I am attempting to forecast lCCI using the variable 'UKIS' from the start of my dataset to see how well it predicts the level of lCCI compared to its actual values.
I have fitted an arima (3,0,3) model and predicted the residuals
.
I am struggling with how to now forecast this one month ahead (I have monthly data).
I am currently using
, where '528' is the value of the month at the start of my dataset.
Data:
ldiffGDP - ldiffjobclaim are my control variables and ukis01 is my independent.
I have fitted an arima (3,0,3) model and predicted the residuals
Code:
predict resid, y
I am struggling with how to now forecast this one month ahead (I have monthly data).
I am currently using
Code:
predict lCCI_for, dynamic(tq(528)) y
Data:
Code:
input float(mdate lCCI ukis01) double ldiffGDP float(ldiffprodindex ldiffjobclaim resid) 528 . . . . . . 529 .0005973233 . . -.0019821613 -.007927245 . 530 .0004273768 . . .011834458 -.012520608 . 531 -.0013833106 . . -.00294551 -.010382888 . 532 -.0022602894 . . -.014859115 -.015954096 . 533 -.0015312182 . . -.002998503 -.013138019 . 534 -.0003333058 . . -.010060447 -.012475656 . 535 -.00007342696 -.02866241 . -.01220768 -.0022742245 . 536 .0004801298 -.04280979 . -.0020491811 -.001919156 . 537 .0018597638 -.1807404 . .008171649 -.0016822884 . 538 .0020786687 .2073853 . .005073578 -.002408189 . 539 .0011294137 .16495016 . -.00609139 -.004107278 . 540 .001232658 .08972668 .0024401894619012765 0 -.002909446 -.0006288217 541 .0014457434 -.3914609 .0024401894619012765 -.0010188487 -.002431021 .0008937875 542 .0006148703 .4111633 .0024401894619012765 -.022681385 .01017081 .000921642 543 -.0010755953 .05701828 .0024401894619012765 .03282346 .012212795 .000656313 544 -.0017815884 -.13246275 .0024401894619012765 -.006072893 .016289312 -.002651216 545 -.0011082797 .01433666 .0024401894619012765 .013111635 .010598131 -.0008489126 546 .00008195838 .04890225 .0024401894619012765 -.007038742 .004046482 -.00053401146 547 -.0002557655 .22815663 .0024401894619012765 -.008105414 .0017292068 .0008285991 548 -.001205502 -.09737428 .0024401894619012765 .0030472344 .0090581365 -.000928288 549 -.001842799 -.11818444 .0024401894619012765 -.007124712 .013716703 -.0021062884 550 -.0013094485 .1031033 .0024401894619012765 .005094254 .01419732 -.001616784 551 -.00020532635 .13108149 .0024401894619012765 .032002732 .007960241 .00013430763 552 .0011481659 .4272834 .0007919922406749436 -.01487385 .005928871 -.00022189216 553 .0005983724 .004493982 .0007919922406749436 .004982571 .011212222 .0020563754 554 -.0005607226 .065831155 .0007919922406749436 .00890654 .01290618 -.0002169882 555 .0003953123 -.04363072 .0007919922406749436 -.0009857073 .01020634 -.0014311324 556 -.0003784535 -.04848986 .0007919922406749436 .002954212 .005695617 .0015527784 557 -.0004965832 -.13445634 .0007919922406749436 .00685941 .005140321 -.0007500609 558 -.0007646015 .016451325 .0007919922406749436 -.00685941 .0010458064 -.0004435997 559 .00003966318 -.29379946 .0007919922406749436 .009784814 -.005555279 -.00055281084 560 .0010976823 -.15728237 .0007919922406749436 .005825259 .007017573 .0007821496 561 .0015477184 .021955494 .0007919922406749436 -.005825259 .0004174058 .001351063 562 -.0008126955 .1018508 .0007919922406749436 .002916871 -.009750038 .001582487 563 -.0021065192 -.04000555 .0007919922406749436 -.00486619 -.009846038 -.0027224284 564 -.002473094 -.10547047 .0036497908678221336 .0009751341 -.01533131 -.0018734855 565 -.00019007115 .02508106 .0036497908678221336 -.00586512 -.015350582 -.0020144014 566 .0012628704 -.036156137 .0036497908678221336 -.0039292783 -.016258722 .0017346367 567 .002423856 .029814754 .0036497908678221336 -.009891277 -.020958513 .00122914 568 .002830332 .10273252 .0036497908678221336 .01577942 -.008462029 .00272697 569 .0008863329 -.015873242 .0036497908678221336 -.01876598 -.010273075 .0013612005
