Concordia University
Department of Economics
ECON 681: Econometric Theory II
Assignment 3: Due on 05/04/2023
Winter 2023–20/03/2023
Question 1: (Checking the robustness of quantile estimators)
The goal of this question is to investigate the robustness of quantile estimators compare to OLS. We
will only consider the quantile estimator with ⌧ = 0.5 (the least absolute deviation estimator). Recall
that the LAD estimator in the linear regression model is equivalently defined as:
argmin
�
nX
i=1
|yi � x0i�| or argmin
�
nX
i=1
ui(⌧ � I(ui < 0)); with ui = yi � x0i� and ⌧ = 0.5.
This optimization program can be done using the fminsearch routine of Matlab which uses a simplex
algorithm and can work well even when the objective function is not smooth. Choose large values fo
‘MaxFunEvals’ and ‘MaxIter’ as option (e.g 10000 and 2000, respectively). Also, choose the OLS (o
MLE, GMM) estimates (whichever is relevant) of the parameter of interest as starting value for the
search algorithm.
Make sure you report your computation codes as this will count largely in the grade.
1. Generate a vector Y of n i.i.d variables from t distribution with ⌫ degrees of freedom (t(⌫))
and generate a vector X of n i.i.d variables from N(0, 1) in a way that X is independent of Y .
Choose n = 1000.
2. Compute the OLS estimator �̂ of � = (�1,�2)0 from the regression:
Yi = �1 + �2Xi + ui.
Store �̂2. Compute the LAD estimator �̃ of � from the same model and store �̃2.
3. Repeat 1. and 2. MC = 10000 times. What is the true parameter value �20 of �2 in this
simulation exercise? Calculate the simulated bias and root-mean-square-e
or (RMSE) of �̂2
and �̃2 through the MC replications. Recall that the simulated bias and RMSE are given by:
Bias(✓̂) =
1
MC
MCX
j=1
✓̂(j) � ✓0 and rmse(✓̂) =
vuut 1
MC
MCX
j=1
⇣
✓̂(j) � ✓0
⌘2
,
where ✓̂(j) is the value of ✓̂ obtained in the jth replication.
4. Perform 3. for ⌫ = 1.0, 1.1, 1.2, 1.3, 1.4, 1.6, 1.8, 2.0, 5.0, and 10.0.
5. Report the results and comment on the choice of OLS and LAD estimators as well as the
obustness of the latter. Have also in mind that the t(⌫) has finite moments only up to ⌫ � 1.
1
Question 2: (Empirical application)
Use the data set in the file 401KSUBS to estimate the quantile regression:
nettfai = �1 + �2inci + �3agei + �4age
2
i + �5e401ki + ui, Quantile⌧ (ui|inci, agei, e401ki) = 0.
(Use the fminsearch matlab routine as indicated in the previous question and the OLS estimate as
starting value. Make sure you report your computation codes as this will count largely in the grade.)
Report six (6) columns of outputs containing the OLS estimates, and the quantile estimates of
� = (�1,�2,�3,�4,�5)0 for ⌧ = 0.1, 0.25, 0.5 (LAD), 0.75 and 0.9.
Report also their respective standard e
ors.
Comment your results. Discuss in particular the variation of the e↵ect of the co-variates on the
conditional distribution of nettfa at the considered locations.
Important note: See Abadie (2003, Section 6) for more background on the 401(K) retirement
savings programs in the US and data description.
Question 3: (Simulated method of moments (SMM), GMM and Indirect inference (II)
estimation.)
Assume that the process (xt)t follows the dynamics:
xt = ↵+ �xt�1ft + "t, � � 0, (1)
where zt = (ft, "t)0 ⇠ NID(0,�2I2) and zt is independent of xs for all s < t.
Our goal is to generate several samples {xt : t = 1, . . . , T} of the process (xt)t and on each
sample, we estimate the parameter of interest, ✓ = (↵,�,�2), by SMM, GMM and II and then
compare their bias, RMSE and MAD (mean absolute deviation). For this we first choose a true value
✓0 = (0.2, 0.5, 1.0) for ✓.
