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ws8 - Regressie analyse van een tijdreeks (History)

*The author of this computation has been verified*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Sat, 27 Nov 2010 11:35:43 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk.htm/, Retrieved Sat, 27 Nov 2010 12:34:22 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
264.6 280.7 235.1 240.7 264.6 280.7 201.4 240.7 264.6 240.8 201.4 240.7 241.1 240.8 201.4 223.8 241.1 240.8 206.1 223.8 241.1 174.7 206.1 223.8 203.3 174.7 206.1 220.5 203.3 174.7 299.5 220.5 203.3 347.4 299.5 220.5 338.3 347.4 299.5 327.7 338.3 347.4 351.6 327.7 338.3 396.6 351.6 327.7 438.8 396.6 351.6 395.6 438.8 396.6 363.5 395.6 438.8 378.8 363.5 395.6 357 378.8 363.5 369 357 378.8 464.8 369 357 479.1 464.8 369 431.3 479.1 464.8 366.5 431.3 479.1 326.3 366.5 431.3 355.1 326.3 366.5 331.6 355.1 326.3 261.3 331.6 355.1 249 261.3 331.6 205.5 249 261.3 235.6 205.5 249 240.9 235.6 205.5 264.9 240.9 235.6 253.8 264.9 240.9 232.3 253.8 264.9 193.8 232.3 253.8 177 193.8 232.3 213.2 177 193.8 207.2 213.2 177 180.6 207.2 213.2 188.6 180.6 207.2 175.4 188.6 180.6 199 175.4 188.6 179.6 199 175.4 225.8 179.6 199 234 225.8 179.6 200.2 234 225.8 183.6 200.2 234 178.2 183.6 200.2 203.2 178.2 183.6 208.5 203.2 178.2 191.8 208.5 203.2 172.8 191.8 208.5 148 172.8 191.8 etc...
 
Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 9.58964637151776 + 1.04580934007148Y1[t] -0.0719625816414837Y2[t] -26.1109600817039M1[t] -41.0618474924478M2[t] -22.5257196733794M3[t] + 68.850375119617M4[t] -31.4842555305213M5[t] -33.610105974548M6[t] -19.8116313220874M7[t] -19.6330828306989M8[t] + 17.3278641633569M9[t] -4.42597633795568M10[t] + 68.8787515530213M11[t] + 0.0283166849034852t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)9.589646371517766.6590391.44010.1507220.075361
Y11.045809340071480.05276919.818500
Y2-0.07196258164148370.052769-1.36370.1735140.086757
M1-26.11096008170396.917593-3.77460.0001889.4e-05
M2-41.06184749244787.875401-5.213900
M3-22.52571967337948.454619-2.66430.0080670.004033
M468.8503751196177.8148948.810100
M5-31.48425553052136.018376-5.231400
M6-33.6101059745487.88608-4.2622.6e-051.3e-05
M7-19.81163132208748.173684-2.42380.0158570.007928
M8-19.63308283069897.703787-2.54850.0112390.00562
M917.32786416335697.6626722.26130.0243430.012172
M10-4.425976337955686.635942-0.6670.5052240.252612
M1168.87875155302137.1614629.61800
t0.02831668490348520.0165031.71590.0870620.043531


Multiple Linear Regression - Regression Statistics
Multiple R0.98890473317585
R-squared0.9779325712976
Adjusted R-squared0.977062306503702
F-TEST (value)1123.71841094241
F-TEST (DF numerator)14
F-TEST (DF denominator)355
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation23.4963888187043
Sum Squared Residuals195988.502069505


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1264.6260.1472817888684.45271821113211
2240.7225.10568696502515.5943130349745
3201.4219.833885805717-18.4338858057166
4240.8271.857895920039-31.0578959200389
5241.1215.58459941213125.5154005878694
6223.8210.96548273835412.8345172616456
7206.1206.678183717989-0.578183717989501
8174.7189.619176237414-14.919176237414
9203.3195.0437643331838.2562356668168
10220.5205.48801270636115.0119872936391
11299.5294.7508480965244.74915190347554
12347.4307.2815946898240.1184053101802
13338.3325.60817473276612.6918252672342
14327.7297.72173135164829.978268648352
15351.6305.855456343845.7445436562004
16396.6423.017514414808-26.4175144148076
17438.8368.05271505155870.7472849484422
18395.6406.850019269584-11.2500192695842
19363.5372.46102617059-8.96102617058989
20378.8342.206195057536.5938049425005
21357397.506340510244-40.506340510244
22369351.88114558116217.118854418838
23464.8439.33268651768525.4673134823154
24479.1469.8072354487169.29276455128356
25431.3451.785650293684-20.485650293684
26366.5385.844328194954-19.3443281949537
27326.3340.080138864757-13.7801388647568
28355.1394.106190162151-39.0061901621514
29331.6326.8120809729634.78791902703717
30261.3298.065505370885-36.7655053708852
31249240.0630207697998.93697923020068
32205.5232.465400552608-26.9654005526084
33235.6224.84709769264910.7529023073513
34240.9237.7308073137963.16919268620445
35264.9314.440567684646-49.5405676846463
