Updated manual. Description of the methods for the @dseries class.
Stéphane Adjemian (Penelope)
parent
e36516f766
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%%Title: ../doc/dynare.plots/BaxterKingFilter.eps
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%%CreationDate: 11/12/2013 18:18:18
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628 247 5717 4541 rc
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85 dict begin %Colortable dictionary
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c0
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1 j
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1 sg
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0 0 6913 5186 rf
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6 w
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0 4226 5356 0 0 -4226 899 4615 4 MP
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PP
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-5356 0 0 4226 5356 0 0 -4226 899 4615 5 MP stroke
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4 w
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DO
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SO
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6 w
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0 sg
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899 4615 mt 6255 4615 L
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899 389 mt 6255 389 L
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899 4615 mt 899 389 L
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6255 4615 mt 6255 389 L
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||||
899 4615 mt 6255 4615 L
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||||
899 4615 mt 899 389 L
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1480 4615 mt 1480 4561 L
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1480 389 mt 1480 442 L
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%%IncludeResource: font Helvetica
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/Helvetica /ISOLatin1Encoding 120 FMSR
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1267 4760 mt
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(1957Q4) s
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2092 4615 mt 2092 4561 L
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2092 389 mt 2092 442 L
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1879 4760 mt
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(1962Q4) s
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2704 4615 mt 2704 4561 L
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2704 389 mt 2704 442 L
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2491 4760 mt
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(1967Q4) s
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3316 4615 mt 3316 4561 L
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3316 389 mt 3316 442 L
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3103 4760 mt
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(1972Q4) s
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3928 4615 mt 3928 4561 L
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3928 389 mt 3928 442 L
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3715 4760 mt
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(1977Q4) s
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4541 4615 mt 4541 4561 L
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4541 389 mt 4541 442 L
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4328 4760 mt
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(1982Q4) s
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5153 4615 mt 5153 4561 L
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5153 389 mt 5153 442 L
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4940 4760 mt
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(1987Q4) s
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5765 4615 mt 5765 4561 L
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5765 389 mt 5765 442 L
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5552 4760 mt
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(1992Q4) s
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899 4514 mt 952 4514 L
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6255 4514 mt 6201 4514 L
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728 4558 mt
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(-1) s
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899 4050 mt 952 4050 L
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6255 4050 mt 6201 4050 L
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628 4094 mt
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(-0.8) s
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899 3585 mt 952 3585 L
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6255 3585 mt 6201 3585 L
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628 3629 mt
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(-0.6) s
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899 3121 mt 952 3121 L
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6255 3121 mt 6201 3121 L
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628 3165 mt
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(-0.4) s
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899 2657 mt 952 2657 L
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6255 2657 mt 6201 2657 L
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||||
628 2701 mt
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||||
(-0.2) s
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||||
899 2193 mt 952 2193 L
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6255 2193 mt 6201 2193 L
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798 2237 mt
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(0) s
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899 1729 mt 952 1729 L
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6255 1729 mt 6201 1729 L
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||||
698 1773 mt
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||||
(0.2) s
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899 1264 mt 952 1264 L
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6255 1264 mt 6201 1264 L
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698 1308 mt
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||||
(0.4) s
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899 800 mt 952 800 L
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6255 800 mt 6201 800 L
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698 844 mt
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(0.6) s
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899 4615 mt 6255 4615 L
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899 389 mt 6255 389 L
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899 4615 mt 899 389 L
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6255 4615 mt 6255 389 L
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gs 899 389 5357 4227 rc
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||||
31 12 31 676 30 -486 31 425 31 -1153 30 -555 31 -24 30 460
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||||
31 -265 31 -694 30 753 31 -407 30 230 31 -138 31 659 30 472
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||||
31 -400 30 -229 31 -107 31 -5 30 -479 31 615 30 -148 31 597
|
||||
31 -50 30 -1070 31 679 30 77 31 493 31 524 30 -41 31 -1027
|
||||
30 -141 31 214 31 3 30 -157 31 198 31 202 30 -681 31 411
|
||||
30 -34 31 -120 31 4 30 -526 31 -11 30 275 31 1054 31 176
|
||||
30 -1612 31 249 30 1174 31 -48 31 -535 30 609 31 364 30 -70
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||||
31 -1162 31 454 30 579 31 -638 30 210 31 -685 31 1139 30 -375
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31 405 30 1063 31 -819 31 -1243 30 -191 31 54 31 182 30 -625
|
||||
31 742 30 307 31 -138 31 18 30 -392 31 1208 30 -331 31 -617
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||||
31 -397 30 500 31 -129 30 650 31 -643 31 -39 30 -633 31 262
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||||
30 238 31 -119 31 581 30 -78 31 418 30 277 31 -272 31 -539
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||||
30 619 31 418 30 -911 31 -454 31 -270 30 -569 31 1089 30 -246
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||||
31 210 31 532 30 303 31 -230 31 -1012 30 156 31 -977 30 -216
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31 452 31 -425 30 1663 31 -578 30 1117 31 122 31 -122 30 -108
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31 399 30 22 31 -661 31 60 30 125 31 1128 30 -715 31 -50
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31 -594 30 -469 31 153 30 287 31 -45 31 462 30 273 31 -89
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30 -321 31 -421 31 -124 30 297 31 -159 31 -443 30 286 31 -229
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30 -7 31 213 31 97 30 598 31 -556 30 -367 31 -95 31 391
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30 -463 31 268 30 1060 31 -948 31 419 30 -89 31 566 30 -43
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31 -678 31 -47 30 573 31 -23 30 604 31 -26 31 -643 30 409
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31 293 30 -83 31 -665 31 510 30 -870 31 78 30 589 899 1359 176 MP stroke
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DA
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/c8 { 1.000000 0.000000 0.000000 sr} bdef
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c8
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31 299 31 88 30 -301 31 -716 31 -901 30 -729 31 -326 30 58
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31 216 31 118 30 -42 31 10 30 279 31 615 31 724 30 429
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||||
31 -134 30 -602 31 -680 31 -328 30 199 31 505 30 392 31 -47
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||||
31 -388 30 -329 31 128 30 619 31 705 31 319 30 -296 31 -731
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||||
30 -703 31 -295 31 110 30 239 31 148 31 58 30 9 31 -102
|
||||
30 -214 31 -260 31 -210 30 -6 31 321 30 534 31 380 31 -5
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||||
30 -274 31 -224 30 104 31 428 31 485 30 180 31 -257 30 -450
|
||||
31 -305 31 -81 30 -35 31 -167 30 -196 31 70 31 490 30 719
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||||
31 565 30 27 31 -720 31 -1174 30 -1007 31 -337 31 317 30 581
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||||
31 483 30 286 31 215 31 247 30 237 31 72 30 -222 31 -387
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31 -252 30 10 31 165 30 102 31 -128 31 -430 30 -519 31 -200
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||||
30 336 31 751 31 738 30 361 31 5 30 -129 31 -78 31 21
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||||
30 -17 31 -229 30 -555 31 -725 31 -612 30 -187 31 402 30 816
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||||
31 912 31 573 30 -19 31 -590 31 -942 30 -1036 31 -803 30 -229
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||||
31 441 31 900 30 1018 31 860 30 591 31 341 31 75 30 -227
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||||
31 -457 30 -466 31 -276 31 47 30 293 31 237 30 -104 31 -601
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||||
31 -861 30 -663 31 -72 30 568 31 886 31 685 30 148 31 -345
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||||
30 -547 31 -399 31 -134 30 44 31 18 31 -103 30 -176 31 -84
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||||
30 202 31 451 31 429 30 93 31 -293 30 -440 31 -292 31 4
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||||
30 245 31 322 30 302 31 230 31 111 30 -34 31 -158 30 -209
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31 -178 31 15 30 223 31 310 30 298 31 226 31 58 30 -143
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31 -200 30 -175 31 -115 31 -137 30 -250 31 -397 30 -514 899 2994 176 MP stroke
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gr
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c8
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DA
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0 sg
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882 4658 mt
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( ) s
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6239 431 mt
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( ) s
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SO
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1 sg
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0 334 1903 0 0 -334 4293 783 4 MP
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PP
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-1903 0 0 334 1903 0 0 -334 4293 783 5 MP stroke
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4 w
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DO
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SO
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6 w
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0 sg
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4293 783 mt 6196 783 L
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4293 449 mt 6196 449 L
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4293 783 mt 4293 449 L
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6196 783 mt 6196 449 L
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4293 783 mt 6196 783 L
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4293 783 mt 4293 449 L
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4293 783 mt 6196 783 L
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4293 449 mt 6196 449 L
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4293 783 mt 4293 449 L
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6196 783 mt 6196 449 L
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4756 583 mt
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(Stationary component of y) s
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gs 4293 449 1904 335 rc
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356 0 4364 540 2 MP stroke
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gr
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4756 734 mt
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(Filtered y) s
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gs 4293 449 1904 335 rc
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DA
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c8
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356 0 4364 691 2 MP stroke
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SO
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gr
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c8
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end %%Color Dict
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@ -0,0 +1,386 @@
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%!PS-Adobe-3.0 EPSF-3.0
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%%Creator: MATLAB, The MathWorks, Inc. Version 8.1.0.604 (R2013a). Operating System: Linux 3.10-3-amd64 #1 SMP Debian 3.10.11-1 (2013-09-10) x86_64.