1. Show that the dynamics of (xt)t in (1) implies that the value of ✓ that governs this dynamics
solves the moment condition model:
E[g(xt, xt�1, ✓)] = 0 with g(xt, xt�1, ✓) =
0
@
xt � ↵
x2t � ↵2 � �2�2x2t�1 � �2
x2txt�1 � ↵2xt�1 � �2�2x3t�1 � �2xt�1
1
A (2)
2. Repeat the following steps MC = 10, 000 times and store the SMM, GMM and II estimators:
(a) Generate a sample of size T = 200 of (xt)t under ✓0. [Indication: Set the initial value of xt
to 0 and generate a sample of T + 100 observations and then take the last T observations
for your sample. This helps to minimize the e↵ect of initial value.]
(b) Estimate ✓0 by SMM by matching to the sample counterparts of the moments:
E
0
@
xt
x2t
x2txt�1
1
A
and store the estimates.
2
(c) Estimate ✓0 by GMM using the moment condition (2) and store the estimates.
(d) Estimate ✓0 by II based on GMM and store the estimates.
3. Report the bias, RMSE and MAD of each estimator component by component. Comment.
3
401ksubs
0 13.17 0 0 40 1 4.575 0 1 XXXXXXXXXX 1600
1 61.23 0 1 35 1 154 1 0 XXXXXXXXXX 1225
0 12.858 1 0 44 2 0 0 0 XXXXXXXXXX 1936
0 98.88 1 1 44 2 21.8 0 0 XXXXXXXXXX 1936
0 22.614 0 0 53 1 18.45 0 0 511.393 2809
0 15 1 0 60 3 0 0 0 225 3600
0 37.155 1 0 49 5 3.483 0 1 XXXXXXXXXX 2401
0 31.896 1 0 38 5 -2.1 0 0 XXXXXXXXXX 1444
0 47.295 1 0 52 2 5.29 0 1 XXXXXXXXXX 2704
1 29.1 0 1 45 1 29.6 0 1 846.81 2025
0 23.457 1 0 61 3 0 0 0 XXXXXXXXXX 3721
0 31.785 1 0 40 6 18.149 0 0 XXXXXXXXXX 1600
0 34.941 1 0 48 3 0.695 0 0 XXXXXXXXXX 2304
0 24.432 1 0 60 2 0.2 0 0 XXXXXXXXXX 3600
0 25.131 1 0 43 5 -4.25 0 0 XXXXXXXXXX 1849
0 19.074 0 1 43 1 0 0 0 XXXXXXXXXX 1849
1 38.772 1 0 47 2 4.15 1 0 XXXXXXXXXX 2209
1 12.48 1 0 27 2 -10 0 0 XXXXXXXXXX 729
1 45.39 1 0 57 2 122.5 0 1 XXXXXXXXXX 3249
0 39.861 1 0 35 2 1.6 0 1 XXXXXXXXXX 1225
1 102.6 1 0 53 5 40.999 1 1 XXXXXXXXXX 2809
0 39.579 1 0 36 4 12.175 0 0 XXXXXXXXXX 1296
0 40.194 1 0 40 3 8.3 0 1 XXXXXXXXXX 1600
0 25.254 0 1 31 1 9.687 0 1 XXXXXXXXXX 961
0 10.8 0 0 48 2 0.5 0 0 116.64 2304