36253.8270.308155295544-16.508155295544
37232.3230.8899262645551.41007373544549
38193.8194.281239383398-0.481239383397823
39177174.129219799912.87078020009025
40213.2250.734593757806-37.5345937578059
41207.2189.49554927473517.7044507252645
42180.6178.5181140197622.08188598023824
43188.6164.95815240107423.6418475989265
44175.4175.445696969601-0.0456969696007394
45199198.0545767064850.945423293515333
46179.6201.96005939343-22.36005939343
47225.8253.306085845185-27.5060858451848
48234234.168116572214-0.168116572213999
49200.2213.336438492163-13.1364384921631
50183.6162.47541890244721.1245810975534
51178.2166.11176362071412.0882363792859
52203.2253.063383517477-49.8633835174766
53208.5179.29090099489329.2090990051072
54191.8180.93709219711110.8629078028887
55172.8176.917465872582-4.11746587258193
56148158.455728700929-10.4557287009286
57159.4170.876209797303-11.4762097973034
58154.5162.857584482418-8.35758448241795
59213.2230.245789861235-17.0457898612354
60196.4223.136979905356-26.7369799053564
61182.8175.2605360537.53946394699994
62176.4147.32392967376529.0760703262355
63153.6160.173885511603-6.57388551160315
64173.2228.194404558379-54.9944045583788
65171150.02670051997120.9732994800292
66151.2144.2179196125176.98208038748275
67161.9137.49600369607724.4039963039226
68157.2150.3178879276366.88211207236441
69201.7181.62184808469520.078151915305
70236.4206.77306403518229.6269359648184
71356.1313.19335782849642.9066421715036
72398.3367.02919938397531.2708006160251
73403.7376.46578911570527.2342108842948
74384.6364.1537678809820.4462321190199
75365.8362.3546560487233.44534395127718
76368.1435.472337242631-67.3723372426312
77367.9338.92428129442128.9757187055792
78347336.45207172950810.5479282704922
79343.3328.43584037570614.8641596242936
80292.9326.277228950041-33.377228950041
81311.5310.8239634414710.676036558528796
82300.9312.177407465122-11.2774074651224
83366.9373.086369017714-6.1863690177136
84356.9374.022153959713-17.122153959713
85329.7332.73188677386-3.03188677385978
86316.2290.0829278144926.1170721855099
87269296.486328448145-27.4863284481454
88289.3339.500033926832-50.2000339268315
89266.2263.8202834185262.37971658147422
90253.6236.10371349642917.4962865035707
91233.8238.415642784811-4.61564278481106
92228.4218.8222115563719.5777884436295
93253.6251.5889639154452.01103608455478
94260.1256.6064334097013.49356659029871
95306.6334.923781638681-28.323781638681
96309.2314.235724303217-5.03572430321728
97309.5287.52592514427421.9740748557264
98271272.729994508187-1.72999450818682
99279.9251.00919064491428.8908093550856
100317.9354.491864642647-36.5918646426475
101298.4293.285838623525.11416137648034
102246.7268.060444630626-21.3604446306262
103227.3229.222163428304-1.9221634283039
104209.1212.860792878074-3.76079287807397
105259.9232.21240065157727.6875993484228
106266264.9237102966741.07628970332592
107320.6340.980492699603-20.3804926996032
108308.5328.792276051375-20.292276051375
109282.2286.126182682085-3.92618268208466
110262.7244.56957355022618.1304264497737
111263.5244.63335181997518.8666481800246
112313.1338.277681111941-25.1776811119414
113284.3289.785940348939-5.48594034893874
114252.6253.999753546339-1.39975354633939
115250.3236.74691115471213.5530888452876
116246.5236.8296286868759.67037131312497
117312.7270.01033081133842.6896691886619
118333.2317.79084311789815.4091568821016
119446.4407.79905626057838.600943739422
120511.6455.85900576490155.7409942350991
121515.5489.81696709894525.6830329010551
122506.4474.28109247635832.1189075236415
123483.2483.0480179172780.151982082721847
124522.3550.844512198457-28.5445121984572
125509.8493.098875324116.7011246759004
126460.7475.114987871901-14.414987871901
127405.8438.492072882274-32.6920728822741
128375384.817368047239-9.81736804723884
129378.5393.546449784114-15.0464497841141
130406.8377.69770617251329.1022938274872
131467.8480.375286036671-12.5752860366709
132469.8473.282679852459-3.48267985245919
133429.8444.901937655671-15.1019376556712
134355.8388.003068163689-32.2030681636887
135332.7332.0561247680310.64387523196934
136378404.627571531749-26.6275715317491
137360.5353.3587563076717.14124369232943
138334.7329.6996541489375.00034585106267
139319.5317.8039096911831.69609030881672
140323.1303.97110750473919.1288924952609