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%%Title: ../doc/dynare.plots/HPCycle.eps
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||||
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% operator abbreviations
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(1959Q4) s
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(1964Q4) s
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(1969Q4) s
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3350 4760 mt
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3888 4760 mt
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(1979Q4) s
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|
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|
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|
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|
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|
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|
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|
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4293 449 mt 6196 449 L
|
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|
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6196 783 mt 6196 449 L
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|
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/DO { [.5 dpi2point mul 4 dpi2point mul] 0 setdash } bdef
|
||||
/DA { [6 dpi2point mul] 0 setdash } bdef
|
||||
/DD { [.5 dpi2point mul 4 dpi2point mul 6 dpi2point mul 4
|
||||
dpi2point mul] 0 setdash } bdef
|
||||
% macros for lines and objects
|
||||
/L {lineto stroke} bdef
|
||||
/MP {3 1 roll moveto 1 sub {rlineto} repeat} bdef
|
||||
/AP {{rlineto} repeat} bdef
|
||||
/PDlw -1 def
|
||||
/W {/PDlw currentlinewidth def setlinewidth} def
|
||||
/PP {closepath eofill} bdef
|
||||
/DP {closepath stroke} bdef
|
||||
/MR {4 -2 roll moveto dup 0 exch rlineto exch 0 rlineto
|
||||
neg 0 exch rlineto closepath} bdef
|
||||
/FR {MR stroke} bdef
|
||||
/PR {MR fill} bdef
|
||||
/L1i {{currentfile picstr readhexstring pop} image} bdef
|
||||
/tMatrix matrix def
|
||||
/MakeOval {newpath tMatrix currentmatrix pop translate scale
|
||||
0 0 1 0 360 arc tMatrix setmatrix} bdef
|
||||
/FO {MakeOval stroke} bdef
|
||||
/PO {MakeOval fill} bdef
|
||||
/PD {currentlinewidth 2 div 0 360 arc fill
|
||||
PDlw -1 eq not {PDlw w /PDlw -1 def} if} def
|
||||
/FA {newpath tMatrix currentmatrix pop translate scale
|
||||
0 0 1 5 -2 roll arc tMatrix setmatrix stroke} bdef
|
||||
/PA {newpath tMatrix currentmatrix pop translate 0 0 moveto scale
|
||||
0 0 1 5 -2 roll arc closepath tMatrix setmatrix fill} bdef
|
||||
/FAn {newpath tMatrix currentmatrix pop translate scale
|
||||
0 0 1 5 -2 roll arcn tMatrix setmatrix stroke} bdef
|
||||
/PAn {newpath tMatrix currentmatrix pop translate 0 0 moveto scale
|
||||
0 0 1 5 -2 roll arcn closepath tMatrix setmatrix fill} bdef
|
||||
/vradius 0 def /hradius 0 def /lry 0 def
|
||||
/lrx 0 def /uly 0 def /ulx 0 def /rad 0 def
|
||||
/MRR {/vradius xdef /hradius xdef /lry xdef /lrx xdef /uly xdef
|
||||
/ulx xdef newpath tMatrix currentmatrix pop ulx hradius add uly
|
||||
vradius add translate hradius vradius scale 0 0 1 180 270 arc
|
||||
tMatrix setmatrix lrx hradius sub uly vradius add translate
|
||||
hradius vradius scale 0 0 1 270 360 arc tMatrix setmatrix
|
||||
lrx hradius sub lry vradius sub translate hradius vradius scale
|
||||
0 0 1 0 90 arc tMatrix setmatrix ulx hradius add lry vradius sub
|
||||
translate hradius vradius scale 0 0 1 90 180 arc tMatrix setmatrix
|
||||
closepath} bdef
|
||||
/FRR {MRR stroke } bdef
|
||||
/PRR {MRR fill } bdef
|
||||
/MlrRR {/lry xdef /lrx xdef /uly xdef /ulx xdef /rad lry uly sub 2 div def
|
||||
newpath tMatrix currentmatrix pop ulx rad add uly rad add translate
|
||||
rad rad scale 0 0 1 90 270 arc tMatrix setmatrix lrx rad sub lry rad
|
||||
sub translate rad rad scale 0 0 1 270 90 arc tMatrix setmatrix
|
||||
closepath} bdef
|
||||
/FlrRR {MlrRR stroke } bdef
|
||||
/PlrRR {MlrRR fill } bdef
|
||||
/MtbRR {/lry xdef /lrx xdef /uly xdef /ulx xdef /rad lrx ulx sub 2 div def
|
||||
newpath tMatrix currentmatrix pop ulx rad add uly rad add translate
|
||||
rad rad scale 0 0 1 180 360 arc tMatrix setmatrix lrx rad sub lry rad
|
||||
sub translate rad rad scale 0 0 1 0 180 arc tMatrix setmatrix
|
||||
closepath} bdef
|
||||
/FtbRR {MtbRR stroke } bdef
|
||||
/PtbRR {MtbRR fill } bdef
|
||||
/stri 6 array def /dtri 6 array def
|
||||
/smat 6 array def /dmat 6 array def
|
||||
/tmat1 6 array def /tmat2 6 array def /dif 3 array def
|
||||
/asub {/ind2 exch def /ind1 exch def dup dup
|
||||
ind1 get exch ind2 get sub exch } bdef
|
||||
/tri_to_matrix {
|
||||
2 0 asub 3 1 asub 4 0 asub 5 1 asub
|
||||
dup 0 get exch 1 get 7 -1 roll astore } bdef
|
||||
/compute_transform {
|
||||
dmat dtri tri_to_matrix tmat1 invertmatrix
|
||||
smat stri tri_to_matrix tmat2 concatmatrix } bdef
|
||||
/ds {stri astore pop} bdef
|
||||
/dt {dtri astore pop} bdef
|
||||
/db {2 copy /cols xdef /rows xdef mul dup 3 mul string
|
||||
currentfile
|
||||
3 index 0 eq {/ASCIIHexDecode filter}
|
||||
{/ASCII85Decode filter 3 index 2 eq {/RunLengthDecode filter} if }
|
||||
ifelse exch readstring pop
|
||||
dup 0 3 index getinterval /rbmap xdef
|
||||
dup 2 index dup getinterval /gbmap xdef
|
||||