0 27 0 1 42 1 0.13 0 0 729 1764
0 17.856 1 0 42 5 -15.495 0 0 XXXXXXXXXX 1764
1 38.94 1 0 35 2 0.2 1 0 XXXXXXXXXX 1225
0 12.24 0 1 32 2 -2.5 0 1 XXXXXXXXXX 1024
0 18 0 0 46 2 0 0 1 324 2116
1 21.456 0 0 25 1 -21.02 1 0 XXXXXXXXXX 625
0 14.025 1 0 39 5 1.4 0 0 XXXXXXXXXX 1521
0 15.186 1 1 36 4 -5.192 0 1 XXXXXXXXXX 1296
1 41.415 1 0 34 5 5.8 1 1 XXXXXXXXXX 1156
0 12.966 0 0 52 2 -4.25 0 0 XXXXXXXXXX 2704
0 58.8 1 0 41 2 63.7 0 1 3457.44 1681
1 63.849 1 0 44 5 5.899 1 0 XXXXXXXXXX 1936
1 47.1 0 1 61 2 59.8 1 0 2218.41 3721
0 36.072 1 0 63 3 72.225 0 1 XXXXXXXXXX 3969
1 107.64 1 0 51 2 69 1 1 XXXXXXXXXX 2601
1 58.92 1 0 49 4 19.679 0 0 XXXXXXXXXX 2401
1 48.615 1 0 36 4 -2.7 0 0 XXXXXXXXXX 1296
0 29.205 1 0 34 6 0.268 0 0 852.932 1156
0 24.441 0 0 36 2 2 0 0 XXXXXXXXXX 1296
0 18.525 1 0 28 5 -1.98 0 0 XXXXXXXXXX 784
0 15.36 1 0 38 4 -0.95 0 0 XXXXXXXXXX 1444
0 53.475 1 0 54 2 0.33 0 0 XXXXXXXXXX 2916
1 31.056 1 0 47 6 5.64 1 0 XXXXXXXXXX 2209
0 36.072 1 0 46 5 0 0 0 XXXXXXXXXX 2116
1 19.164 1 0 51 2 0.899 0 0 XXXXXXXXXX 2601
0 44.376 0 1 48 1 24.999 0 0 XXXXXXXXXX 2304
0 28.329 0 0 54 2 26.53 0 1 XXXXXXXXXX 2916
1 37.38 0 1 48 1 2.999 0 0 XXXXXXXXXX 2304
0 17.817 0 1 42 1 1.7 0 0 XXXXXXXXXX 1764
1 71.028 1 0 60 2 257.6 1 1 XXXXXXXXXX 3600
0 28.476 0 0 58 1 27 0 1 XXXXXXXXXX 3364
0 34.041 1 0 30 2 -4 0 0 1158.79 900
0 25.86 0 0 26 1 10.5 0 0 XXXXXXXXXX 676
1 77.355 1 0 33 4 30.9 1 1 XXXXXXXXXX 1089
0 15.414 0 0 50 4 0.145 0 0 XXXXXXXXXX 2500
0 73.902 1 0 44 4 14.299 0 0 XXXXXXXXXX 1936
1 10.545 0 1 25 1 1.031 1 0 111.197 625
0 24.177 0 1 25 1 -1.301 0 0 XXXXXXXXXX 625
1 33.42 0 0 46 2 8.412 1 0 XXXXXXXXXX 2116
0 53.226 1 0 51 8 -1.2 0 0 XXXXXXXXXX 2601
0 31.869 0 1 36 1 1.6 0 1 XXXXXXXXXX 1296
0 26.946 0 0 64 2 -3.8 0 0 XXXXXXXXXX 4096
1 90.726 1 0 62 2 13.6 1 1 XXXXXXXXXX 3844
0 28.8 1 0 31 8 0.1 0 0 XXXXXXXXXX 961
0 23.193 1 0 37 7 -1.886 0 0 XXXXXXXXXX 1369
1 36.036 0 1 64 1 18.25 1 0 XXXXXXXXXX 4096
0 56.082 1 0 28 2 9.8 0 0 XXXXXXXXXX 784
0 16.032 0 1 25 2 1.93 0 0 257.025 625