141363.6345.81911604890617.7808839510937
142352.1366.189805211483-14.0898052114826
143411.9424.581557820061-12.6815578200611
144388.6419.098091177095-30.4980911770946
145416.4364.34472777446852.055272225532
146360.7380.172384854861-19.4723848548612
147338338.484689347219-0.484689347218579
148417.2410.1575446029277.04245539707345
149388.4394.312880974614-5.91288097461446
150371.1356.39660175542714.7033982445729
151331.5354.20341386083-22.7034138608295
152353.7314.24118183268939.4588181673113
153396.7377.29713109423719.4028689057625
154447398.94383958846148.0561604115391
155533.5521.78670295935311.713297040647
156565.4539.77905815085125.6209418491487
157542.3540.8329693903431.46703060965743
158488.7499.456596554488-10.7565965544877
159467.1463.6279960665473.47200393345332
160531.3536.300120174886-5.00012017488623
161496.1504.689157605696-8.5891576056962
162444461.159137334674-17.1591373346739
163403.4423.032344928094-19.6323449280942
164386.3384.5286014010061.7713985989945
165394.1406.556206179387-12.4562061793868
166404.1394.2185553616059.88144463839545
167462.1477.448385201396-15.3483852013963
168448.1468.535266241009-20.4352662410092
169432.3423.6374623480028.66253765199795
170386.3393.198580192013-6.89858019201308
171395.2364.79280384263230.4071961573675
172421.9468.815197202677-46.9151972026767
173382.9395.791525640741-12.8915256407412
174384.2350.98602668900333.2139733109972
175345.5368.978910852478-23.4789108524776
176323.4328.61940321187-5.21940321186956
177372.6345.28123238477527.3187676152254
178376376.599901154159-0.599901154158967
179462.7449.94813846952112.7518615304785
180487471.5247006080215.4752993919804
181444.2464.616068346639-20.4160683466395
182399.3403.184167131852-3.88416713185174
183394.9377.8717707608717.0282292391301
184455.4467.905741058158-12.5057410581578
185414431.18752752647-17.1875275264699
186375.5381.439750899078-5.93975089907781
187347357.982133523647-10.9821335236475
188339.4331.1539919010998.2460080989005
189385.8362.24603817229823.5539618277022
190378.8389.592983355681-10.7929833556805
191451.8452.266298762896-0.46629876289586
192446.1460.263683791486-14.1636837914862
193422.5422.96665869645-0.46665869645004
194383.1383.773174260279-0.673174260279187
195352.8362.831047692174-10.0310476921739
196445.3425.38276188258219.9172381174175
197367.5423.994278097696-56.4942780976963
198355.1333.87623887917521.2237611208251
199326.2340.33368325136-14.1336832513602
200319.8311.2089945119418.59100548805917
201331.8343.584797023882-11.7847970238816
202340.9334.8695458108366.03045418916432
203394.1416.855904401669-22.7559044016688
204417.2402.98766693241614.2123330675839
205369.9397.23480994794-27.3348099479398
206349.2331.183121800818.0168781991998
207321.4331.503143076935-10.1031430769347
208405.7395.32368034082610.3763196591738
209342.9385.17965351325-42.2796535132502
210316.5311.3388475652615.16115243473887
211284.2302.075522451823-17.8755224518235
212270.9270.4025580991420.497441900858092
213288.8295.80694894217-7.00694894217043
214278.8293.758514648873-14.9585146488725
215324.4355.345335612656-30.9453356126557
216310.9334.903432468212-24.003432468212
217299291.4208692575957.57913074240506
218273265.0246622370647.97533776293598
219279.3257.25441862071122.0455813792889
220359.2357.118456063742.08154393626008
221305339.918944105875-34.9189441058748
222282.1275.3887338417236.71126615827703
223250.3269.166863216419-18.8668632164187
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228318.4327.238993050637-8.83899305063654
229295.4300.215921470925-4.81592147092548
230266.4261.0598294693375.34017053066261
231245.8250.950942488991-5.15094248899053
232362.8322.89859642902139.901403570979
233324.9346.434404433964-21.5344044339637
234294.2296.281074634078-2.08107463407784
235289.5280.728901075468.77109892454014
236295.2278.22971360980916.9702863901905
237290.3321.518314660891-31.2183146608911
238272294.258138362775-22.2581383627753
239307.4348.805488665391-41.4054886653911
240328.7318.29361967984310.4063803201574
241292.9311.939239836456-19.0392398364562
242249.1258.043891747093-8.9438917470932
243230.4233.3781475787-2.97814757869951
244361.5308.3778854731653.1221145268402
245321.7346.522876267991-24.8228762679914
246277.2293.367836320825-16.1678363208249