1 index dup 2 mul exch getinterval /bbmap xdef pop pop}bdef
|
||||
/it {gs np dtri aload pop moveto lineto lineto cp c
|
||||
cols rows 8 compute_transform
|
||||
rbmap gbmap bbmap true 3 colorimage gr}bdef
|
||||
/il {newpath moveto lineto stroke}bdef
|
||||
currentdict end def
|
||||
%%EndProlog
|
||||
|
||||
%%BeginSetup
|
||||
MathWorks begin
|
||||
|
||||
0 cap
|
||||
|
||||
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|
||||
%%EndSetup
|
||||
|
||||
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||||
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|
||||
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||||
MathWorks begin
|
||||
bpage
|
||||
%%EndPageSetup
|
||||
|
||||
%%BeginObject: obj1
|
||||
bplot
|
||||
|
||||
/dpi2point 12 def
|
||||
portraitMode 0216 7344 csm
|
||||
|
||||
731 247 5786 4541 rc
|
||||
85 dict begin %Colortable dictionary
|
||||
/c0 { 0.000000 0.000000 0.000000 sr} bdef
|
||||
/c1 { 1.000000 1.000000 1.000000 sr} bdef
|
||||
/c2 { 0.900000 0.000000 0.000000 sr} bdef
|
||||
/c3 { 0.000000 0.820000 0.000000 sr} bdef
|
||||
/c4 { 0.000000 0.000000 0.800000 sr} bdef
|
||||
/c5 { 0.910000 0.820000 0.320000 sr} bdef
|
||||
/c6 { 1.000000 0.260000 0.820000 sr} bdef
|
||||
/c7 { 0.000000 0.820000 0.820000 sr} bdef
|
||||
c0
|
||||
1 j
|
||||
1 sg
|
||||
0 0 6913 5186 rf
|
||||
6 w
|
||||
0 4226 5356 0 0 -4226 899 4615 4 MP
|
||||
PP
|
||||
-5356 0 0 4226 5356 0 0 -4226 899 4615 5 MP stroke
|
||||
4 w
|
||||
DO
|
||||
SO
|
||||
6 w
|
||||
0 sg
|
||||
899 4615 mt 6255 4615 L
|
||||
899 389 mt 6255 389 L
|
||||
899 4615 mt 899 389 L
|
||||
6255 4615 mt 6255 389 L
|
||||
899 4615 mt 6255 4615 L
|
||||
899 4615 mt 899 389 L
|
||||
1410 4615 mt 1410 4561 L
|
||||
1410 389 mt 1410 442 L
|
||||
%%IncludeResource: font Helvetica
|
||||
/Helvetica /ISOLatin1Encoding 120 FMSR
|
||||
|
||||
1197 4760 mt
|
||||
(1954Q4) s
|
||||
1948 4615 mt 1948 4561 L
|
||||
1948 389 mt 1948 442 L
|
||||
1735 4760 mt
|
||||
(1959Q4) s
|
||||
2486 4615 mt 2486 4561 L
|
||||
2486 389 mt 2486 442 L
|
||||
2273 4760 mt
|
||||
(1964Q4) s
|
||||
3025 4615 mt 3025 4561 L
|
||||
3025 389 mt 3025 442 L
|
||||
2812 4760 mt
|
||||
(1969Q4) s
|
||||
3563 4615 mt 3563 4561 L
|
||||
3563 389 mt 3563 442 L
|
||||
3350 4760 mt
|
||||
(1974Q4) s
|
||||
4101 4615 mt 4101 4561 L
|
||||
4101 389 mt 4101 442 L
|
||||
3888 4760 mt
|
||||
(1979Q4) s
|
||||
4640 4615 mt 4640 4561 L
|
||||
4640 389 mt 4640 442 L
|
||||
4427 4760 mt
|
||||
(1984Q4) s
|
||||
5178 4615 mt 5178 4561 L
|
||||
5178 389 mt 5178 442 L
|
||||
4965 4760 mt
|
||||
(1989Q4) s
|
||||
5716 4615 mt 5716 4561 L
|
||||
5716 389 mt 5716 442 L
|
||||
5503 4760 mt
|
||||
(1994Q4) s
|
||||
6255 4615 mt 6255 4561 L
|
||||
6255 389 mt 6255 442 L
|
||||
6042 4760 mt
|
||||
(1999Q4) s
|
||||
899 3776 mt 952 3776 L
|
||||
6255 3776 mt 6201 3776 L
|
||||
798 3820 mt
|
||||
(5) s
|
||||
899 2921 mt 952 2921 L
|
||||
6255 2921 mt 6201 2921 L
|
||||
731 2965 mt
|
||||
(10) s
|
||||
899 2067 mt 952 2067 L
|
||||
6255 2067 mt 6201 2067 L
|
||||
731 2111 mt
|
||||
(15) s
|
||||
899 1212 mt 952 1212 L
|
||||
6255 1212 mt 6201 1212 L
|
||||
731 1256 mt
|
||||
(20) s
|
||||
899 4615 mt 6255 4615 L
|
||||
899 389 mt 6255 389 L
|
||||
899 4615 mt 899 389 L
|
||||
6255 4615 mt 6255 389 L
|
||||
gs 899 389 5357 4227 rc
|
||||
27 -29 27 -16 27 -32 27 -21 27 59 27 -35 27 18 27 -24
|
||||
27 -16 27 -14 27 67 26 -83 27 -31 27 -48 27 -116 27 11
|
||||
27 -38 27 -42 27 30 27 8 27 19 27 -25 27 -16 26 -75
|
||||
27 -2 27 -16 27 8 27 -41 27 -29 27 50 27 -10 27 -38
|
||||
27 -39 27 -13 27 -49 26 6 27 3 27 21 27 -22 27 -25
|
||||
27 -14 27 34 27 0 27 -53 27 -9 27 -18 26 -87 27 -53
|
||||
27 -15 27 -28 27 -54 27 -28 27 -28 27 -14 27 41 27 50
|
||||
27 -12 27 -73 26 50 27 50 27 -44 27 8 27 -24 27 25
|
||||
27 -42 27 -15 27 3 27 -21 27 -6 27 -74 26 -23 27 -39
|
||||
27 -42 27 -47 27 -20 27 -18 27 -10 27 31 27 -54 27 -38
|
||||
27 -44 26 29 27 -49 27 10 27 -38 27 -51 27 -30 27 -57
|
||||
27 -7 27 2 27 -11 27 20 27 -44 26 -88 27 -39 27 -5
|
||||
27 10 27 12 27 -26 27 3 27 -78 27 -23 27 -39 27 -57
|
||||
27 -10 26 38 27 0 27 -66 27 1 27 -9 27 1 27 -27
|
||||
27 -65 27 -12 27 -27 27 20 27 -41 26 -22 27 -20 27 27
|
||||
27 -4 27 23 27 -43 27 -28 27 8 27 -16 27 -25 27 19
|
||||
26 25 27 -104 27 -17 27 -14 27 -8 27 -4 27 -6 27 -35
|
||||
27 -11 27 -25 27 -41 27 -32 26 -42 27 -36 27 -59 27 -73
|
||||
27 5 27 -7 27 12 27 -29 27 59 27 -66 27 -75 27 -15
|
||||
26 -23 27 -52 27 5 27 -54 27 -39 27 -47 27 9 27 13
|
||||
27 -46 27 -9 27 -78 26 -21 27 -18 27 -55 27 -68 27 -25
|
||||
27 -37 27 -22 27 -65 27 -29 27 -28 27 22 27 -5 26 -24
|
||||
27 44 27 -70 27 31 27 -44 27 -17 27 -58 27 -28 27 -68
|
||||
27 -50 27 -46 27 -56 26 -36 27 3 27 -13 27 -61 27 -50
|
||||
27 -43 27 -56 27 14 27 -19 27 3 27 -5 26 -50 899 4592 200 MP stroke
|
||||
DA
|
||||
/c8 { 1.000000 0.000000 0.000000 sr} bdef
|
||||
c8
|
||||
27 -13 27 -13 27 -12 27 -13 27 -13 27 -13 27 -14 27 -15
|
||||
27 -15 27 -16 27 -17 26 -18 27 -19 27 -20 27 -20 27 -21
|
||||
27 -21 27 -20 27 -20 27 -20 27 -19 27 -19 27 -19 26 -18
|
||||
27 -18 27 -17 27 -17 27 -17 27 -16 27 -16 27 -15 27 -15
|
||||
27 -15 27 -15 27 -15 26 -14 27 -15 27 -15 27 -15 27 -15
|
||||
27 -16 27 -17 27 -17 27 -18 27 -19 27 -19 26 -20 27 -20
|
||||
27 -20 27 -20 27 -18 27 -18 27 -16 27 -15 27 -13 27 -12
|
||||
27 -10 27 -10 26 -9 27 -8 27 -8 27 -8 27 -9 27 -10