1 52.236 1 0 38 6 -3 0 0 2728.6 1444
0 39.63 1 0 41 3 0.225 0 0 XXXXXXXXXX 1681
1 34.47 1 0 50 5 27.798 1 0 XXXXXXXXXX 2500
0 31.548 1 0 56 6 -24.8 0 0 XXXXXXXXXX 3136
1 31.2 1 0 28 2 -72.8 0 0 XXXXXXXXXX 784
1 19.806 1 0 39 4 25.57 1 0 XXXXXXXXXX 1521
1 74.529 1 0 42 4 17.95 1 0 XXXXXXXXXX 1764
1 26.682 1 0 43 5 3.6 1 0 XXXXXXXXXX 1849
0 33.75 0 1 25 1 -3 0 0 XXXXXXXXXX 625
1 146.577 1 0 43 5 778.628 1 1 XXXXXXXXXX 1849
0 23.718 0 0 32 1 14.299 0 1 XXXXXXXXXX 1024
1 19.302 0 0 31 1 -3.4 0 0 XXXXXXXXXX 961
0 29.4 1 0 53 2 27.96 0 1 864.36 2809
0 16.977 0 0 35 2 -1.025 0 0 XXXXXXXXXX 1225
0 19.734 1 0 49 7 -1.3 0 0 XXXXXXXXXX 2401
0 14.28 1 0 43 4 -0.5 0 0 XXXXXXXXXX 1849
0 30.075 1 0 47 3 4.232 0 1 XXXXXXXXXX 2209
0 27 1 0 46 3 -3.3 0 0 729 2116
1 33.96 1 0 36 13 10.2 1 0 XXXXXXXXXX 1296
0 64.5 1 0 47 3 5.9 0 0 4160.25 2209
0 24.03 0 0 31 2 -4 0 0 XXXXXXXXXX 961
0 21.111 0 0 64 1 2.5 0 0 XXXXXXXXXX 4096
0 51.798 0 0 38 2 41.999 0 0 XXXXXXXXXX 1444
1 17.364 1 0 27 2 -37.504 1 0 XXXXXXXXXX 729
0 19.254 0 0 28 1 -1.1 0 0 XXXXXXXXXX 784
0 27.603 1 0 35 4 7.559 0 0 XXXXXXXXXX 1225
0 31.44 0 1 33 1 -1 0 0 XXXXXXXXXX 1089
0 30.24 0 0 45 3 0.92 0 0 XXXXXXXXXX 2025
0 16.83 0 1 28 1 0.74 0 0 XXXXXXXXXX 784
1 30.231 1 0 34 4 -0.03 1 0 XXXXXXXXXX 1156
0 35.4 0 1 33 2 2.475 0 0 1253.16 1089
1 46.155 1 0 55 5 53.948 1 1 XXXXXXXXXX 3025
0 27.33 1 0 50 4 -2.6 0 0 XXXXXXXXXX 2500
1 24.678 0 0 44 3 0.583 0 0 XXXXXXXXXX 1936
1 36.027 1 1 59 2 20.249 1 0 XXXXXXXXXX 3481
0 13.59 0 0 32 4 0 0 0 XXXXXXXXXX 1024
0 15.741 0 0 49 2 0.05 0 0 XXXXXXXXXX 2401
0 27.465 1 0 52 4 1.1 0 0 XXXXXXXXXX 2704
0 54.963 1 0 55 2 53.699 0 0 XXXXXXXXXX 3025
0 18.549 1 0 63 2 19 0 0 XXXXXXXXXX 3969
1 21.576 0 1 39 1 14.5 1 1 XXXXXXXXXX 1521
0 38.76 1 0 47 3 -0.5 0 0 XXXXXXXXXX 2209
1 64.662 1 0 62 2 19.444 1 1 XXXXXXXXXX 3844
1 43.158 1 0 46 2 8 1 0 XXXXXXXXXX 2116
0 17.88 0 0 43 3 -3.151 0 0 XXXXXXXXXX 1849
1 23.7 1 0 53 2 13.2 1 0 XXXXXXXXXX 2809
0 37.755 1 0 56 2 -1.2 0 0 1425.44 3136
1 40.971 1 1 29 3 -9.968 1 0 XXXXXXXXXX 841