247260.7263.520222774339-2.82022277433933
248251249.6735687224981.32643127750206
249257.6277.705864399848-20.1058643998484
250241.8263.580719269833-21.7807192698334
251287.5319.915023233751-32.4150232337508
252292.3299.995083996835-7.69508399683487
253274.7275.643635451362-0.943635451361715
254254.2241.96939994838412.2306000516158
255230240.361294417781-10.3612944177809
256339307.93235278960131.0676472103986
257318.2323.360751367882-5.16075136788159
258287291.66646193635-4.66646193634991
259295.8274.36082356162721.4391764383732
260284286.016043477762-2.01604347776211
261271310.031486225433-39.0314862254329
262262.7275.559599451464-12.8595994514641
263340.6341.147940066091-0.54794006609057
264379.4354.36334221716525.0366577828348
265373.3363.25221610526610.0477838947336
266355.2339.158060237316.0419397626995
267338.4339.232327433992-0.832327433991652
268466.9414.36966472640152.5303352735985
269451449.6588223319281.34117766807152
270422421.6857283247380.314271675261819
271429.2406.32825384812922.871746151871
272425.9416.1518611405399.7481388594613
273460.7449.17182340944311.5281765905566
274463.6464.077941146939-0.477941146938563
275541.4537.9395349679033.46046503209719
276544.2550.244375270585-6.04437527058543
277517.5521.491309174278-3.99130917427778
278469.4478.444133839933-9.04413383993277
279439.4448.626550016294-9.22655001629422
280549512.11808146900536.8819185309949
281533528.5913486248494.40865137515126
282506.1501.8737664766754.22623352332471
283484488.71968787238-4.71968787238047
284457467.749960079249-10.7499600792487
285481.5478.0927446305553.40725536944512
286469.5483.932539350217-14.432539350217
287544.7542.9527885950231.7472114049766
288541.2553.610767079978-12.4107670799785
289521.5518.4562048534883.04379514651172
290469.7483.183059163985-13.4830591639849
291434.4448.992242710592-14.5922427105916
292542.6507.20724621299735.3927537870029
293517.3522.597781975441-5.2977819754406
294485.7486.2549205789-0.554920578900396
295465.8468.854790085535-3.05479008553541
296447450.524066974276-3.52406697427591
297426.6469.284170434557-42.6841704345569
298411.6427.57703261555-15.9770326155496
299467.5486.690973755844-19.1909737558442
300484.5477.3807197223447.11928027765585
301451.2465.054126793-13.8541267929999
302417.4414.0827411548743.31725884512595
303379.9399.695183933091-19.7951839330914
304484.7454.31408041779330.385919582207
305455466.307182103605-11.3071821036047
306420.8425.607432388331-4.80743238833114
307416.5405.80483297000310.6951670299972
308376.3403.975838276126-27.6758382761262
309405.6399.233005585276.36699441472955
310405.8411.042591214943-5.24259121494327
311500.8482.47629401674318.3237059832574
312514512.9633539390871.0366460609132
313475.5493.848948575289-18.3489485752889
314430.1437.712812179029-7.61281217902897
315414.4411.5680720369532.831927963047
316538489.82037808225448.1796219177461
317526519.9059110816256.09408891837494
318488.5496.364090150757-7.86409015075675
319520.2471.83658221513848.3634177848618
320504.4507.894200283252-3.49420028325182
321568.5526.07846255104742.4215374489534
322610.6572.52632622315538.0736737768454
323818685.275142532825132.724857467175
324830.9830.2959401084250.604059891574666
325835.9802.77919776610333.1208022338968
326782792.157356437445-10.1573564374451
327762.3753.9928646033578.30713539664306
328856.9828.67361523232528.2263847676754
329820.9828.718527696189-7.81852769618884
330769.6782.164197471208-12.5641974712081
331752.2744.9316226019997.26837739800121
332724.4730.633085699255-6.2330856992553
333723.1739.800998644789-16.7009986447893
334719.5718.716482455920.783517544079507
335817.4788.37816476367829.0218352363224
336803.3822.171529582467-18.8715295824667
337752.5774.297837747957-21.7978377479571
338689707.26282494763-18.2628249476306
339630.4663.074075504451-32.6740755044511
340765.5697.76368358839767.7363164116034
341757.7742.9632187510114.7367812489907
342732.2722.9862273595649.21377264043583
343702.6710.706188661909-8.10618866190921
344683.3681.7921432039431.50785679605665
345709.5700.7273790361118.77262096388909
346702.2707.790937755255-5.59093775525515
347784.8771.60415450960713.1958454903929
348810.9789.66289797737621.237102022624
349755.6784.931769112855-29.3317691128545
350656.8710.297718500219-53.4977185002188
