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st: Autocorrelation doesn't seem to go away. What can I do here?


From   San K <devank@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: Autocorrelation doesn't seem to go away. What can I do here?
Date   Fri, 30 Mar 2012 17:51:20 +1100

Hello,
I’m wondering if anyone can tell me what my next step should be or how
bad my results are going to be if I ignore the autocorrelation problem
and bad Hansen statistics.
Basically this modelling is done on a utility data where consumption
is metered on a roughly 90 day period. I’m trying to estimate the
impact of the price on the consumption.
I chose to go with GMM as I have endogeneity problems with the lagged
consumption and two tier pricing.
I’m using laglimits(2 2) of consumption to handle the first endogeneity.
Also using actual RealTier1& RealTier2 as instrument for the weighted
average price. I don’t think this causes any concern.
RainDeviation, TempDeviation & EvapDeviation measure weather variables
as deviation from their mean value for the meter reading period. I
don’t think this causes any concern.
Summer, autumn, winter & spring are variables measuring how much
season is covered by each meter reading period.
restrictionsL2 is some sort of restriction on use placed by the government.
I have tried many different variations but nothing seems to solve the
autocorrelation problem.

I have attached the results.

Any suggestion I should do? Any help is appreciated.

Regards,
devank



. xtabond2 l(0/1).ConsDayAvgLN waitedAvgPrice waitedAvgPriceL1
waitedAvgPriceL2 waitedAvgPriceL3 RainDeviation TempDeviation
EvapDeviation summer autumn winter spring restrictionsL2, noleveleq
gmmstyle(ConsDayAvgLN, laglimits(2 2) equation(diff))
gmmstyle(RealTier1 RealTier2, laglimits(0 4) equation(diff) collapse)
ivstyle(l(0/0).(RainDeviation TempDeviation EvapDeviation
waitedAvgPriceL1 waitedAvgPriceL2 waitedAvgPriceL3), equation(diff))
ivstyle(summer autumn winter spring restrictionsL2 , equation(diff))
twostep ar(9) robust

Favoring space over speed. To switch, type or click on mata: mata set
matafavor speed, perm.

Dynamic panel-data estimation, two-step difference GMM
------------------------------------------------------------------------------
Group variable: subject                         Number of obs      =     48800
Time variable : period                          Number of groups   =      1952
Number of instruments = 46                      Obs per group: min =        25
Wald chi2(13) =   1369.01                                      avg =     25.00
Prob > chi2   =     0.000                                      max =        25
----------------------------------------------------------------------------------
                 |              Corrected
    ConsDayAvgLN |      Coef.   Std. Err.      z    P>|z|     [95%
Conf. Interval]
-----------------+----------------------------------------------------------------
    ConsDayAvgLN |
             L1. |   .4040915   .0171865    23.51   0.000     .3704065
   .4377764
                 |
  waitedAvgPrice |   .0011504   .0002147     5.36   0.000     .0007296
   .0015712
waitedAvgPriceL1 |  -.0006233   .0002843    -2.19   0.028    -.0011804
  -.0000661
waitedAvgPriceL2 |  -.0005686   .0003135    -1.81   0.070    -.0011831
   .0000459
waitedAvgPriceL3 |  -.0006479   .0002677    -2.42   0.015    -.0011725
  -.0001233
   RainDeviation |  -.0099555   .0014745    -6.75   0.000    -.0128455
  -.0070655
   TempDeviation |   .0003753   .0020412     0.18   0.854    -.0036253
   .0043759
   EvapDeviation |   .0504452   .0049941    10.10   0.000      .040657
   .0602335
          summer |  -.4569891   .1917348    -2.38   0.017    -.8327825
  -.0811957
          autumn |  -.4950277   .1921709    -2.58   0.010    -.8716757
  -.1183796
          winter |  -.5130355   .1954777    -2.62   0.009    -.8961648
  -.1299063
          spring |  -.4546386   .1939049    -2.34   0.019    -.8346852
   -.074592
  restrictionsL2 |   .0045096   .0045326     0.99   0.320    -.0043742
   .0133933
----------------------------------------------------------------------------------
Instruments for first differences equation
  Standard
    D.(RainDeviation TempDeviation EvapDeviation waitedAvgPriceL1
    waitedAvgPriceL2 waitedAvgPriceL3)
    D.(summer autumn winter spring restrictionsL2)
  GMM-type (missing=0, separate instruments for each period unless collapsed)
    L2.ConsDayAvgLN
    L(0/4).(RealTier1 RealTier2) collapsed
------------------------------------------------------------------------------
Arellano-Bond test for AR(1) in first differences: z = -26.06  Pr > z =  0.000
Arellano-Bond test for AR(2) in first differences: z =  -7.07  Pr > z =  0.000
Arellano-Bond test for AR(3) in first differences: z =  -2.38  Pr > z =  0.018
Arellano-Bond test for AR(4) in first differences: z =  13.79  Pr > z =  0.000
Arellano-Bond test for AR(5) in first differences: z =  -4.87  Pr > z =  0.000
Arellano-Bond test for AR(6) in first differences: z =  -8.13  Pr > z =  0.000
Arellano-Bond test for AR(7) in first differences: z =  -2.26  Pr > z =  0.024
Arellano-Bond test for AR(8) in first differences: z =  11.77  Pr > z =  0.000
Arellano-Bond test for AR(9) in first differences: z =  -2.04  Pr > z =  0.042
------------------------------------------------------------------------------
Sargan test of overid. restrictions: chi2(33)   =1045.99  Prob > chi2 =  0.000
  (Not robust, but not weakened by many instruments.)
Hansen test of overid. restrictions: chi2(33)   = 283.74  Prob > chi2 =  0.000
  (Robust, but can be weakened by many instruments.)

Difference-in-Hansen tests of exogeneity of instrument subsets:
  gmm(ConsDayAvgLN, eq(diff) lag(2 2))
    Hansen test excluding group:     chi2(8)    =  88.43  Prob > chi2 =  0.000
    Difference (null H = exogenous): chi2(25)   = 195.31  Prob > chi2 =  0.000
  gmm(RealTier1 RealTier2, collapse eq(diff) lag(0 4))
    Hansen test excluding group:     chi2(23)   = 175.38  Prob > chi2 =  0.000
    Difference (null H = exogenous): chi2(10)   = 108.36  Prob > chi2 =  0.000
  iv(RainDeviation TempDeviation EvapDeviation waitedAvgPriceL1
waitedAvgPriceL2 waitedAvgPriceL3, eq(diff))
    Hansen test excluding group:     chi2(27)   = 133.54  Prob > chi2 =  0.000
    Difference (null H = exogenous): chi2(6)    = 150.20  Prob > chi2 =  0.000
  iv(summer autumn winter spring restrictionsL2, eq(diff))
    Hansen test excluding group:     chi2(28)   = 264.04  Prob > chi2 =  0.000
    Difference (null H = exogenous): chi2(5)    =  19.71  Prob > chi2 =  0.001

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