0 13.428 1 0 28 2 7.179 0 0 XXXXXXXXXX 784
1 92.991 1 0 48 5 83.9 1 1 XXXXXXXXXX 2304
0 35.061 1 0 41 3 22 0 0 XXXXXXXXXX 1681
0 17.136 0 1 34 3 0.015 0 0 XXXXXXXXXX 1156
0 16.956 1 0 47 2 0.826 0 0 XXXXXXXXXX 2209
1 56.7 1 0 63 2 94.399 1 1 3214.89 3969
0 70.275 1 0 41 4 -2.157 0 0 XXXXXXXXXX 1681
0 13.464 0 0 27 2 0.4 0 0 XXXXXXXXXX 729
0 74.649 1 0 39 4 -8.005 0 0 XXXXXXXXXX 1521
0 24.012 0 0 41 3 -9.456 0 0 XXXXXXXXXX 1681
0 20.559 0 0 30 3 2.9 0 0 XXXXXXXXXX 900
0 39.528 1 1 55 2 -15.718 0 1 XXXXXXXXXX 3025
0 39.303 0 0 34 1 -12.188 0 0 XXXXXXXXXX 1156
0 17.715 0 1 31 1 4.05 0 0 XXXXXXXXXX 961
1 55.08 1 0 54 2 44.043 1 0 XXXXXXXXXX 2916
1 20.76 0 1 55 1 7.955 0 0 XXXXXXXXXX 3025
0 14.283 0 1 40 1 0.028 0 0 XXXXXXXXXX 1600
0 20.436 1 0 31 4 -0.2 0 0 XXXXXXXXXX 961
0 26.388 1 0 26 4 0.866 0 0 XXXXXXXXXX 676
0 46.104 1 0 39 3 4.35 0 0 XXXXXXXXXX 1521
1 49.71 1 0 42 3 27.097 0 1 XXXXXXXXXX 1764
1 76.806 1 0 52 2 80.049 1 0 XXXXXXXXXX 2704
1 39.705 1 0 39 4 71.569 0 1 XXXXXXXXXX 1521
0 11.22 0 0 30 3 0 0 0 XXXXXXXXXX 900
0 18.348 0 0 50 4 0.3 0 0 XXXXXXXXXX 2500
0 13.95 0 0 49 6 0 0 0 XXXXXXXXXX 2401
0 15.216 1 0 32 10 0.2 0 0 XXXXXXXXXX 1024
0 36.15 1 0 32 4 -2.2 0 0 XXXXXXXXXX 1024
0 47.34 0 1 27 2 -10.43 0 0 XXXXXXXXXX 729
0 63.225 1 0 40 6 10.571 0 1 3997.4 1600
1 26.031 0 0 49 2 0.5 0 0 677.613 2401
0 20.4 1 0 36 5 0 0 0 416.16 1296
0 17.928 0 0 36 3 0 0 0 XXXXXXXXXX 1296
1 31.8 1 0 38 5 12.51 1 0 1011.24 1444
0 30 1 1 42 2 -15.1 0 0 900 1764
0 26.463 0 0 43 1 10.011 0 1 XXXXXXXXXX 1849
1 73.722 1 0 42 5 3.2 1 0 XXXXXXXXXX 1764
0 33.414 1 0 32 4 -4.7 0 0 XXXXXXXXXX 1024
1 34.08 1 0 35 6 5.1 0 0 XXXXXXXXXX 1225
1 48.9 0 0 61 1 28.08 1 1 2391.21 3721
1 21 0 1 41 1 -2.347 0 0 441 1681
1 39.609 1 0 59 2 48.684 0 1 XXXXXXXXXX 3481
1 80.85 1 0 45 5 132.425 1 1 XXXXXXXXXX 2025
1 91.761 1 0 35 4 -3.45 0 0 XXXXXXXXXX 1225
0 46.719 1 0 41 5 4 0 0 XXXXXXXXXX 1681
0 19.2 0 1 25 1 0 0 0 368.64 625
1 102.705 1 0 35 2 56.6 1 0 XXXXXXXXXX 1225
0 54.966 1 0 33 3 1.4 0 0 XXXXXXXXXX 1089