|
||||
27 -10 27 -12 27 -13 27 -14 27 -16 27 -17 26 -18 27 -20
|
||||
27 -20 27 -22 27 -22 27 -22 27 -23 27 -24 27 -23 27 -25
|
||||
27 -24 26 -24 27 -25 27 -25 27 -25 27 -25 27 -25 27 -25
|
||||
27 -25 27 -25 27 -25 27 -24 27 -25 26 -24 27 -24 27 -24
|
||||
27 -24 27 -23 27 -24 27 -23 27 -23 27 -23 27 -22 27 -23
|
||||
27 -21 26 -21 27 -21 27 -20 27 -20 27 -19 27 -18 27 -18
|
||||
27 -18 27 -17 27 -16 27 -15 27 -15 26 -14 27 -13 27 -12
|
||||
27 -12 27 -12 27 -11 27 -12 27 -11 27 -12 27 -12 27 -12
|
||||
26 -13 27 -14 27 -15 27 -16 27 -16 27 -17 27 -19 27 -19
|
||||
27 -21 27 -21 27 -23 27 -24 26 -24 27 -25 27 -25 27 -26
|
||||
27 -25 27 -26 27 -25 27 -26 27 -26 27 -26 27 -26 27 -27
|
||||
26 -27 27 -28 27 -28 27 -28 27 -28 27 -29 27 -28 27 -29
|
||||
27 -30 27 -30 27 -30 26 -30 27 -31 27 -31 27 -30 27 -30
|
||||
27 -30 27 -29 27 -29 27 -28 27 -26 27 -27 27 -25 26 -26
|
||||
27 -25 27 -26 27 -26 27 -27 27 -28 27 -29 27 -31 27 -31
|
||||
27 -33 27 -33 27 -34 26 -35 27 -35 27 -35 27 -36 27 -36
|
||||
27 -36 27 -35 27 -36 27 -35 27 -35 27 -35 26 -36 899 4615 200 MP stroke
|
||||
gr
|
||||
|
||||
c8
|
||||
DA
|
||||
0 sg
|
||||
882 4658 mt
|
||||
( ) s
|
||||
6239 431 mt
|
||||
( ) s
|
||||
SO
|
||||
1 sg
|
||||
0 334 2103 0 0 -334 4092 783 4 MP
|
||||
PP
|
||||
-2103 0 0 334 2103 0 0 -334 4092 783 5 MP stroke
|
||||
4 w
|
||||
DO
|
||||
SO
|
||||
6 w
|
||||
0 sg
|
||||
4092 783 mt 6195 783 L
|
||||
4092 449 mt 6195 449 L
|
||||
4092 783 mt 4092 449 L
|
||||
6195 783 mt 6195 449 L
|
||||
4092 783 mt 6195 783 L
|
||||
4092 783 mt 4092 449 L
|
||||
4092 783 mt 6195 783 L
|
||||
4092 449 mt 6195 449 L
|
||||
4092 783 mt 4092 449 L
|
||||
6195 783 mt 6195 449 L
|
||||
4555 583 mt
|
||||
(Nonstationary component of y) s
|
||||
gs 4092 449 2104 335 rc
|
||||
356 0 4163 540 2 MP stroke
|
||||
gr
|
||||
|
||||
4555 734 mt
|
||||
(Estimated trend of y) s
|
||||
gs 4092 449 2104 335 rc
|
||||
DA
|
||||
c8
|
||||
356 0 4163 691 2 MP stroke
|
||||
SO
|
||||
gr
|
||||
|
||||
c8
|
||||
|
||||
end %%Color Dict
|
||||
|
||||
eplot
|
||||
%%EndObject
|
||||
|
||||
epage
|
||||
end
|
||||
|
||||
showpage
|
||||
|
||||
%%Trailer
|
||||
%%EOF
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 175 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 126 KiB |
923
doc/dynare.texi
923
doc/dynare.texi
|
|
@ -49,6 +49,13 @@
|
|||
@emph{Examples}
|
||||
@end macro
|
||||
|
||||
@macro remarkhead
|
||||
@iftex
|
||||
@sp 1
|
||||
@end iftex
|
||||
@noindent @emph{Remark}
|
||||
@end macro
|
||||
|
||||
@macro outputhead
|
||||
@iftex
|
||||
@sp 1
|
||||
|
|
@ -9065,14 +9072,922 @@ do2 = dseries(`filename.csv');
|
|||
do3 = dseries([1; 2; 3], 1999Q3, @{`var123'@}, @{`var_@{123@}'@});
|
||||
@end example
|
||||
|
||||
@sp1
|
||||
@sp 1
|
||||
|
||||
@example In a Matlab/Octave script:
|
||||
do1 = dseries(dates('1999Q3'));
|
||||
do2 = dseries(`filename.csv');
|
||||
do3 = dseries([1; 2; 3], dates('1999Q3'), @{`var123'@}, @{`var_@{123@}'@});
|
||||
>> do1 = dseries(dates('1999Q3'));
|
||||
>> do2 = dseries(`filename.csv');
|
||||
>> do3 = dseries([1; 2; 3], dates('1999Q3'), @{`var123'@}, @{`var_@{123@}'@});
|
||||
@end example
|
||||
|
||||
@sp 1
|
||||
|
||||
A list of the available methods, by alphabetical order, is given below.
|
||||
|
||||
@deftypefn {dseries} {[@var{A}, @var{B}] = } align (@var{A}, @var{B})
|
||||
|
||||
If @dseries objects @var{A} and @var{B} are defined on different time ranges, this function extends @var{A} and/or @var{B} with NaNs so that they are defined on the same time range. Note that both @dseries objects must have the same frequency.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(rand(5,1),dates('2000Q1')); % 2000Q1 -> 2001Q1
|
||||
>> ts1 = dseries(rand(3,1),dates('2000Q4')); % 2000Q4 -> 2001Q2
|
||||
>> [ts0, ts1] = align(ts0, ts1); % 2000Q1 -> 2001Q2
|
||||
>> ts0
|
||||
|
||||
ts0 is a dseries object:
|
||||
|
||||
| Variable_1
|
||||
2000Q1 | 0.81472
|
||||
2000Q2 | 0.90579
|
||||
2000Q3 | 0.12699
|
||||
2000Q4 | 0.91338
|
||||
2001Q1 | 0.63236
|
||||
2001Q2 | NaN
|
||||
|
||||
>> ts1
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| Variable_1
|
||||
2000Q1 | NaN
|
||||
2000Q2 | NaN
|
||||
2000Q3 | NaN
|
||||
2000Q4 | 0.66653
|
||||
2001Q1 | 0.17813
|
||||
2001Q2 | 0.12801
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn {dseries} {@var{B} = } baxter_king_filter (@var{A}, @var{hf}, @var{lf}, @var{K})
|
||||
|
||||
Implementation of Baxter and King (1999) band pass filter for @dseries objects. This filter isolates business cycle fluctuations with a period of length ranging between @var{hf} (high frequency) to @var{lf} (low frequency) using a symetric moving average smoother with @math{2K+1} points, so that K observations at the beginning and at the end of the sample are lost in the computation of the filter.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
% Simulate a component model (stochastic trend, deterministic trend, and a
|
||||
% stationary autoregressive process).
|
||||
e = .2*randn(200,1);
|
||||
u = randn(200,1);
|
||||
stochastic_trend = cumsum(e);
|
||||
deterministic_trend = .1*transpose(1:200);
|
||||
x = zeros(200,1);
|
||||
for i=2:200
|
||||
x(i) = .75*x(i-1) + e(i);
|
||||
end
|
||||
y = x + stochastic_trend + deterministic_trend;
|
||||
|
||||
% Instantiates time series objects.
