This MATLAB/Octave toolbox comes with two classes:
-
@dateswhich is used to handle dates. -
@dserieswhich is used to handle time series data.
The package is a dependence of Dynare, but can also be used as a standalone package without Dynare. The package is compatible with MATLAB 2008a and following versions, and (almost compatible with) the latest Octave version.
Installation
The toolbox can be installed by cloning the Git repository:
~$ git clone https://git.dynare.org/Dynare/dseries.gitor downloading a zip archive:
~$ wget https://git.dynare.org/Dynare/dseries/-/archive/master/dseries-master.zip
~$ unsip dseries-master.zip
-$ mv dseries-master dseriesUsage
Add the dseries/src folder to the MATLAB/Octave path, and run the following command (on MATLAB/Octave) prompt:
>> dseries().initialize()which, depending on your system, will add the necessary subfolders to the MATLAB/Octave path.
You are then ready to go. A full documentation will come soon, but you can already obtain a general idea by looking into the Dynare reference manual.
Note that X13-ARIMA-SEATS is required
for accessing all the features of the toolbox. On Windows and macOS, an
X13-ARIMA-SEATS binary is included in standalone dseries packages and in Dynare
packages. On Debian and Ubuntu it is possible to install X13-ARIMA-SEATS with
apt install x13as (on Debian, you must have the non-free archive area listed
in package sources).
Examples
Instantiate a dseries object from an array
>> A = randn(50, 3);
>> d = dseries(A, dates('2000Q1'), {'A1', 'A2', 'A3'});The first argument of the dseries constructor is an array of data,
observations and variables are respectively along the rows and
columns. The second argument is the initial period of the dataset. The
last argument is a cell array of row character arrays for the names of
the variables.
>> d
d is a dseries object:
| A1 | A2 | A3
2000Q1 | -1.0891 | -2.1384 | -0.29375
2000Q2 | 0.032557 | -0.83959 | -0.84793
2000Q3 | 0.55253 | 1.3546 | -1.1201
2000Q4 | 1.1006 | -1.0722 | 2.526
2001Q1 | 1.5442 | 0.96095 | 1.6555
2001Q2 | 0.085931 | 0.12405 | 0.30754
2001Q3 | -1.4916 | 1.4367 | -1.2571
2001Q4 | -0.7423 | -1.9609 | -0.86547
2002Q1 | -1.0616 | -0.1977 | -0.17653
2002Q2 | 2.3505 | -1.2078 | 0.79142
| | |
2009Q4 | -1.7947 | 0.96423 | 0.62519
2010Q1 | 0.84038 | 0.52006 | 0.18323
2010Q2 | -0.88803 | -0.020028 | -1.0298
2010Q3 | 0.10009 | -0.034771 | 0.94922
2010Q4 | -0.54453 | -0.79816 | 0.30706
2011Q1 | 0.30352 | 1.0187 | 0.13517
2011Q2 | -0.60033 | -0.13322 | 0.51525
2011Q3 | 0.48997 | -0.71453 | 0.26141
2011Q4 | 0.73936 | 1.3514 | -0.94149
2012Q1 | 1.7119 | -0.22477 | -0.16234
2012Q2 | -0.19412 | -0.58903 | -0.14605
>>Instantiate a dseries object from a file
It is possible to instantiate a dseries object from a .csv,
.xls, .xlsx, .mat or m file, see the Dynare reference manual
for a complete description of the constraints on the content of these
files.
