monthly time series into 10-year granularity. This may not be very useful if you want to predict something with an horizon less than 10 years...
answered Jun 15 '17 at 6:48
ncasasncasas
3,6531130
$endgroup$
It really depends on the outcome you want from your analysis.
The most straightforward approaches might be resampling:
Interpolate the 10-year census data to month granularity. The interpolation might be linear, polynomial, etc. This effectively imposes a strong assumption in the dynamics of the data. This may make sense for some variables but not for others. And you are probably introducing a lot of variance in the obtained conclusions.
Aggregate the monthly time series into 10-year granularity. This may not be very useful if you want to predict something with an horizon less than 10 years...
answered Jun 15 '17 at 6:48
ncasasncasas
3,6531130
answered Jun 15 '17 at 6:48
ncasasncasas
3,6531130
answered Jun 15 '17 at 6:48
ncasasncasas
3,6531130
answered Jun 15 '17 at 6:48
ncasasncasas
3,6531130
3,6531130
add a comment |
add a comment |
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