|
||||
ts0 = dseries(y,'1950Q1');
|
||||
ts1 = dseries(x,'1950Q1'); % stationary component.
|
||||
|
||||
% Apply the Baxter-King filter.
|
||||
ts2 = ts0.baxter_king_filter();
|
||||
|
||||
% Plot the filtered time series.
|
||||
plot(ts1(ts2.dates).data,'-k'); % Plot of the stationary component.
|
||||
hold on
|
||||
plot(ts2.data,'--r'); % Plot of the filtered y.
|
||||
hold off
|
||||
axis tight
|
||||
id = get(gca,'XTick');
|
||||
set(gca,'XTickLabel',strings(ts.dates(id)));
|
||||
@end example
|
||||
|
||||
@iftex
|
||||
@sp 1
|
||||
The previous code should produce something like:
|
||||
@center
|
||||
@image{dynare.plots/BaxterKingFilter,11.32cm,7cm}
|
||||
@end iftex
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn {dseries} {[@var{error_flag}, @var{message} ] = } check (@var{A})
|
||||
|
||||
Sanity check of @dseries object @var{A}. Returns @math{1} if there is an error, @math{0} otherwise. The second output argument is a string giving brief informations about the error.
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn {dseries} {@var{B} = } cumsum (@var{A}[, @var{d}[, @var{v}]])
|
||||
|
||||
Overloads the Matlab/Octave @code{cumsum} function for @dseries objects. The cumulated sum cannot be computed if the variables in @dseries object @var{A} have @code{NaN}s. If a @dates object @var{d} is provided as a second argument, then the method computes the cumulated sum with the additional constraint that the variables in the @dseries object @var{B} are zero in period @var{d}. If a single observation @dseries object @var{v} is provided as a third argument, the cumulated sum in @var{B} is such that @code{B(d)} matches @var{v}.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts1 = dseries(ones(10,1));
|
||||
>> ts2 = ts1.cumsum();
|
||||
>> ts2
|
||||
|
||||
ts2 is a dseries object:
|
||||
|
||||
| cumsum(Variable_1)
|
||||
1Y | 1
|
||||
2Y | 2
|
||||
3Y | 3
|
||||
4Y | 4
|
||||
5Y | 5
|
||||
6Y | 6
|
||||
7Y | 7
|
||||
8Y | 8
|
||||
9Y | 9
|
||||
10Y | 10
|
||||
|
||||
>> ts3 = cumsum(dates('3Y'));
|
||||
>> ts3
|
||||
|
||||
ts3 is a dseries object:
|
||||
|
||||
| cumsum(Variable_1)
|
||||
1Y | -2
|
||||
2Y | -1
|
||||
3Y | 0
|
||||
4Y | 1
|
||||
5Y | 2
|
||||
6Y | 3
|
||||
7Y | 4
|
||||
8Y | 5
|
||||
9Y | 6
|
||||
10Y | 7
|
||||
|
||||
>> ts4 = ts1.cumsum(dates('3Y'),dseries(pi));
|
||||
>> ts4
|
||||
|
||||
ts4 is a dseries object:
|
||||
|
||||
| cumsum(Variable_1)
|
||||
1Y | 1.1416
|
||||
2Y | 2.1416
|
||||
3Y | 3.1416
|
||||
4Y | 4.1416
|
||||
5Y | 5.1416
|
||||
6Y | 6.1416
|
||||
7Y | 7.1416
|
||||
8Y | 8.1416
|
||||
9Y | 9.1416
|
||||
10Y | 10.1416
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{C} =} eq (@var{A}, @var{B})
|
||||
|
||||
Overloads the Matlab/Octave @code{eq} (equal, @code{==}) operator. @dseries objects @var{A} and @var{B} must have the same number of observations (say, @math{T}) and variables (@math{N}). The returned argument is a @math{T} by @math{N} matrix of zeros and ones. Element @math{(i,j)} of @var{C} is equal to @code{1} if and only if observation @math{i} for variable @math{j} in @var{A} and @var{B} are the same.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(2*ones(3,1));
|
||||
>> ts1 = dseries([2; 0; 2]);
|
||||
>> ts0==ts1
|
||||
|
||||
ans =
|
||||
|
||||
1
|
||||
0
|
||||
1
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{B} =} exp (@var{A})
|
||||
|
||||
Overloads the Matlab/Octave @code{exp} function for @dseries objects.
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(rand(10,1));
|
||||
>> ts1 = ts0.exp();
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{C} =} extract (@var{A}, @var{B}[, ]...)
|
||||
|
||||
Extracts some variables from a @dseries object @var{A} and returns a @dseries object @var{C}. The input arguments following @var{A} are strings representing the variables to be selected in the new @dseries object @var{C}. To simplify the creation of sub-objects, the @dseries class overloads the curly braces (@code{D = extract (A, B, C)} is equivalent to @code{D = A@{B,C@}}) and allows implicit loops (defined between a pair of @@ symbol, see examples below) or Matlab/Octave's regular expressions (introduced by square brackets).
|
||||
|
||||
@exampleshead
|
||||
|
||||
@noindent The following selections are equivalent:
|
||||
@example
|
||||
>> ts0 = dseries(ones(100,10));
|
||||
>> ts1 = ts0@{'Variable_1','Variable_2','Variable_3'@};
|
||||
>> ts2 = ts0@{'Variable_@@1,2,3@@'@}
|
||||
>> ts3 = ts0@{'Variable_[1-3]$'@}
|
||||
>> isequal(ts1,ts2) && isequal(ts1,ts3)
|
||||
|
||||
ans =
|
||||
|
||||
1
|
||||
@end example
|
||||
|
||||
@noindent It is possible to use up to two implicit loops to select variables:
|
||||
@example
|
||||
names = @{'GDP_1';'GDP_2';'GDP_3'; 'GDP_4'; 'GDP_5'; 'GDP_6'; 'GDP_7'; 'GDP_8'; ...
|
||||
'GDP_9'; 'GDP_10'; 'GDP_11'; 'GDP_12'; ...
|
||||
'HICP_1';'HICP_2';'HICP_3'; 'HICP_4'; 'HICP_5'; 'HICP_6'; 'HICP_7'; 'HICP_8'; ...
|
||||
'HICP_9'; 'HICP_10'; 'HICP_11'; 'HICP_12'@};
|
||||
|
||||
ts0 = dseries(randn(4,24),dates('1973Q1'),names);
|
||||
ts0@{'@@GDP,HICP@@_@@1,3,5@@'@}
|
||||
|
||||
ans is a dseries object:
|
||||
|
||||
| GDP_1 | GDP_3 | GDP_5 | HICP_1 | HICP_3 | HICP_5
|
||||
1973Q1 | 1.7906 | -1.6606 | -0.57716 | 0.60963 | -0.52335 | 0.26172
|
||||
1973Q2 | 2.1624 | 3.0125 | 0.52563 | 0.70912 | -1.7158 | 1.7792
|
||||
1973Q3 | -0.81928 | 1.5008 | 1.152 | 0.2798 | 0.88568 | 1.8927
|
||||
1973Q4 | -0.03705 | -0.35899 | 0.85838 | -1.4675 | -2.1666 | -0.62032
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{D} =} horzcat (@var{A}, @var{B}[, ]...)