>> websave('US_CMR_data_t.csv', 'https://www.dynare.org/Datasets/US_CMR_data_t.csv');
>> d = dseries('US_CMR_data_t.csv');
>> d
d is a dseries object:
| gdp_rpc | conso_rpc | inves_rpc | defgdp | ... | networth_rpc | re | slope | creditspread
1980Q1 | 47941413.1257 | NaN | NaN | 0.40801 | ... | 33.6814 | 0.15047 | -0.0306 | 0.014933
1980Q2 | 46775570.3923 | NaN | NaN | 0.41772 | ... | 32.2721 | 0.12687 | -0.0221 | 0.028833
1980Q3 | 46528261.9561 | NaN | NaN | 0.42705 | ... | 36.6499 | 0.098367 | 0.011167 | 0.022167
1980Q4 | 47249592.2997 | NaN | NaN | 0.43818 | ... | 39.4069 | 0.15853 | -0.0343 | 0.022467
1981Q1 | 48059176.868 | NaN | NaN | 0.44972 | ... | 37.9954 | 0.1657 | -0.0361 | 0.0229
1981Q2 | 47531422.174 | NaN | NaN | 0.45863 | ... | 38.6262 | 0.1778 | -0.0403 | 0.0202
1981Q3 | 47951509.5055 | NaN | NaN | 0.46726 | ... | 36.3246 | 0.17577 | -0.0273 | 0.016333
1981Q4 | 47273009.6902 | NaN | NaN | 0.47534 | ... | 34.8693 | 0.13587 | 0.005 | 0.025933
1982Q1 | 46501690.1111 | NaN | NaN | 0.48188 | ... | 32.0964 | 0.14227 | 0.00066667 | 0.027367
1982Q2 | 46525455.3206 | NaN | NaN | 0.48814 | ... | 31.6967 | 0.14513 | -0.0058333 | 0.0285
| | | | | ... | | | |
2016Q1 | 85297205.4011 | 51926452.5716 | 21892729.0934 | 1.0514 | ... | 420.7154 | 0.0016 | 0.0203 | 0.0323
2016Q2 | 85407205.5913 | 52096454.9154 | 21824323.7487 | 1.0506 | ... | 398.7084 | 0.0036 | 0.0156 | 0.0339
2016Q3 | 85796604.1157 | 52436447.9843 | 21874814.014 | 1.0578 | ... | 424.8703 | 0.0037333 | 0.0138 | 0.029167
2016Q4 | 86101149.6919 | 52595613.0404 | 22010921.8985 | 1.0617 | ... | 444.622 | 0.0039667 | 0.011667 | 0.026967
2017Q1 | 86376652.4732 | 52795431.0988 | 22399301.0801 | 1.0672 | ... | 450.8777 | 0.0045 | 0.0168 | 0.0251
2017Q2 | 86982016.8089 | 53164725.076 | 22671020.5449 | 1.0728 | ... | 481.8778 | 0.007 | 0.017433 | 0.022167
2017Q3 | 87605975.0339 | 53451779.0342 | 23033324.7981 | 1.0758 | ... | 496.3342 | 0.0095 | 0.013133 | 0.022367
2017Q4 | 88111231.6601 | 53601437.7291 | 23477516.6946 | 1.081 | ... | 509.1968 | 0.011533 | 0.0109 | 0.020867
2018Q1 | 88557263.9759 | 53960814.0875 | 23726936.444 | 1.0882 | ... | 536.4746 | 0.012033 | 0.011667 | 0.019
2018Q2 | 88817646.3122 | 53931032.9449 | 23989494.0402 | 1.0937 | ... | 560.3093 | 0.014467 | 0.013133 | 0.0171
2018Q3 | 89689102.8539 | 54343965.1391 | 24123408.6269 | 1.1027 | ... | 554.472 | 0.017367 | 0.011833 | 0.0186
>>Create time series
Using an existing dseries object it is possible to create new time series:
>> d.cy = d.conso_rpc/d.gdp_rpc
d is a dseries object:
| conso_rpc | creditspread | cy | defgdp | ... | pinves_defl | re | slope | wage_rph
1980Q1 | NaN | 0.014933 | NaN | 0.40801 | ... | 145.6631 | 0.15047 | -0.0306 | 65.0376