0 12.75 0 1 38 2 0 0 0 XXXXXXXXXX 1444
1 52.836 1 0 50 2 37.65 1 0 XXXXXXXXXX 2500
0 12.72 1 0 32 3 0 0 0 XXXXXXXXXX 1024
1 26.646 0 0 28 1 62.679 1 0 XXXXXXXXXX 784
0 59.664 1 0 44 5 11.4 0 1 XXXXXXXXXX 1936
0 39.15 1 0 58 2 189.4 0 1 XXXXXXXXXX 3364
0 21.3 1 0 47 6 -0.986 0 0 453.69 2209
0 30.87 0 0 48 3 10.95 0 1 952.957 2304
0 16.5 1 0 57 2 23 0 0 272.25 3249
0 32.592 1 0 57 4 -2.1 0 0 XXXXXXXXXX 3249
0 27.345 1 0 34 5 0.409 0 0 747.749 1156
0 35.418 1 0 57 5 -5.75 0 0 XXXXXXXXXX 3249
0 24.3 0 0 45 1 0.05 0 0 590.49 2025
1 52.365 1 0 46 2 6 0 0 XXXXXXXXXX 2116
0 26.865 1 1 29 4 -1.945 0 0 XXXXXXXXXX 841
0 39.48 1 0 35 3 40.2 0 1 1558.67 1225
1 45.693 1 0 40 2 18.576 1 1 2087.85 1600
0 39.3 1 0 42 5 -4.9 0 0 1544.49 1764
0 14.244 0 0 60 1 120 0 1 XXXXXXXXXX 3600
0 43.518 1 0 27 3 -7.5 0 0 XXXXXXXXXX 729
0 10.857 0 1 27 1 1.046 0 0 XXXXXXXXXX 729
0 51.222 0 0 33 3 1.6 0 0 XXXXXXXXXX 1089
0 34.764 1 1 63 2 3 0 0 XXXXXXXXXX 3969
0 16.959 1 0 43 7 -0.5 0 0 XXXXXXXXXX 1849
0 23.64 1 0 26 5 -6.001 0 0 XXXXXXXXXX 676
1 56.304 1 0 45 4 29.4 1 0 XXXXXXXXXX 2025
1 107.097 1 0 54 2 47.922 0 0 XXXXXXXXXX 2916
1 46.818 1 0 39 4 1.8 1 1 XXXXXXXXXX 1521
0 13.11 0 0 30 3 0 0 0 XXXXXXXXXX 900
0 29.16 0 0 36 3 4.245 0 0 XXXXXXXXXX 1296
1 42.54 1 0 52 2 30.1 1 0 XXXXXXXXXX 2704
0 74.31 1 0 49 6 -5.5 0 0 XXXXXXXXXX 2401
0 27.729 1 0 31 6 -4.88 0 0 XXXXXXXXXX 961
0 15 0 0 42 3 49.538 0 0 225 1764
1 54.723 1 0 57 2 21.948 1 0 XXXXXXXXXX 3249
0 61.581 1 0 44 4 61.366 0 1 3792.22 1936
1 72.03 1 0 43 2 21.998 0 1 XXXXXXXXXX 1849
1 37.338 1 0 48 7 -18.89 0 1 XXXXXXXXXX 2304
0 27.354 0 0 39 3 2.6 0 1 XXXXXXXXXX 1521
1 92.898 0 1 27 1 30.957 1 1 XXXXXXXXXX 729
0 42.942 1 0 33 4 -2.205 0 0 XXXXXXXXXX 1089
1 31.017 1 0 31 5 1.209 0 1 XXXXXXXXXX 961
1 23.892 0 0 35 1 5.249 1 0 XXXXXXXXXX 1225
1 44.46 1 0 39 2 38.307 1 1 XXXXXXXXXX 1521
0 33.72 1 1 30 2 -2.552 0 0 XXXXXXXXXX 900
0 52.821 1 0 37 5 3.3 0 1 XXXXXXXXXX 1369
1 86.4 1 0 43 4 45.1 0 1 7464.96 1849
0 14.763 1 0 31 4 -5.007 0 0 XXXXXXXXXX 961