351615.1629.515730969903-14.4157309699026
352745.3684.41979603300160.8802039669994
353694.1723.278697799522-29.178697799522
354675.7658.26619769901817.433802300982
355643.7656.534581359111-12.8345813591109
356622.1624.599659155319-2.4996591553189
357634.6641.302243701262-6.70224370126176
358588634.203728399202-46.2037283992021
359689.7657.90252545723331.7974745427667
360673.9698.764356778878-24.8643567788779
361647.9648.839331256009-0.93933125600908
362568.8607.862726478246-39.0627264782458
363545.7545.5746793052420.125320694757771
364632.6618.51313523533214.0868647646676
365643.8610.74998855822733.0500114417726
366593.1614.11197106326-21.0119710632596
367579.7574.1102479446155.58975205538473
368546563.951770853173-17.9517708531728
369562.9566.661558365719-3.76155836571913
370572.5565.0353513978367.46464860216408


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.6511127978839930.6977744042320140.348887202116007
190.5770720482884740.8458559034230520.422927951711526
200.5961532635368920.8076934729262160.403846736463108
210.6105213727389650.778957254522070.389478627261035
220.5552512616377380.8894974767245250.444748738362262
230.4566297605578940.9132595211157870.543370239442106
240.447011947335090.894023894670180.55298805266491
250.7000389670580270.5999220658839460.299961032941973
260.831369452496750.3372610950064990.16863054750325
270.8115407791172530.3769184417654930.188459220882747
280.7610921890751270.4778156218497460.238907810924873
290.7589481226274110.4821037547451770.241051877372589
300.7141292985796140.5717414028407710.285870701420386
310.7401264835438060.5197470329123880.259873516456194
320.7082565242988530.5834869514022940.291743475701147
330.7651446500423230.4697106999153550.234855349957677
340.711010078071260.5779798438574790.28898992192874
350.7977940913888720.4044118172222550.202205908611128
360.7697371694426720.4605256611146550.230262830557328
370.7715385603539490.4569228792921030.228461439646051
380.734463774845880.5310724503082390.26553622515412
390.7024259272089230.5951481455821530.297574072791077
400.6699086931034380.6601826137931250.330091306896562
410.619839104148720.760321791702560.38016089585128
420.6169325631691520.7661348736616960.383067436830848
430.6347515100171370.7304969799657270.365248489982863
440.5839049876742890.8321900246514220.416095012325711
450.543207740146040.913584519707920.45679225985396
460.545472397720080.909055204559840.45452760227992
470.5053876703932850.9892246592134290.494612329606715
480.4528403681807940.9056807363615870.547159631819206
490.4031350728218540.8062701456437070.596864927178146
500.4025284219268670.8050568438537340.597471578073133
510.3680956161872510.7361912323745020.631904383812749
520.3553584465235240.7107168930470480.644641553476476
530.326431862626250.65286372525250.67356813737375
540.3248765697410480.6497531394820970.675123430258952
550.2843240297376790.5686480594753570.715675970262321
560.2455333545563480.4910667091126950.754466645443652
570.2102809177484520.4205618354969030.789719082251549
580.1786165013308070.3572330026616140.821383498669193
590.151666295196060.3033325903921190.84833370480394
600.1593850286116410.3187700572232820.84061497138836
610.1556228449264830.3112456898529650.844377155073517
620.162489455951130.3249789119022590.83751054404887
630.1384206115214420.2768412230428840.861579388478558
640.1415773138922390.2831546277844780.858422686107761
650.1224020898438360.2448041796876720.877597910156164
660.1131861663260140.2263723326520270.886813833673986
670.1164572699362540.2329145398725080.883542730063746
680.1011810632632740.2023621265265480.898818936736726
690.1108948634963160.2217897269926320.889105136503684
700.120992401705150.24198480341030.87900759829485
710.2137161398731820.4274322797463630.786283860126818
720.204390550048980.4087811000979590.79560944995102
730.2020840131519030.4041680263038050.797915986848097
740.1793142435103850.358628487020770.820685756489615
750.1539924641023760.3079849282047510.846007535897624
760.2059997281329020.4119994562658040.794000271867098
770.1979805000764510.3959610001529030.802019499923549
780.1810380160488030.3620760320976050.818961983951197
790.1591625277194450.318325055438890.840837472280555
800.1864326016067970.3728652032135940.813567398393203
