Multiple Linear Regression - Estimated Regression Equation
[t] = + 3322.01 + 0.128828`F1C(t-1)`[t] -0.248037`F1C(t-2)`[t] + 0.0153302`F1C(t-3)`[t] -0.0929215`F1C(t-4)`[t] -0.0976214`F1C(t-5)`[t] -0.0475415`F1C(t-6)`[t] -0.0798657`F1C(t-7)`[t] + 0.0636949`F1C(t-8)`[t] -0.00833499`F1C(t-9)`[t] + 0.222908`F1C(t-10)`[t] + 0.00887549`F1C(t-11)`[t] -0.283728`F1C(t-12)`[t] + 489.948M1[t] + 781.735M2[t] + 746.321M3[t] + 258.598M4[t] -150.416M5[t] -339.192M6[t] -463.056M7[t] -808.364M8[t] -990.457M9[t] -921.372M10[t] -868.529M11[t] -7.77076t + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)+3322 1546+2.1480e+00 0.0387 0.01935
`F1C(t-1)`+0.1288 0.1688+7.6330e-01 0.4504 0.2252
`F1C(t-2)`-0.248 0.1685-1.4720e+00 0.15 0.07498
`F1C(t-3)`+0.01533 0.1702+9.0090e-02 0.9287 0.4644
`F1C(t-4)`-0.09292 0.1713-5.4260e-01 0.5909 0.2954
`F1C(t-5)`-0.09762 0.1721-5.6740e-01 0.5741 0.287
`F1C(t-6)`-0.04754 0.1724-2.7570e-01 0.7844 0.3922
`F1C(t-7)`-0.07987 0.1722-4.6390e-01 0.6456 0.3228
`F1C(t-8)`+0.0637 0.1725+3.6930e-01 0.7141 0.3571
`F1C(t-9)`-0.008335 0.1725-4.8320e-02 0.9617 0.4809
`F1C(t-10)`+0.2229 0.1724+1.2930e+00 0.2044 0.1022
`F1C(t-11)`+0.008875 0.1616+5.4920e-02 0.9565 0.4783
`F1C(t-12)`-0.2837 0.1613-1.7590e+00 0.08727 0.04363
M1+489.9 249+1.9680e+00 0.05707 0.02854
M2+781.7 382.6+2.0430e+00 0.04861 0.0243
M3+746.3 516.2+1.4460e+00 0.1571 0.07857
M4+258.6 607.9+4.2540e-01 0.6732 0.3366
M5-150.4 639-2.3540e-01 0.8153 0.4076
M6-339.2 624.2-5.4340e-01 0.5903 0.2951
M7-463.1 584.5-7.9230e-01 0.4335 0.2168
M8-808.4 525.9-1.5370e+00 0.1332 0.06662
M9-990.5 435.9-2.2720e+00 0.02932 0.01466
M10-921.4 318.2-2.8960e+00 0.006479 0.00324
M11-868.5 213-4.0780e+00 0.0002488 0.0001244
t-7.771 4.247-1.8300e+00 0.0758 0.0379


Multiple Linear Regression - Regression Statistics
Multiple R 0.959
R-squared 0.9196
Adjusted R-squared 0.8645
F-TEST (value) 16.69
F-TEST (DF numerator)24
F-TEST (DF denominator)35
p-value 7.895e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 232.3
Sum Squared Residuals 1.889e+06


Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1 2933 2974-41.36
2 2889 3276-387.1
3 2938 2924 13.95
4 2497 2327 169.9
5 1870 1898-27.87
6 1726 1757-31.18
7 1607 1710-103
8 1545 1554-9.376
9 1396 1522-126.2
10 1787 1598 188.5
11 2076 1885 191.3
12 2837 2634 202.6
13 2787 3041-253.8
14 3891 3035 855.6
15 3179 2942 237.5
16 2011 2059-47.58
17 1636 1711-75.36
18 1580 1640-60.46
19 1489 1492-2.853
20 1300 1414-114.2
21 1356 1417-60.91
22 1653 1817-164.1
23 2013 1868 145.2
24 2823 2722 100.6
25 3102 3097 4.625
26 2294 2625-330.8
27 2385 2473-88.43
28 2444 2370 74.35
29 1748 1870-122.4
30 1554 1543 11.03
31 1498 1609-111.4
32 1361 1457-96.27
33 1346 1458-111.8
34 1564 1672-107.9
35 1640 1779-139
36 2293 2274 19.24
37 2815 2747 68.39
38 3137 3166-28.92
39 2679 2846-166.7
40 1969 2068-98.63
41 1870 1728 141.9
42 1633 1608 25.1
43 1529 1408 121.1
44 1366 1281 85.32
45 1357 1217 139.5
46 1570 1519 50.96
47 1535 1754-219.2
48 2491 2452 39.29
49 3084 2862 222.1
50 2605 2714-108.8
51 2573 2569 3.669
52 2143 2241-98.02
53 1693 1609 83.76
54 1504 1448 55.52
55 1461 1365 96.24
56 1354 1219 134.6
57 1333 1174 159.4
58 1492 1459 32.54
59 1781 1759 21.67
60 1915 2277-361.7


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
28 0.9996 0.0007376 0.0003688
29 0.9982 0.003624 0.001812
30 0.9919 0.01619 0.008093
31 0.9718 0.05633 0.02817
32 0.9135 0.1731 0.08655


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level2 0.4NOK
5% type I error level30.6NOK
10% type I error level40.8NOK


Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 7.0456, df1 = 2, df2 = 33, p-value = 0.002831
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = -0.43364, df1 = 48, df2 = -13, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.40428, df1 = 2, df2 = 33, p-value = 0.6707


Variance Inflation Factors (Multicollinearity)
> vif
 `F1C(t-1)`  `F1C(t-2)`  `F1C(t-3)`  `F1C(t-4)`  `F1C(t-5)`  `F1C(t-6)` 
  12.518333   12.453451   12.529724   12.522414   12.521569   12.442948 
 `F1C(t-7)`  `F1C(t-8)`  `F1C(t-9)` `F1C(t-10)` `F1C(t-11)` `F1C(t-12)` 
  12.321787   12.285902   12.411126   12.471964   10.935649   10.845821 
         M1          M2          M3          M4          M5          M6 
   5.266731   12.433374   22.635058   31.390465   34.680240   33.091501 
         M7          M8          M9         M10         M11           t 
  29.015216   23.490252   16.138635    8.600256    3.852449    6.014358