|
||||
|
||||
Overloads the @code{horzcat} Matlab/Octave's method for @dseries objects. Returns a @dseries object @var{D} containing the variables in @dseries objects passed as inputs: @var{A}, @var{B}, ... If the inputs are not defined on the same time ranges, the method add @code{NaN}s to the variables so that the variables are redefined on the smallest common time range. Note that the names in the @dseries objects passed as inputs must be different and these objects must have common frequency.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(rand(5,2),'1950Q1',@{'nifnif';'noufnouf'@});
|
||||
>> ts1 = dseries(rand(7,1),'1950Q3',@{'nafnaf'@});
|
||||
>> ts2 = [ts0, ts1];
|
||||
>> ts2
|
||||
|
||||
ts2 is a dseries object:
|
||||
|
||||
| nifnif | noufnouf | nafnaf
|
||||
1950Q1 | 0.17404 | 0.71431 | NaN
|
||||
1950Q2 | 0.62741 | 0.90704 | NaN
|
||||
1950Q3 | 0.84189 | 0.21854 | 0.83666
|
||||
1950Q4 | 0.51008 | 0.87096 | 0.8593
|
||||
1951Q1 | 0.16576 | 0.21184 | 0.52338
|
||||
1951Q2 | NaN | NaN | 0.47736
|
||||
1951Q3 | NaN | NaN | 0.88988
|
||||
1951Q4 | NaN | NaN | 0.065076
|
||||
1952Q1 | NaN | NaN | 0.50946
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn {dseries} {@var{B} = } hpcycle (@var{A}[, @var{lambda}])
|
||||
|
||||
Extracts the cycle component from a @dseries @var{A} object using Hodrick Prescott filter and returns a @dseries object, @var{B}. Default value for @var{lambda}, the smoothing parameter, is @math{1600}.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
% Simulate a component model (stochastic trend, deterministic trend, and a
|
||||
% stationary autoregressive process).
|
||||
e = .2*randn(200,1);
|
||||
u = randn(200,1);
|
||||
stochastic_trend = cumsum(e);
|
||||
deterministic_trend = .1*transpose(1:200);
|
||||
x = zeros(200,1);
|
||||
for i=2:200
|
||||
x(i) = .75*x(i-1) + e(i);
|
||||
end
|
||||
y = x + stochastic_trend + deterministic_trend;
|
||||
|
||||
% Instantiates time series objects.
|
||||
ts0 = dseries(y,'1950Q1');
|
||||
ts1 = dseries(x,'1950Q1'); % stationary component.
|
||||
|
||||
% Apply the HP filter.
|
||||
ts2 = ts0.hpcycle();
|
||||
|
||||
% Plot the filtered time series.
|
||||
plot(ts1(ts2.dates).data,'-k'); % Plot of the stationary component.
|
||||
hold on
|
||||
plot(ts2.data,'--r'); % Plot of the filtered y.
|
||||
hold off
|
||||
axis tight
|
||||
id = get(gca,'XTick');
|
||||
set(gca,'XTickLabel',strings(ts.dates(id)));
|
||||
@end example
|
||||
|
||||
@iftex
|
||||
@sp 1
|
||||
The previous code should produce something like:
|
||||
@center
|
||||
@image{dynare.plots/HPCycle,11.32cm,7cm}
|
||||
@end iftex
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn {dseries} {@var{B} = } hptrend (@var{A}[, @var{lambda}])
|
||||
|
||||
Extracts the trend component from a @dseries @var{A} object using Hodrick Prescott filter and returns a @dseries object, @var{B}. Default value for @var{lambda}, the smoothing parameter, is @math{1600}.
|
||||
|
||||
@examplehead
|
||||
Using the same generating data process as in the previous example:
|
||||
@example
|
||||
ts1 = dseries(stochastic_trend + deterministic_trend,'1950Q1');
|
||||
% Apply the HP filter.
|
||||
ts2 = ts0.hptrend();
|
||||
|
||||
% Plot the filtered time series.
|
||||
plot(ts1.data,'-k'); % Plot of the nonstationary components.
|
||||
hold on
|
||||
plot(ts2.data,'--r'); % Plot of the estimated trend.
|
||||
hold off
|
||||
axis tight
|
||||
id = get(gca,'XTick');
|
||||
set(gca,'XTickLabel',strings(ts0.dates(id)));
|
||||
@end example
|
||||
|
||||
@iftex
|
||||
@sp 1
|
||||
The previous code should produce something like:
|
||||
@center
|
||||
@image{dynare.plots/HPTrend,11.32cm,7cm}
|
||||
@end iftex
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn {dseries} {@var{C} = } insert (@var{A}, @var{B}, @var{I})
|
||||
|
||||
Inserts variables contained in @dseries object @var{B} in @dseries object @var{A} at positions specified by integer scalars in vector @var{I}, returns augmented @dseries object @var{C}. The integer scalars in @var{I} must take values between @code{1} and @code{A.length()+1} and refers to @var{A}'s column numbers. The @dseries objects @var{A} and @var{B} need not to be defined over the same time ranges, but it is assumled that they have common frequency.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(ones(2,4),'1950Q1',{'Sly'; 'Gobbo'; 'Sneaky'; 'Stealthy'});
|
||||
>> ts1 = dseries(pi*ones(2,1),'1950Q1',{'Noddy'});
|
||||
>> ts2 = ts0.insert(ts1,3)
|
||||
|
||||
ts2 is a dseries object:
|
||||
|
||||
| Sly | Gobbo | Noddy | Sneaky | Stealthy
|
||||
1950Q1 | 1 | 1 | 3.1416 | 1 | 1
|
||||
1950Q2 | 1 | 1 | 3.1416 | 1 | 1
|
||||
|
||||
>> ts3 = dseries([pi*ones(2,1) sqrt(pi)*ones(2,1)],'1950Q1',{'Noddy';'Tessie Bear'});
|
||||
>> ts4 = ts0.insert(ts1,[3, 4])
|
||||
|
||||
ts4 is a dseries object:
|
||||
|
||||
| Sly | Gobbo | Noddy | Sneaky | Tessie Bear | Stealthy
|
||||
1950Q1 | 1 | 1 | 3.1416 | 1 | 1.7725 | 1
|
||||
1950Q2 | 1 | 1 | 3.1416 | 1 | 1.7725 | 1
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn {dseries} {@var{B} = } isempty (@var{A})
|
||||
|
||||
Overloads the Matlab/octave's @code{isempty} function. Returns @code{1} if @dseries object @var{A} is empty, @code{0} otherwise.
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn {dseries} {@var{C} = } isequal (@var{A},@var{B})
|
||||
|
||||
Overloads the Matlab/octave's @code{isequal} function. Returns @code{1} if @dseries objects @var{A} and @code{B} are identical, @code{0} otherwise.
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn {dseries} {@var{B} = } lag (@var{A}[, @var{p}])
|
||||
|
||||
Returns lagged time series. Default value of @var{p}, the number of lags, is @code{1}.
|
||||
|
||||
@exampleshead
|
||||
|
||||
@example
|
||||
>> ts0 = dseries(transpose(1:4),'1950Q1')
|
||||
|
||||
ts0 is a dseries object:
|
||||
|
||||
| Variable_1
|
||||
1950Q1 | 1
|
||||
1950Q2 | 2
|
||||
1950Q3 | 3
|
||||
1950Q4 | 4
|
||||
|
||||
>> ts1 = ts0.lag()
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| lag(Variable_1,1)
|
||||
1950Q1 | NaN
|
||||
1950Q2 | 1
|
||||
1950Q3 | 2
|
||||
1950Q4 | 3
|
||||
|
||||
>> ts2 = ts0.lag(2)
|
||||
|
||||
ts2 is a dseries object:
|
||||
|
||||
| lag(Variable_1,2)
|
||||
1950Q1 | NaN
|
||||
1950Q2 | NaN
|
||||
1950Q3 | 1
|
||||
1950Q4 | 2
|
||||
@end example
|
||||
|
||||
@noindent @dseries class overloads the parenthesis so that @code{ts.lag(p)} can be written more compactly as @code{ts(-p)}. For instance:
|
||||
|
||||
@example
|
||||
>> ts0.lag(1)
|
||||
|
||||
ans is a dseries object:
|
||||
|
||||
| lag(Variable_1,1)
|
||||
1950Q1 | NaN
|
||||
1950Q2 | 1
|
||||
1950Q3 | 2
|
||||
1950Q4 | 3
|
||||
@end example
|
||||
|
||||
@noindent or alternatively:
|
||||
|
||||
@example
|
||||
>> ts0(-1)
|
||||
|
||||
ans is a dseries object:
|
||||
|
||||
| lag(Variable_1,1)
|
||||
1950Q1 | NaN
|
||||
1950Q2 | 1
|
||||
1950Q3 | 2
|
||||
1950Q4 | 3
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn {dseries} {@var{B} = } lead (@var{A}[, @var{p}])
|
||||
|
||||
Returns leaded time series. Default value of @var{p}, the number of leads, is @code{1}. As for the @code{lag} method, the @dseries class overloads the parenthesis so that @code{ts.lead(p)} is equivalent to @code{ts(p)}.