1980Q2 | NaN | 0.028833 | NaN | 0.41772 | ... | 145.6095 | 0.12687 | -0.0221 | 65.1872
1980Q3 | NaN | 0.022167 | NaN | 0.42705 | ... | 145.3811 | 0.098367 | 0.011167 | 65.3858
1980Q4 | NaN | 0.022467 | NaN | 0.43818 | ... | 144.3745 | 0.15853 | -0.0343 | 65.5028
1981Q1 | NaN | 0.0229 | NaN | 0.44972 | ... | 144.6055 | 0.1657 | -0.0361 | 65.4385
1981Q2 | NaN | 0.0202 | NaN | 0.45863 | ... | 145.6512 | 0.1778 | -0.0403 | 65.3054
1981Q3 | NaN | 0.016333 | NaN | 0.46726 | ... | 144.7545 | 0.17577 | -0.0273 | 65.5074
1981Q4 | NaN | 0.025933 | NaN | 0.47534 | ... | 145.4748 | 0.13587 | 0.005 | 65.4142
1982Q1 | NaN | 0.027367 | NaN | 0.48188 | ... | 144.924 | 0.14227 | 0.00066667 | 66.1617
1982Q2 | NaN | 0.0285 | NaN | 0.48814 | ... | 144.4647 | 0.14513 | -0.0058333 | 65.8827
| | | | | ... | | | |
2016Q1 | 51926452.5716 | 0.0323 | 0.60877 | 1.0514 | ... | 98.7988 | 0.0016 | 0.0203 | 102.4176
2016Q2 | 52096454.9154 | 0.0339 | 0.60998 | 1.0506 | ... | 98.2923 | 0.0036 | 0.0156 | 102.5282
2016Q3 | 52436447.9843 | 0.029167 | 0.61117 | 1.0578 | ... | 98.1811 | 0.0037333 | 0.0138 | 102.0061
2016Q4 | 52595613.0404 | 0.026967 | 0.61086 | 1.0617 | ... | 98.0833 | 0.0039667 | 0.011667 | 102.1861
2017Q1 | 52795431.0988 | 0.0251 | 0.61122 | 1.0672 | ... | 97.8223 | 0.0045 | 0.0168 | 102.8336
2017Q2 | 53164725.076 | 0.022167 | 0.61122 | 1.0728 | ... | 97.6873 | 0.007 | 0.017433 | 103.4761
2017Q3 | 53451779.0342 | 0.022367 | 0.61014 | 1.0758 | ... | 97.8137 | 0.0095 | 0.013133 | 103.5137
2017Q4 | 53601437.7291 | 0.020867 | 0.60834 | 1.081 | ... | 97.4819 | 0.011533 | 0.0109 | 104.3091
2018Q1 | 53960814.0875 | 0.019 | 0.60933 | 1.0882 | ... | 97.4234 | 0.012033 | 0.011667 | 104.1112
2018Q2 | 53931032.9449 | 0.0171 | 0.60721 | 1.0937 | ... | 97.5643 | 0.014467 | 0.013133 | 104.5487
2018Q3 | 54343965.1391 | 0.0186 | 0.60591 | 1.1027 | ... | 97.8751 | 0.017367 | 0.011833 | 103.7128
>>Recursive definitions for new time series are also possible. For instance one can create a sample from an ARMA(1,1) stochastic process as follows:
>> e = dseries(randn(100, 1), '2000Q1', 'e', '\varepsilon');
>> y = dseries(zeros(100, 1), '2000Q1', 'y');
>> from 2000Q2 to 2024Q4 do y(t)=.9*y(t-1)+e(t)-.4*e(t-1);
>> y
y is a dseries object:
| y
2000Q1 | 0
2000Q2 | -0.95221
2000Q3 | -0.6294
2000Q4 | -1.8935
2001Q1 | -1.1536
2001Q2 | -1.5905
2001Q3 | 0.97056
2001Q4 | 1.1409
2002Q1 | -1.9255
2002Q2 | -0.29287
|
2022Q2 | -1.4683
2022Q3 | -1.3758
2022Q4 | -1.2218
2023Q1 | -0.98145
2023Q2 | -0.96542
2023Q3 | -0.23203
2023Q4 | -0.34404
2024Q1 | 1.4606
2024Q2 | 0.901
2024Q3 | 2.4906
2024Q4 | 0.79661
>>Any univariate nonlinear recursive model can be simulated with this approach.