1 107.301 1 0 50 3 371.901 1 1 11513.5 2500
0 20.385 0 0 27 1 -0.8 0 0 XXXXXXXXXX 729
1 41.526 1 0 35 2 -1.5 0 0 XXXXXXXXXX 1225
0 27.375 1 0 41 4 -1.2 0 0 XXXXXXXXXX 1681
0 24.336 1 0 42 4 -0.3 0 0 XXXXXXXXXX 1764
1 34.425 1 0 40 4 -6.46 1 0 XXXXXXXXXX 1600
0 43.131 1 0 45 2 59.16 0 1 XXXXXXXXXX 2025
1 57.063 1 0 54 4 138.999 1 1 XXXXXXXXXX 2916
1 41.535 0 1 30 1 83.7 0 1 XXXXXXXXXX 900
0 61.722 1 1 32 3 -2.48 0 0 XXXXXXXXXX 1024
0 99.153 1 0 43 2 80.802 0 1 XXXXXXXXXX 1849
0 37.296 1 0 28 3 -2 0 0 XXXXXXXXXX 784
1 27.06 0 0 38 3 37.1 1 0 XXXXXXXXXX 1444
0 32.46 0 0 26 1 -4.49 0 0 XXXXXXXXXX 676
1 28.491 0 1 46 3 4.02 1 0 XXXXXXXXXX 2116
1 19.596 0 1 41 2 -1.2 1 0 XXXXXXXXXX 1681
1 18.54 0 0 37 5 0.964 1 0 XXXXXXXXXX 1369
1 102.435 1 0 54 2 139.629 1 1 XXXXXXXXXX 2916
0 26.7 1 0 39 4 46 0 0 712.89 1521
1 43.788 0 0 43 3 46.85 1 1 XXXXXXXXXX 1849
1 45.87 1 0 47 2 38.8 1 1 XXXXXXXXXX 2209
0 38.88 0 1 61 2 -17.7 0 0 XXXXXXXXXX 3721
0 17.706 0 0 43 3 3.399 0 0 XXXXXXXXXX 1849
0 21.63 0 0 63 2 -0.74 0 0 XXXXXXXXXX 3969
1 43.437 1 0 36 3 12.818 1 1 XXXXXXXXXX 1296
1 57.15 1 0 34 4 2.2 1 0 XXXXXXXXXX 1156
1 22.902 0 1 51 1 61.749 1 1 XXXXXXXXXX 2601
0 40.74 1 0 42 5 3.6 0 1 XXXXXXXXXX 1764
0 13.182 1 0 32 4 -2.373 0 0 XXXXXXXXXX 1024
0 11.61 0 0 44 1 0.05 0 0 XXXXXXXXXX 1936
1 108.588 1 0 62 2 84.196 0 0 XXXXXXXXXX 3844
1 29.772 1 0 53 2 1.198 0 0 XXXXXXXXXX 2809
1 67.32 1 0 45 3 36.199 1 0 XXXXXXXXXX 2025
1 46.236 1 0 57 3 22.398 0 1 XXXXXXXXXX 3249
0 32.589 1 0 31 4 1.3 0 0 XXXXXXXXXX 961
0 60.498 1 0 40 2 10.05 0 1 XXXXXXXXXX 1600
0 30.21 0 1 30 2 3.1 0 1 912.644 900
1 55.125 1 0 35 4 23.969 1 1 XXXXXXXXXX 1225
1 56.676 1 0 56 2 -6.239 0 0 XXXXXXXXXX 3136
1 70.845 1 0 57 2 200 1 1 XXXXXXXXXX 3249
0 46.815 0 0 44 3 30.902 0 1 XXXXXXXXXX 1936
1 14.43 0 0 27 1 -4.5 0 0 XXXXXXXXXX 729
1 58.173 1 0 31 2 22.45 0 0 XXXXXXXXXX 961
1 50.646 0 0 40 2 45.584 1 1 XXXXXXXXXX 1600
0 12.207 0 1 32 1 -0.843 0 0 XXXXXXXXXX 1024
0 62.526 1 0 52 2 69.1 0 1 XXXXXXXXXX 2704