|
||||
|
||||
@examplehead
|
||||
|
||||
@example
|
||||
>> ts0 = dseries(transpose(1:4),'1950Q1');
|
||||
>> ts1 = ts0.lead()
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| lead(Variable_1,1)
|
||||
1950Q1 | 2
|
||||
1950Q2 | 3
|
||||
1950Q3 | 4
|
||||
1950Q4 | NaN
|
||||
|
||||
>> ts2 = ts0(2)
|
||||
|
||||
ts2 is a dseries object:
|
||||
|
||||
| lead(Variable_1,2)
|
||||
1950Q1 | 3
|
||||
1950Q2 | 4
|
||||
1950Q3 | NaN
|
||||
1950Q4 | NaN
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@noindent @remarkhead
|
||||
|
||||
@noindent The overloading of the parenthesis for @dseries objects, allows to easily create new @dseries objects by copying/pasting equations declared in the @code{model} block. For instance, if an Euler equation is defined in the @code{model} block:
|
||||
@example
|
||||
model;
|
||||
...
|
||||
1/C - beta/C(1)*(exp(A(1))*K^(alpha-1)+1-delta) ;
|
||||
...
|
||||
end;
|
||||
@end example
|
||||
@noindent and if variables @var{C}, @var{A} and @var{K} are defined as @dseries objects, then by writting:
|
||||
@example
|
||||
Residuals = 1/C - beta/C(1)*(exp(A(1))*K^(alpha-1)+1-delta) ;
|
||||
@end example
|
||||
@noindent outside of the @code{model} block, we create a new @dseries object, called @var{Residuals}, for the residuals of the Euler equation (the conditional expectation of the equation defined in the @code{model} block is zero, but the residuals are non zero).
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{B} =} log (@var{A})
|
||||
|
||||
Overloads the Matlab/Octave @code{log} function for @dseries objects.
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(rand(10,1));
|
||||
>> ts1 = ts0.log();
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{C} =} merge (@var{A}, @var{B})
|
||||
|
||||
Merges two @dseries objects @var{A} and @var{B} in @dseries object @var{C}. Objects @var{A} and @var{B} need to have common frequency but can be defined on different time ranges. If a variable, say @code{x}, is defined both in @dseries objects @var{A} and @var{B}, then the merge will select the variable @code{x} as defined in the second input argument, @var{B}.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(rand(3,2),'1950Q1',{'A1';'A2'})
|
||||
|
||||
ts0 is a dseries object:
|
||||
|
||||
| A1 | A2
|
||||
1950Q1 | 0.42448 | 0.92477
|
||||
1950Q2 | 0.60726 | 0.64208
|
||||
1950Q3 | 0.070764 | 0.1045
|
||||
|
||||
>> ts1 = dseries(rand(3,1),'1950Q2',{'A1'})
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| A1
|
||||
1950Q2 | 0.70023
|
||||
1950Q3 | 0.3958
|
||||
1950Q4 | 0.084905
|
||||
|
||||
>> merge(ts0,ts1)
|
||||
|
||||
ans is a dseries object:
|
||||
|
||||
| A1 | A2
|
||||
1950Q1 | NaN | 0.92477
|
||||
1950Q2 | 0.70023 | 0.64208
|
||||
1950Q3 | 0.3958 | 0.1045
|
||||
1950Q4 | 0.084905 | NaN
|
||||
|
||||
>> merge(ts1,ts0)
|
||||
|
||||
ans is a dseries object:
|
||||
|
||||
| A1 | A2
|
||||
1950Q1 | 0.42448 | 0.92477
|
||||
1950Q2 | 0.60726 | 0.64208
|
||||
1950Q3 | 0.070764 | 0.1045
|
||||
1950Q4 | NaN | NaN
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{C} =} minus (@var{A}, @var{B})
|
||||
|
||||
Overloads the @code{minus} (@code{-}) operator for @dseries objects, element by element substraction. If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then @math{N_A} must be equal to @math{N_B} or @math{1} and @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{minus} operator will compute for each couple @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)-B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, the @code{minus} operator will substract the variable defined in @var{B} to each variable in @var{A}. If @var{B} is a double scalar, then the method @code{minus} will substract @var{B} to all the observations/variables in @var{A}.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(rand(3,2));
|
||||
>> ts1 = ts0@{'Variable_2'@};
|
||||
>> ts0-ts1
|
||||
|
||||
ans is a dseries object:
|
||||
|
||||
| minus(Variable_1,Variable_2) | minus(Variable_2,Variable_2)
|
||||
1Y | -0.48853 | 0
|
||||
2Y | -0.50535 | 0
|
||||
3Y | -0.32063 | 0
|
||||
|
||||
>> ts1
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| Variable_2
|
||||
1Y | 0.703
|
||||
2Y | 0.75415
|
||||
3Y | 0.54729
|
||||
|
||||
>> ts1-ts1.data(1)
|
||||
|
||||
ans is a dseries object:
|
||||
|
||||
| minus(Variable_2,0.703)
|
||||
1Y | 0
|
||||
2Y | 0.051148
|
||||
3Y | -0.15572
|
||||
|
||||
>> ts1.data(1)-ts1
|
||||
|
||||
ans is a dseries object:
|
||||
|
||||
| minus(0.703,Variable_2)
|
||||
1Y | 0
|
||||
2Y | -0.051148
|
||||
3Y | 0.15572
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{C} =} mrdivide (@var{A}, @var{B})
|
||||
|
||||
Overloads the @code{mrdivide} (@code{/}) operator for @dseries objects, element by element division (like the @code{./} Matlab/Octave operator). If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then @math{N_A} must be equal to @math{N_B} or @math{1} and @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{mrdivide} operator will compute for each couple @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)/B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, @code{mrdivides} operator will divide each variable defined in @var{A} by the variable in @var{B}, observation per observation. If @var{B} is a double scalar, then the method @code{mrdivide} will divide all the observations/variables in @var{A} by @var{B}.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(rand(3,2))
|
||||
|
||||
ts0 is a dseries object:
|
||||
|
||||
| Variable_1 | Variable_2
|
||||
1Y | 0.72918 | 0.90307
|
||||
2Y | 0.93756 | 0.21819
|
||||
3Y | 0.51725 | 0.87322
|
||||
|
||||
>> ts1 = ts0@{'Variable_2'@};
|
||||
>> ts0/ts1
|
||||
|
||||
ans is a dseries object:
|
||||
|
||||
| divide(Variable_1,Variable_2) | divide(Variable_2,Variable_2)
|
||||
1Y | 0.80745 | 1
|
||||
2Y | 4.2969 | 1
|
||||
3Y | 0.59235 | 1
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{C} =} mtimes (@var{A}, @var{B})
|
||||
|
||||
Overloads the @code{mtimes} (@code{*}) operator for @dseries objects, Hadammard product (the @code{.*} Matlab/Octave operator). If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then @math{N_A} must be equal to @math{N_B} or @math{1} and @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{mtimes} operator will compute for each couple @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)*B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, @code{mtimes} operator will multiply each variable defined in @var{A} by the variable in @var{B}, observation per observation. If @var{B} is a double scalar, then the method @code{mtimes} will multiply all the observations/variables in @var{A} by @var{B}.
