Actual StataMP numerical output. Log header and machine paths omitted.

. * BEGIN dynamic
. newey logacc L.logacc trend i.month unem spdlaw beltlaw wkends, lag(4)

Regression with Newey–West standard errors      Number of obs     =        107
Maximum lag = 4                                 F( 17,        89) =      87.90
                                                Prob > F          =     0.0000

------------------------------------------------------------------------------
             |             Newey–West
      logacc | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      logacc |
         L1. |   .3326578   .1071252     3.11   0.003     .1198023    .5455133
             |
       trend |   .0008893   .0002853     3.12   0.002     .0003223    .0014562
             |
       month |
          2  |   .0146527   .0239401     0.61   0.542    -.0329158    .0622212
          3  |   .1430085   .0223102     6.41   0.000     .0986787    .1873384
          4  |    .038098    .017672     2.16   0.034      .002984     .073212
          5  |    .072244   .0204505     3.53   0.001     .0316094    .1128787
          6  |    .062745   .0200754     3.13   0.002     .0228557    .1026344
          7  |   .0893948   .0204455     4.37   0.000     .0487701    .1300196
          8  |   .0938234   .0183948     5.10   0.000     .0572734    .1303733
          9  |   .0761947    .017798     4.28   0.000     .0408305     .111559
         10  |   .1188968   .0206093     5.77   0.000     .0779465    .1598471
         11  |   .0991803   .0200293     4.95   0.000     .0593825    .1389781
         12  |   .1255027   .0242489     5.18   0.000     .0773206    .1736849
             |
        unem |  -.0125834   .0044189    -2.85   0.005    -.0213638   -.0038031
      spdlaw |  -.0414191   .0141509    -2.93   0.004    -.0695366   -.0133016
     beltlaw |   .0652123   .0154671     4.22   0.000     .0344794    .0959452
      wkends |   .0032099    .002814     1.14   0.257    -.0023815    .0088014
       _cons |   7.018421   1.156725     6.07   0.000     4.720033    9.316809
------------------------------------------------------------------------------

. * END dynamic
. record dynamic L.logacc

. record law beltlaw

. * BEGIN prais
. prais logacc trend i.month unem spdlaw beltlaw wkends

Iteration 0:  rho = 0.0000
Iteration 1:  rho = 0.3553
Iteration 2:  rho = 0.3819
Iteration 3:  rho = 0.3855
Iteration 4:  rho = 0.3860
Iteration 5:  rho = 0.3861
Iteration 6:  rho = 0.3861
Iteration 7:  rho = 0.3861
Iteration 8:  rho = 0.3861

Prais–Winsten AR(1) regression with iterated estimates

      Source |       SS           df       MS      Number of obs   =       108
-------------+----------------------------------   F(16, 91)       =    640.24
       Model |  10.9813963        16  .686337271   Prob > F        =    0.0000
    Residual |  .097552142        91  .001072002   R-squared       =    0.9912
-------------+----------------------------------   Adj R-squared   =    0.9896
       Total |  11.0789485       107  .103541575   Root MSE        =    .03274

------------------------------------------------------------------------------
      logacc | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       trend |   .0009183    .000368     2.50   0.014     .0001874    .0016493
             |
       month |
          2  |  -.0298558   .0139349    -2.14   0.035    -.0575357   -.0021759
          3  |   .0815099    .015585     5.23   0.000     .0505523    .1124675
          4  |   .0161941   .0167108     0.97   0.335        -.017    .0493881
          5  |   .0286477   .0170886     1.68   0.097    -.0052966    .0625921
          6  |   .0275122   .0171963     1.60   0.113    -.0066461    .0616706
          7  |    .054312   .0167468     3.24   0.002     .0210466    .0875774
          8  |   .0612811   .0169516     3.62   0.000     .0276088    .0949535
          9  |   .0487127   .0173537     2.81   0.006     .0142417    .0831836
         10  |    .088227   .0168678     5.23   0.000     .0547211    .1217328
         11  |   .0849744   .0163305     5.20   0.000     .0525358     .117413
         12  |   .1094368   .0143486     7.63   0.000      .080935    .1379385
             |
        unem |   -.019975   .0047144    -4.24   0.000    -.0293397   -.0106104
      spdlaw |   -.045256   .0179614    -2.52   0.013     -.080934   -.0095779
     beltlaw |   .1008944   .0201055     5.02   0.000     .0609572    .1408315
      wkends |   .0042874   .0028062     1.53   0.130    -.0012867    .0098615
       _cons |   10.61796   .0628171   169.03   0.000     10.49318    10.74274
-------------+----------------------------------------------------------------
         rho |   .3861376
------------------------------------------------------------------------------
Durbin–Watson statistic (original)    = 1.200475
Durbin–Watson statistic (transformed) = 2.028900

. * END prais
. record prais beltlaw

. * A controlled quarterly simulation, not estimated inflation data.
. clear

. set seed 6031212

. set obs 440
Number of observations (_N) was 0, now 440.

. generate quarter=tq(1960q1)+_n-1

. format quarter %tq

. tsset quarter

Time variable: quarter, 1960q1 to 2069q4
        Delta: 1 quarter

. generate double shock=rnormal()

. generate double inflation=0

. forvalues i=2/440 {
  2.     quietly replace inflation=.6*inflation[_n-1]+.8*shock+rnormal() in `i'
  3. }

. keep in 201/440
(200 observations deleted)

. tsset quarter

Time variable: quarter, 2010q1 to 2069q4
        Delta: 1 quarter

. tempname lp

. postfile `lp' horizon coefficient se using "output/meeting_12_lp.dta", replace
(file output/meeting_12_lp.dta not found)

. forvalues h=0/8 {
  2.     quietly newey F`h'.inflation shock L.inflation, lag(8)
  3.     post `lp' (`h') (_b[shock]) (_se[shock])
  4. }

. postclose `lp'

. * BEGIN projection
. newey F4.inflation shock L.inflation, lag(8)

Regression with Newey–West standard errors      Number of obs     =        235
Maximum lag = 8                                 F(  2,       232) =       2.50
                                                Prob > F          =     0.0842

------------------------------------------------------------------------------
             |             Newey–West
F4.inflation | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
       shock |    -.17841   .1013114    -1.76   0.080    -.3780179    .0211979
             |
   inflation |
         L1. |   .0652363   .0710399     0.92   0.359    -.0747295    .2052021
             |
       _cons |  -.2784281   .1669179    -1.67   0.097    -.6072968    .0504406
------------------------------------------------------------------------------

. * END projection
. record horizon4 shock

. use "output/meeting_12_lp.dta", clear

. export delimited using "output/meeting_12_lp.csv", replace
(file output/meeting_12_lp.csv not found)
file output/meeting_12_lp.csv saved

. 
. postclose results

. use "output/meeting_12_estimates.dta", clear

. export delimited using "output/meeting_12_estimates.csv", replace
(file output/meeting_12_estimates.csv not found)
file output/meeting_12_estimates.csv saved

. display "ECO603_MEETING_12_COMPLETE"
ECO603_MEETING_12_COMPLETE