810.1683539799683380.3367079599366760.831646020031662
820.1534880991874880.3069761983749750.846511900812512
830.1321418231069210.2642836462138410.86785817689308
840.1261566522835990.2523133045671980.8738433477164
850.1076199496792550.2152398993585090.892380050320745
860.1041754078497710.2083508156995430.895824592150229
870.1213830793782970.2427661587565940.878616920621703
880.1337351867793980.2674703735587950.866264813220602
890.1293106925615190.2586213851230380.870689307438481
900.1354239846019550.270847969203910.864576015398045
910.1226757459830210.2453514919660410.87732425401698
920.1167543138029130.2335086276058270.883245686197087
930.09947095883072680.1989419176614540.900529041169273
940.084262631608420.168525263216840.91573736839158
950.0869997080298310.1739994160596620.913000291970169
960.07327869899254760.1465573979850950.926721301007452
970.0768458816595120.1536917633190240.923154118340488
980.07172685701024840.1434537140204970.928273142989751
990.08933809274540440.1786761854908090.910661907254596
1000.0947548369671790.1895096739343580.905245163032821
1010.09121316861041620.1824263372208320.908786831389584
1020.086166968877170.172333937754340.91383303112283
1030.07291728597390950.1458345719478190.92708271402609
1040.06176974866571550.1235394973314310.938230251334285
1050.0719368066434750.143873613286950.928063193356525
1060.06130759024962650.1226151804992530.938692409750373
1070.05589545773170530.1117909154634110.944104542268295
1080.05245515440783730.1049103088156750.947544845592163
1090.04366475748846190.08732951497692390.956335242511538
1100.03928276982656490.07856553965312990.960717230173435
1110.03681945376186890.07363890752373780.963180546238131
1120.04043621115439730.08087242230879470.959563788845603
1130.04454584944017750.0890916988803550.955454150559823
1140.03760000309202160.07520000618404320.962399996907978
1150.03299737886877090.06599475773754190.96700262113123
1160.02902276291824250.0580455258364850.970977237081758
1170.04667497222733130.09334994445466270.953325027772669
1180.04018742038369620.08037484076739250.959812579616304
1190.06635115596752670.1327023119350530.933648844032473
1200.1207789456479060.2415578912958110.879221054352094
1210.1139967871725870.2279935743451740.886003212827413
1220.1187167591919720.2374335183839450.881283240808028
1230.1058142770345710.2116285540691430.894185722965429
1240.1171855010714290.2343710021428590.88281449892857
1250.1114518072346180.2229036144692360.888548192765382
1260.101637959276350.2032759185526990.89836204072365
1270.1218031227183070.2436062454366140.878196877281693
1280.1078687152977880.2157374305955760.892131284702212
1290.1005177547266920.2010355094533850.899482245273308
1300.1213387571173660.2426775142347320.878661242882634
1310.1124989953191370.2249979906382740.887501004680863
1320.0982886956603180.1965773913206360.901711304339682
1330.09105506330220450.1821101266044090.908944936697796
1340.1167818104094570.2335636208189150.883218189590543
1350.1050085741147440.2100171482294890.894991425885256
1360.1228566008567250.2457132017134490.877143399143275
1370.1177283303359230.2354566606718460.882271669664077
1380.1053192902975090.2106385805950170.894680709702491
1390.09171704080582820.1834340816116560.908282959194172
1400.09224548067587040.1844909613517410.90775451932413
1410.08512372483412280.1702474496682460.914876275165877
1420.08436151061079130.1687230212215830.915638489389209
1430.07439294559161270.1487858911832250.925607054408387
1440.08646148668612320.1729229733722460.913538513313877
1450.2004755087996660.4009510175993330.799524491200334
1460.2358672765987270.4717345531974530.764132723401273
1470.212611464439280.4252229288785610.78738853556072
1480.2684673773992730.5369347547985450.731532622600727
1490.2830408820495730.5660817640991460.716959117950427
1500.2768361690664380.5536723381328750.723163830933562
1510.2857392386857320.5714784773714650.714260761314268
1520.3745242136408140.7490484272816280.625475786359186
1530.3624914716956450.724982943391290.637508528304355
1540.4759475477728250.9518950955456490.524052452227175
1550.4516875985994670.9033751971989350.548312401400533
1560.4652966709105290.9305933418210580.534703329089471
1570.4423218273941990.8846436547883980.557678172605801
