5:15 p.m.
Dear Gretl Team,
I'm not an expert in MIDAS and because of that I am not confident
translating the following lines:
1. Normalized beta (zero last lag)
2. spread (MIDAS)
Line 1: Could you explain me what is "zero last lag"?
Line 2: Could you give me a little more context about "spread (MIDAS)"?
Another line I get myself stuck is:
3. Decoration
Could you give me more context?
Best,
Henrique Andrade
5:27 p.m.
On Mon, 31 Oct 2016, Henrique Andrade wrote:
Dear Gretl Team,
I'm not an expert in MIDAS and because of that I am not confident
translating the following lines:
1. Normalized beta (zero last lag)
2. spread (MIDAS)
Line 1: Could you explain me what is "zero last lag"?
The beta distribution is one parsimonious way of obtaining
coefficients on p lags of a high-frequency regressor from either 2 or
3 hyper-parameters. In the 2-parameter case the coefficient on lag p
(the last lag) is forced to zero.
Line 2: Could you give me a little more context about "spread
(MIDAS)"?
This is one way of "compacting" a high frequency series on importation
to a lower-frequency dataset. We "spread" the original series onto m
series, where m is the ratio of the higher frequency to the lower.
This is useful if and only if you're planning to do some sort of MIDAS
analysis.
Another line I get myself stuck is:
3. Decoration
Could you give me more context?
This is just used in the documentation, when categorizing
matrix-related functions. The functions rownames() and colnames() are
shown under the heading "Decoration".
Allin
6:27 p.m.
Em 31 de outubro de 2016, Allin escreveu:
On Mon, 31 Oct 2016, Henrique Andrade wrote:
Dear Gretl Team,
>
> I'm not an expert in MIDAS and because of that I am not confident
> translating the following lines:
>
> 1. Normalized beta (zero last lag)
> 2. spread (MIDAS)
>
> Line 1: Could you explain me what is "zero last lag"?
>
The beta distribution is one parsimonious way of obtaining coefficients on
p lags of a high-frequency regressor from either 2 or 3 hyper-parameters.
In the 2-parameter case the coefficient on lag p (the last lag) is forced
to zero.
The expression "zero last lag" is equivalent to "set the coefficient of
the
last lag to be equal to zero" or "coefficient of the last lag = 0"? In an
anaolgous way... The expression "non-zero last lag" is equivalent to "do
not set the coefficient of the last lag to be equal to zero" or
"coefficient of the last lag != 0"?
Line 2: Could you give me a little more context about "spread
(MIDAS)"?
>
This is one way of "compacting" a high frequency series on importation to
a lower-frequency dataset. We "spread" the original series onto m series,
where m is the ratio of the higher frequency to the lower. This is useful
if and only if you're planning to do some sort of MIDAS analysis.
Ok, now I'm not lost anymore ;-)
Another line I get myself stuck is:
>
> 3. Decoration
>
> Could you give me more context?
>
This is just used in the documentation, when categorizing matrix-related
functions. The functions rownames() and colnames() are shown under the
heading "Decoration".
So "decoration" here is something related to "appearance" or
"look", right?
Best regards,
Henrique Andrade
7:37 p.m.
On Mon, 31 Oct 2016, Henrique Andrade wrote:
Em 31 de outubro de 2016, Allin escreveu:
On Mon, 31 Oct 2016, Henrique Andrade wrote:
>
> Dear Gretl Team,
>>
>> I'm not an expert in MIDAS and because of that I am not confident
>> translating the following lines:
>>
>> 1. Normalized beta (zero last lag)
>> 2. spread (MIDAS)
>>
>> Line 1: Could you explain me what is "zero last lag"?
>
> The beta distribution is one parsimonious way of obtaining coefficients on
> p lags of a high-frequency regressor from either 2 or 3 hyper-parameters.
> In the 2-parameter case the coefficient on lag p (the last lag) is forced
> to zero.
The expression "zero last lag" is equivalent to "set the coefficient of
the
last lag to be equal to zero" or "coefficient of the last lag = 0"? In an
anaolgous way... The expression "non-zero last lag" is equivalent to "do
not set the coefficient of the last lag to be equal to zero" or
"coefficient of the last lag != 0"?
I'd say your alternatives amount to the same thing in context: if you
choose the version labeled "zero last lag" you're selecting a
parameterization which implies mathematically that lag p has a zero
coefficient; and if you choose "non-zero last lag" you get a
parameterization that permits a non-zero coefficient on lag p (by
shifting the whole beta distribution up or down, if the data "want"
this).
> Another line I get myself stuck is:
>>
>> 3. Decoration
>>
>> Could you give me more context?
>
> This is just used in the documentation, when categorizing matrix-related
> functions. The functions rownames() and colnames() are shown under the
> heading "Decoration".
So "decoration" here is something related to "appearance" or
"look", right?
Yes, that's right.
Allin
2973
days inactive
2973
days old
3 comments
2 participants
participants (2)
-
Allin Cottrell -
Henrique Andrade