1 44.703 1 1 32 5 -14 0 0 XXXXXXXXXX 1024
0 85.38 1 0 25 4 -30.24 0 0 XXXXXXXXXX 625
0 34.647 0 1 35 1 4.999 0 0 XXXXXXXXXX 1225
1 27.786 1 1 41 3 -0.001 0 0 XXXXXXXXXX 1681
1 54.45 1 0 63 2 94.599 1 1 XXXXXXXXXX 3969
1 19.638 0 1 52 1 8.5 1 0 XXXXXXXXXX 2704
1 33.312 0 0 36 2 77.2 1 0 XXXXXXXXXX 1296
0 24.534 1 0 25 3 5.15 0 0 XXXXXXXXXX 625
0 18.36 0 1 28 1 0.015 0 0 XXXXXXXXXX 784
0 28.74 0 1 27 1 -1.2 0 0 XXXXXXXXXX 729
0 36.168 1 0 25 3 -1.501 0 0 XXXXXXXXXX 625
0 17.01 1 0 47 3 4.15 0 0 XXXXXXXXXX 2209
0 17.412 0 0 39 3 0 0 0 XXXXXXXXXX 1521
0 32.427 1 0 37 2 0.1 0 0 1051.51 1369
0 38.058 0 1 28 1 5.2 0 0 XXXXXXXXXX 784
1 58.317 1 0 46 3 36.5 0 1 XXXXXXXXXX 2116
0 15.822 1 0 26 2 11.4 0 0 XXXXXXXXXX 676
0 43.275 1 0 26 2 -3.073 0 0 XXXXXXXXXX 676
0 17.445 1 0 34 4 -7 0 0 304.328 1156
1 21.639 1 0 42 4 0.249 0 0 XXXXXXXXXX 1764
0 48.033 0 0 61 2 32.055 0 1 XXXXXXXXXX 3721
0 49.08 1 0 54 2 45.7 0 1 XXXXXXXXXX 2916
1 35.292 0 1 61 1 3 0 0 XXXXXXXXXX 3721
0 28.575 0 0 27 1 1.249 0 0 XXXXXXXXXX 729
0 33.366 0 1 43 1 12.599 0 0 1113.29 1849
1 35.472 0 1 51 2 60.398 1 0 XXXXXXXXXX 2601
0 42.93 1 1 35 3 -18.88 0 0 XXXXXXXXXX 1225
0 17.58 1 0 32 2 12.2 0 1 XXXXXXXXXX 1024
0 77.01 1 0 36 4 26 0 1 XXXXXXXXXX 1296
0 37.662 1 0 37 11 -2.382 0 0 XXXXXXXXXX 1369
1 20.43 1 0 57 1 -1.94 1 0 XXXXXXXXXX 3249
0 27.84 0 0 40 2 0 0 0 XXXXXXXXXX 1600
0 49.806 0 0 37 1 110.103 0 1 XXXXXXXXXX 1369
0 24.678 1 0 34 3 -0.312 0 0 XXXXXXXXXX 1156
0 15.81 1 1 60 3 -3.2 0 0 XXXXXXXXXX 3600
1 17.628 1 0 27 4 12.34 1 0 XXXXXXXXXX 729
0 20.406 1 0 37 4 -10.515 0 1 XXXXXXXXXX 1369
0 37.644 1 1 44 3 -6.656 0 0 XXXXXXXXXX 1936
0 89.175 1 1 43 3 -0.2 0 0 XXXXXXXXXX 1849
0 30.165 1 1 56 3 -0.101 0 0 XXXXXXXXXX 3136
0 27.294 0 0 34 2 -2.5 0 0 XXXXXXXXXX 1156
0 34.509 0 1 39 1 13.3 0 0 XXXXXXXXXX 1521
0 28.716 1 0 30 4 -19.175 0 0 XXXXXXXXXX 900
1 15 0 1 38 1 2.85 1 0 225 1444
1 29.325 1 0 33 4 5.513 1 0 XXXXXXXXXX 1089
0 56.913 1 1 39 3 35.883 0 1 XXXXXXXXXX 1521
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