1580.4243030887955150.8486061775910310.575696911204485
1590.3986089118994640.7972178237989290.601391088100536
1600.4244148867158320.8488297734316640.575585113284168
1610.4304214791888040.8608429583776090.569578520811196
1620.410347005871690.820694011743380.58965299412831
1630.3967545674698470.7935091349396940.603245432530153
1640.3693003024593920.7386006049187840.630699697540608
1650.3523859464417210.7047718928834420.647614053558279
1660.3363662911920170.6727325823840350.663633708807983
1670.3188389987140950.6376779974281910.681161001285905
1680.3121956060959330.6243912121918660.687804393904067
1690.295772013527670.5915440270553390.70422798647233
1700.2768138029022570.5536276058045140.723186197097743
1710.3161373089443620.6322746178887230.683862691055638
1720.462778751364610.925557502729220.53722124863539
1730.4575304720377630.9150609440755250.542469527962237
1740.5251885989354540.949622802129090.474811401064546
1750.5401132557076170.9197734885847670.459886744292383
1760.5100864438074140.9798271123851730.489913556192586
1770.5431856284786440.9136287430427110.456814371521356
1780.520802432748640.958395134502720.47919756725136
1790.5071209925103990.9857580149792030.492879007489601
1800.4973731615513030.9947463231026050.502626838448697
1810.4996019305807120.9992038611614240.500398069419288
1820.4731906055987830.9463812111975660.526809394401217
1830.4770611055494130.9541222110988250.522938894450587
1840.5224271925893890.9551456148212220.477572807410611
1850.5298442189925650.940311562014870.470155781007435
1860.4994782706879760.9989565413759520.500521729312024
1870.4733765435173210.9467530870346420.526623456482679
1880.4534972887562310.9069945775124610.546502711243769
1890.4769894496592450.953978899318490.523010550340755
1900.461063851296520.922127702593040.53893614870348
1910.4316574082085380.8633148164170760.568342591791462
1920.4122380909801540.8244761819603080.587761909019846
1930.3884644252664360.7769288505328730.611535574733564
1940.3656622924229670.7313245848459340.634337707577033
1950.3445775209829720.6891550419659440.655422479017028
1960.408597265290580.817194530581160.59140273470942
1970.5971419066884340.8057161866231310.402858093311566
1980.6200373272818540.7599253454362930.379962672718146
1990.6021011060091020.7957977879817970.397898893990898
2000.5850606859503610.8298786280992780.414939314049639
2010.5661412838808760.8677174322382470.433858716119124
2020.550546937242180.898906125515640.44945306275782
2030.5516322362471450.896735527505710.448367763752855
2040.5499656361158980.9000687277682030.450034363884102
2050.5623568057166320.8752863885667360.437643194283368
2060.5828988741955090.8342022516089820.417101125804491
2070.5591777120910650.8816445758178690.440822287908935
2080.5916248493171960.8167503013656080.408375150682804
2090.6547272546459030.6905454907081950.345272745354097
2100.6361399373071120.7277201253857770.363860062692888
2110.6255353670954040.7489292658091920.374464632904596
2120.5997201404180030.8005597191639940.400279859581997
2130.574693118668360.850613762663280.42530688133164
2140.5553882860007170.8892234279985670.444611713999283
2150.5881859940778460.8236280118443080.411814005922154
2160.5815983858867130.8368032282265740.418401614113287
2170.5656523070477570.8686953859044850.434347692952243
2180.550640554895370.898718890209260.44935944510463
2190.5694409503458530.8611180993082930.430559049654147
2200.6214051158636440.7571897682727110.378594884136356
2210.6495793664048170.7008412671903670.350420633595183
2220.6317910546745710.7364178906508580.368208945325429
2230.6300147793895220.7399704412209570.369985220610478
2240.6149099906942580.7701800186114850.385090009305742
2250.5950530678194270.8098938643611460.404946932180573
2260.5732313789835720.8535372420328560.426768621016428
2270.6207248230319550.758550353936090.379275176968045
2280.5913720989359960.8172558021280090.408627901064004
2290.5617136916680570.8765726166638860.438286308331943
2300.5455267844640190.9089464310719630.454473215535981
2310.5156817064545130.9686365870909750.484318293545487
2320.5960451476978580.8079097046042840.403954852302142
2330.5875433051027570.8249133897944870.412456694897243
2340.5576533121834290.8846933756331430.442346687816572
2350.5320163590348280.9359672819303440.467983640965172