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{C} =} ne (@var{A}, @var{B})
|
||||
|
||||
Overloads the Matlab/Octave @code{ne} (equal, @code{~=}) operator. @dseries objects @var{A} and @var{B} must have the same number of observations (say, @math{T}) and variables (@math{N}). The returned argument is a @math{T} by @math{N} matrix of zeros and ones. Element @math{(i,j)} of @var{C} is equal to @code{1} if and only if observation @math{i} for variable @math{j} in @var{A} and @var{B} are not equal.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(2*ones(3,1));
|
||||
>> ts1 = dseries([2; 0; 2]);
|
||||
>> ts0~=ts1
|
||||
|
||||
ans =
|
||||
|
||||
0
|
||||
1
|
||||
0
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{C} =} plus (@var{A}, @var{B})
|
||||
|
||||
Overloads the @code{plus} (@code{+}) operator for @dseries objects, element by element addition. If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then @math{N_A} must be equal to @math{N_B} or @math{1} and @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{minus} operator will compute for each couple @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)+B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, the @code{plus} operator will add the variable defined in @var{B} to each variable in @var{A}. If @var{B} is a double scalar, then the method @code{add} will add @var{B} to all the observations/variables in @var{A}.
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{B} =} pop (@var{A}[, @var{a}])
|
||||
|
||||
Removes variable @var{a} from @dseries object @var{A}. By default, if the second argument is not provided, the last variable is removed.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(ones(3,3));
|
||||
>> ts1 = ts0.pop('Variable_2');
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| Variable_1 | Variable_3
|
||||
1Y | 1 | 1
|
||||
2Y | 1 | 1
|
||||
3Y | 1 | 1
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{B} =} qdiff (@var{A})
|
||||
@deftypefnx{dseries} {@var{B} =} qgrowth (@var{A})
|
||||
|
||||
Computes quaterly differences or growth rates.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(transpose(1:4),'1950Q1');
|
||||
>> ts1 = ts0.qdiff()
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| qdiff(Variable_1)
|
||||
1950Q1 | NaN
|
||||
1950Q2 | 1
|
||||
1950Q3 | 1
|
||||
1950Q4 | 1
|
||||
|
||||
>> ts0 = dseries(transpose(1:6),'1950M1');
|
||||
>> ts1 = ts0.qdiff()
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| qdiff(Variable_1)
|
||||
1950M1 | NaN
|
||||
1950M2 | NaN
|
||||
1950M3 | NaN
|
||||
1950M4 | 3
|
||||
1950M5 | 3
|
||||
1950M6 | 3
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{B} =} rename (@var{A},@var{oldname},@var{newname})
|
||||
|
||||
Rename variable @var{oldname} to @var{newname} in @dseries object @var{A}, returns a @dseries object.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(ones(2,2));
|
||||
>> ts1 = ts0.rename('Variable_1','Stinkly')
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| Stinkly | Variable_2
|
||||
1Y | 1 | 1
|
||||
2Y | 1 | 1
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} save (@var{A}[, @var{basename}[, @var{format}]])
|
||||
|
||||
Overloads the Matlab/Octave @code{save} function, saves @dseries object @var{A} to disk. Possible formats are @code{csv} (this is the default), @code{m} (Matlab/Octave script), and @code{mat} (Matlab binary data file). The name of the file without extension is specified by @var{basename}, by default @var{basename} is the name of the first input (namely, the @dseries object @var{A}).
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(ones(2,2));
|
||||
>> ts0.save();
|
||||
@end example
|
||||
@noindent The last command will create a file @code{ts0.csv} with the following content:
|
||||
@example
|
||||
,Variable_1,Variable_2
|
||||
1Y, 1, 1
|
||||
2Y, 1, 1
|
||||
@end example
|
||||
To create a Matlab/octave script, the following command:
|
||||
@example
|
||||
>> ts0.save([],'m');
|
||||
@end example
|
||||
will produce a file @code{ts0.m} with the following content:
|
||||
@example
|
||||
% File created on 14-Nov-2013 12:08:52.
|
||||
|
||||
FREQ__ = 1;
|
||||
INIT__ = ' 1Y';
|
||||
|
||||
NAMES__ = {'Variable_1'; 'Variable_2'};
|
||||
TEX__ = {'Variable_{1}'; 'Variable_{2}'};
|
||||
|
||||
Variable_1 = [
|
||||
1
|
||||
1];
|
||||
|
||||
Variable_2 = [
|
||||
1
|
||||
1];
|
||||
@end example
|
||||
@noindent The generated (@code{csv}, @code{m}, or @code{mat}) files can be loaded when instantiating a @dseries object as explained above.
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{B} =} set_names (@var{A}, @var{s1}, @var{s2}, ...)
|
||||
|
||||
Renames variables in @dseries object @var{A}, returns a @dseries object @var{B} with new names @var{s1}, @var{s2}, @var{s3}, ... The number of input arguments after the first one (@dseries object @var{A}) must be equal to @code{A.vobs} (the number of variables in @var{A}). @var{s1} will be the name of the first variable in @var{B}, @var{s2} the name of the second variable in @var{B}, and so on.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(ones(1,3));
|
||||
>> ts1 = ts0.set_names('Barbibul',[],'Barbouille')
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| Barbibul | Variable_2 | Barbouille
|
||||
1Y | 1 | 1 | 1
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {[@var{T}, @var{N} ] = } size (@var{A}[, @var{dim}])
|
||||
|
||||
Overloads the Matlab/Octave's @code{size} function. Returns the number of observations in @dseries object @var{A} (@emph{ie} @code{A.nobs}) and the number of variables (@emph{ie} @code{A.vobs}). If a second input argument is passed, the @code{size} function returns the number of observations if @code{dim=1} or the number of variables if @code{dim=2} (for all other values of @var{dim} an error is issued).
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(ones(1,3));
|
||||
>> ts0.size()
|
||||
|
||||
ans =
|
||||
|
||||
1 3
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{B} =} tex_rename (@var{A},@var{name},@var{newtexname})
|
||||
|
||||
Redefines the tex name of variable @var{name} to @var{newtexname} in @dseries object @var{A}, returns a @dseries object.
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{B} =} uminus (@var{A})
|
||||
|
||||
Overloads @code{uminus} (@code{-}, unary minus) for @dseries object.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(1)
|
||||
|
||||
ts0 is a dseries object:
|
||||
|
||||
| Variable_1
|
||||
1Y | 1
|
||||
|
||||
>> ts1 = -ts0
|
||||
|
||||
ts1 is a dseries object:
|
||||
|
||||
| -Variable_1
|
||||
1Y | -1
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
@deftypefn{dseries} {@var{D} =} vertcat (@var{A}, @var{B}[, ]...)
|
||||
|
||||
Overloads the @code{vertcat} Matlab/Octave's method for @dseries objects. This method is used to append more observations to a @dseries object. Returns a @dseries object @var{D} containing the variables in @dseries objects passed as inputs. All the input arguments must be @dseries objects with the same variables defined on @emph{different time ranges}.
|
||||
|
||||
@examplehead
|
||||
@example
|
||||
>> ts0 = dseries(rand(2,2),'1950Q1',@{'nifnif';'noufnouf'@});
|
||||
>> ts1 = dseries(rand(2,2),'1950Q3',@{'nifnif';'noufnouf'@});
|
||||
>> ts2 = [ts0; ts1]
|
||||
|
||||
ts2 is a dseries object:
|
||||
|
||||
| nifnif | noufnouf
|
||||
1950Q1 | 0.82558 | 0.31852
|
||||
1950Q2 | 0.78996 | 0.53406
|
||||
1950Q3 | 0.089951 | 0.13629
|
||||
1950Q4 | 0.11171 | 0.67865
|
||||
@end example
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@deftypefn{dseries} {@var{B} =} ydiff (@var{A})
|
||||
@deftypefnx{dseries} {@var{B} =} ygrowth (@var{A})
|
||||
|
||||
Computes yearly differences or growth rates.
|
||||
|
||||
@end deftypefn
|
||||
|
||||
@sp 1
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@node Reporting
|
||||
@chapter Reporting
|
||||
|
|
|
|||
Loading…
Reference in New Issue