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utilities

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    This MATLAB/Octave toolbox comes with two classes:

    • @dates which is used to handle dates.
    • @dseries which 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.git

    or downloading a zip archive:

    ~$ wget https://git.dynare.org/Dynare/dseries/-/archive/master/dseries-master.zip
    ~$ unsip dseries-master.zip
    -$ mv dseries-master dseries

    Usage

    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.