2360.5330017448659260.9339965102681490.466998255134074
2370.5571834752876250.885633049424750.442816524712375
2380.5437222512103970.9125554975792070.456277748789603
2390.6659792294667920.6680415410664160.334020770533208
2400.6578870615856520.6842258768286960.342112938414348
2410.6450544615520670.7098910768958660.354945538447933
2420.6188783538140120.7622432923719760.381121646185988
2430.5895931509585490.8208136980829020.410406849041451
2440.689883355952270.6202332880954610.31011664404773
2450.697597174065840.6048056518683210.302402825934161
2460.676760987354510.646478025290980.32323901264549
2470.6501317397840110.6997365204319780.349868260215989
2480.6230411101996470.7539177796007050.376958889800353
2490.612613823080040.774772353839920.38738617691996
2500.5998675149347930.8002649701304150.400132485065207
2510.7053443511556660.5893112976886680.294655648844334
2520.6763008753385860.6473982493228290.323699124661414
2530.6458926040160.7082147919680.354107395984
2540.6436456072689520.7127087854620960.356354392731048
2550.6159400357900030.7681199284199940.384059964209997
2560.6437966086805620.7124067826388750.356203391319438
2570.6134060027570810.7731879944858380.386593997242919
2580.5812662021906890.8374675956186220.418733797809311
2590.5707757157493650.8584485685012710.429224284250635
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2790.3463746048103980.6927492096207970.653625395189602
2800.3573827049816350.714765409963270.642617295018365
2810.325289881708380.650579763416760.67471011829162
2820.2950758630595560.5901517261191120.704924136940444
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2860.1950640472293230.3901280944586460.804935952770677
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2880.1844633949099930.3689267898199850.815536605090007
2890.1642262578128550.3284525156257090.835773742187145
2900.1447678218992950.289535643798590.855232178100705
2910.1272575672976270.2545151345952530.872742432702373
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3000.2063971253449760.4127942506899510.793602874655024
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3090.1294461460049910.2588922920099830.870553853995009
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3460.5773590937807840.8452818124384330.422640906219216
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3490.6373378222317620.7253243555364750.362662177768238
3500.5257933172241290.948413365551740.47420668277587
3510.3850634255618720.7701268511237430.614936574438129
3520.5043376065667560.9913247868664880.495662393433244


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.0149253731343284OK
10% type I error level210.0626865671641791OK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/10rsot1290857723.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/10rsot1290857723.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/12r901290857723.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/12r901290857723.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/22r901290857723.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/22r901290857723.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/3diq21290857723.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/3diq21290857723.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/4diq21290857723.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/4diq21290857723.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/5diq21290857723.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/5diq21290857723.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/6os8o1290857723.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/6os8o1290857723.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/7y1p81290857723.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/7y1p81290857723.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/8y1p81290857723.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/8y1p81290857723.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/9y1p81290857723.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/27/t1290857659p7sqb5bqpcbyqdk/9y1p81290857723.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
 
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
 




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