30-04-2021

Gretl



Here is an example on how to conduct time-series forecasting analysis using the open-source econometrics software Gretl. Unfortunately, there are some details in the manual on forecasting which need to be finished. This brief entry may help to make more clear what's currently implemented in Gretl natively. I will show the basic commands using a simple AR(1) time-series model.
1) Load data first

Jan 18, 2021 Gretl will access other available versions on demand, via the Internet. You can also find the manual files here. Avid port devices driver download. In addition the Gretl Command Reference and Gretl Function Reference are available in HTML format. Fujifilm driver download for windows 10. Getting help, reporting bugs. If you use gretl you may wish to join the gretl-users mailing list. This is a moderate-volume list where. Gretl commands 1.1 Introduction The commands defined below may be executed interactively in the command-line client program or in the console window of the GUI program. They may also be placed in a “script” or batch file for non-interactive execution. The following notational conventions are used below. Witchslayer Gretl (also known as Gretl and Gretl: Witch Hunter) is a 2012 American-Canadian dark fantasy television film written by Brook Durham and Angela Mancuso, and directed by Mario Philip Azzopardi. Microage college station driver download for windows 10.


I use some quarterly inflation series from the Stock/Watson textbook for which the dataset is already provided by gretl:
<hansl>
set verbose off
open sw_ch14.gdt -q
series x = ldiff(PUNEW) # construct quarterly CPI inflation
</hansl>Gretl

2) Obtain fitted values = 1-step ahead forecasts
<hansl>
ols x const x(-1) -q# Estimate the AR(1) by OLS
series yhat = $yhat# Grab the fitted value by the built-in accesor $yhat
fcast fit# Alternatively one can use
print x fit yhat -o# Compare the results
</hansl>
3) 1-step ahead static out-of-sample forecast without re-estimating the model
In this exercise, the training set consists of the first R observations to fit a model. We take the coefficient vector beta-hat as given, and fit the model to a test set consisting R+1 to T observations of actual realizations.
<hansl>
smpl ; 1989:4# set the training set
ols x 0 x(-1)# estimate the AR(1) model
matrix bhat = $coeff# our beta-hat
scalar fc1990q1 = bhat[1] + x[1989:4]*bhat[2]
scalar fc1990q2 = bhat[1] + x[1990:1]*bhat[2]
scalar fc1990q3 = bhat[1] + x[1990:2]*bhat[2]
print fc1990q1 fc1990q2 fc1990q3
# Compute the static forecasts for the observations
# 1990q1 and 1990q3 using gretl's 'fcast' command
fcast 1990:1 1990:3 --static
# We can also do this for all periods after 1999m12
fcast --static --out-of-sample
# Note that you don't have to specify a concrete date
# but could just move the period forecasted by
fcast ($t2+1) ($t2+3) --static
</hansl>
4) Dynamic out-of-sample forecast without re-estimating the model
In this case, we estimate the model using the first R observations as our training set, again grab the coefficient vector beta-hat, and fit the model to the following R+h observations. However, instead of using actual realizations of x, we use the forecasted values. Thus, one obtains for the h-step ahead forecast (e.g. h=2) a whole path of forecasts.
<hansl>
smpl ; 1989:4# set the training set
ols x 0 x(-1)# estimate the AR(1) model
matrix bhat = $coeff# grab the coefficient vector

# Do the dynamic forecast by hand first
scalar dfc1990q1 = bhat[1] + x[1989:4]*bhat[2]# 1st value is the actual outcome
scalar dfc1990q2 = bhat[1] + dfc1990q1*bhat[2]# now we use the forecasted value of the previous period
scalar dfc1990q3 = bhat[1] + dfc1990q2*bhat[2]
print dfc1990q1 dfc1990q2 dfc1990q3
# Compare using gretl
fcast 1990:1 1990:3 --dynamic
# Run the dynamic forecast for all remaining periods
fcast --dynamic --out-of-sample
</hansl>
5) Recursive k-step ahead out-of-sample forecast with re-estimating the model
In this exercise, we fit the model to the first R-k observations in order to compute a forecast for period R. Next, we extend the sample end to R-k+1 while keeping the sample beginning fixed. Based on these R-k+1 observations, the k-step ahead forecast for R+1 is computed. Continue until the procedure stops.
This type of recursive forecast can easily be done in gretl. It is important to note, that this procedure always applies the dynamic forecast scheme described above but the beta-hat is also re-estimated at each iteration:
<hansl>
smpl full
ols x 0 x(-1) -q
 fcast 1990:1 1990:2 1 --rolling# 1-step ahead rolling fc
# Compare fc for 1990:2 with
smpl full
smpl ; 1990:1
ols x 0 x(-1) -q
fcast ($t2+1) ($t2+1) --dynamic
# or equivalently
fcast ($t2) ($t2+1) --static

# Compute all possible sequences of k-step recursive forecasts
smpl full
ols x 0 x(-1) -q
fcast 1 fc1 --rolling# 1-step ahead
fcast 6 fc6 --rolling# 6-step ahead

# Plot the 1-step ahead forecast based on a recursive origin
gnuplot x fc1 fc6 --with-lines --time-series --output=display
To see the plot of the realization 'x' as well as the 1- and 6-step ahead out-of-sample forecasts using a recursive scheme, just download the FORECAST.PDF below.
Here is an example on how to conduct time-series forecasting analysis using the open-source econometrics software Gretl. Unfortunately, there are some details in the manual on forecasting which need to be finished. This brief entry may help to make more clear what's currently implemented in Gretl natively. I will show the basic commands using a simple AR(1) time-series model.
1) Load data first
I use some quarterly inflation series from the Stock/Watson textbook for which the dataset is already provided by gretl:
<hansl>
set verbose off
open sw_ch14.gdt -q
series x = ldiff(PUNEW) # construct quarterly CPI inflation
</hansl>

2) Obtain fitted values = 1-step ahead forecasts
<hansl>
ols x const x(-1) -q# Estimate the AR(1) by OLS
series yhat = $yhat# Grab the fitted value by the built-in accesor $yhat
fcast fit# Alternatively one can use
print x fit yhat -o# Compare the results
</hansl>
3) 1-step ahead static out-of-sample forecast without re-estimating the model
In this exercise, the training set consists of the first R observations to fit a model. We take the coefficient vector beta-hat as given, and fit the model to a test set consisting R+1 to T observations of actual realizations.
<hansl>
smpl ; 1989:4# set the training set
ols x 0 x(-1)# estimate the AR(1) model
matrix bhat = $coeff# our beta-hat
scalar fc1990q1 = bhat[1] + x[1989:4]*bhat[2]
scalar fc1990q2 = bhat[1] + x[1990:1]*bhat[2]
scalar fc1990q3 = bhat[1] + x[1990:2]*bhat[2]
print fc1990q1 fc1990q2 fc1990q3
# Compute the static forecasts for the observations
# 1990q1 and 1990q3 using gretl's 'fcast' command
fcast 1990:1 1990:3 --static
# We can also do this for all periods after 1999m12
fcast --static --out-of-sample
# Note that you don't have to specify a concrete date
# but could just move the period forecasted by
fcast ($t2+1) ($t2+3) --static
</hansl>
4) Dynamic out-of-sample forecast without re-estimating the model

Witchslayer Gretl

In this case, we estimate the model using the first R observations as our training set, again grab the coefficient vector beta-hat, and fit the model to the following R+h observations. However, instead of using actual realizations of x, we use the forecasted values. Thus, one obtains for the h-step ahead forecast (e.g. h=2) a whole path of forecasts.

Gretl Crawford Homes

<hansl>
smpl ; 1989:4# set the training set
ols x 0 x(-1)# estimate the AR(1) model
matrix bhat = $coeff# grab the coefficient vector

# Do the dynamic forecast by hand first
scalar dfc1990q1 = bhat[1] + x[1989:4]*bhat[2]# 1st value is the actual outcome
scalar dfc1990q2 = bhat[1] + dfc1990q1*bhat[2]# now we use the forecasted value of the previous period
scalar dfc1990q3 = bhat[1] + dfc1990q2*bhat[2]
print dfc1990q1 dfc1990q2 dfc1990q3
# Compare using gretl
fcast 1990:1 1990:3 --dynamic
# Run the dynamic forecast for all remaining periods
fcast --dynamic --out-of-sample
</hansl>
5) Recursive k-step ahead out-of-sample forecast with re-estimating the model
In this exercise, we fit the model to the first R-k observations in order to compute a forecast for period R. Next, we extend the sample end to R-k+1 while keeping the sample beginning fixed. Based on these R-k+1 observations, the k-step ahead forecast for R+1 is computed. Continue until the procedure stops.
This type of recursive forecast can easily be done in gretl. It is important to note, that this procedure always applies the dynamic forecast scheme described above but the beta-hat is also re-estimated at each iteration:

Gretl Download

<hansl>
smpl full
ols x 0 x(-1) -q
 fcast 1990:1 1990:2 1 --rolling# 1-step ahead rolling fc
# Compare fc for 1990:2 with
smpl full
smpl ; 1990:1
ols x 0 x(-1) -q
fcast ($t2+1) ($t2+1) --dynamic
# or equivalently
fcast ($t2) ($t2+1) --static

# Compute all possible sequences of k-step recursive forecasts
smpl full
ols x 0 x(-1) -q
fcast 1 fc1 --rolling# 1-step ahead
fcast 6 fc6 --rolling# 6-step ahead

# Plot the 1-step ahead forecast based on a recursive origin
gnuplot x fc1 fc6 --with-lines --time-series --output=display

Gratel

To see the plot of the realization 'x' as well as the 1- and 6-step ahead out-of-sample forecasts using a recursive scheme, just download the FORECAST.PDF below.