ASSESSMENT : ECON0019A5UB/ECON0019A5UA/ECON2007A
A.1您在T≥2个时间段内随机抽取了n个追踪对象。对于

yit =β0+β1xit+ ai + uit，t = 1，…，T.（1）
（a）证明
∆yit = β1∆xit + ∆uit，i = 1，…，n，t = 2，…，T.（2）

（b）给定您的样本，为（2）写出残差平方和（SSR）。假设这里和

i = 1
PT
t = 2（∆xit）

SSR是
βˆ
1 =
n
i = 1
PT
t = 2 ∆xit∆yit
n
i = 1
PT
t = 2（∆xit）
2

（c）假设
uit = uit-1 + eit

E [∆uit | X1，…，Xn] = 0，

1是无偏的。如

(d) Suppose furthermore that
E

e
2
it|xi1, …., xiT
= σ
2
,
E [eiseit|xi1, …., xiT ] = 0, s 6= t.
Demonstrate that
Cov (∆uis, ∆uit|X1, …, Xn) = 
σ
2
, s = t
0, s 6= t
.
Use this in turn to derive an expression of the conditional variance of βˆ
1, Var(βˆ
1|X1, …., Xn),
where you carefully explain each step of your derivation. Comment on the resulting variance
expression. In particular, how is the variability of the OLS estimator affected by the
variation of the error term and the regressor?
ECON0019 2 CONTINUED
(e) Assume that P r PT
t=2 (∆xit)
2 = 0
= 0. Show that this implies E
hPT
t=2 (∆xit)
2
i
>
0. Show consistency of βˆ
1 under this assumption, where you clearly explain each step,
including which assumptions and limit results that you employ. In particular, explain why
the assumption stated at the beginning of this question is needed.
A.2 An extension of the Solow growth model, that includes human capital in addition to physical
capital, suggests that investment in human capital (education) will increase the wealth of a
nation (per capita income). To test this hypothesis, you collect data for 104 countries and
perform the following regression:
relinc \ = 0.046 − 5.869gpop + 0.738sk + 0.055educ, (3)
(0.079) (2.238) (0.294) (0.010)
with R2 = 0.775, standard error of residual SER = 0.1377, and heteroskedasticity-robust standard errors reported in parentheses. Here, relinc is GDP per worker relative to the United
States, gpop is the average population growth rate, 1980 to 1990, sk is the average investment
share of GDP from 1960 to 1990, and educ is the average educational attainment in years for
1985.
(a) Discuss the implications and validity of each of the following assumptions in the context of
the above regression:
i. Data is i.i.d.
ii. E [u|gpop, sk, educ] = 0 where u is the regression error.
In the following we will assume that (i)-(ii) are satisfied together with other relevant technical assumptions.
(b) Interpret the above regression results and indicate whether or not the coefficients are significantly different from zero. Do the coefficients have the expected sign? Explain.
(c) To test for equality of the coefficients between the OECD and other countries, you introduce
a binary variable (oecd), which takes on the value of one for the OECD countries and is
zero otherwise. You obtain the following regression estimates:
relinc \ = −0.068 − 0.063gpop + 0.719sk + 0.044educ (4)
(0.072) (2.271) (0.365) (0.012)
+0.381oecd − 8.038(oecd × gpop) − 0.430(oecd × sk)
(0.184) (5.366) (0.768)
+0.003(oecd × educ)
(0.018)
ECON0019 3 TURN OVER
where R2 = 0.845 and SER = 0.116. Write down the two regression functions, one for the
OECD countries, the other for the non-OECD countries. Explain. Interpret any differences.
(d) In order to test (3) against (4), you compute the corresponding F-statistic which takes the
value 6.76 in your sample. Write up the null hypothesis and its alternative that you are
testing in terms of the population regression coefficients. What do you conclude? Explain.
(e) You decide to investigate further and estimate a restricted version of (4) where you enforce
the same slopes across OECD and non-OECD countries, but allow their intercepts to differ.
In this new regression, the t-statistic for oecd is 3.17. What is the p-value of the t-statistic?
(f) Next, you test the model described in (e) against (4). The value of the corresponding
F-statistic is 1.05. Do you accept or reject the null?
Looking at the tests in this and two previous questions, what is your overall conclusion?
ECON0019 4 CONTINUED
PART B
Answer ONE question from this section.
B.1 Intergenerational mobility is related to several aspects. For example, theoretical studies have
examined the repercussions of the transmission of preferences and attitudes from parents to
children. Thomas Dohmen, Armin Falk, David Huffman and Uwe Sunde (“The Intergenerational Transmission of Risk and Trust Attitudes”) use the German Socio-Economic Panel Study
(SOEP) to empirically examine, among other things, the transmission of attitudes from parents to children and potential mechanisms for such transmission. Aside from comprehensive
demographic information on all individuals in a given household, the survey contains a set of
individual questions regarding risk attitudes (in 2004). (The authors also look at trust.) People
were asked questions eliciting their willingness to take risks on an eleven-point scale. For these
variables, zero (0) would correspond to ‘completely unwilling to take risks’ and the value ten
(10) means that the person is ‘completely willing to take risks.’
(a) One possible way to investigate the transmission of risk attitudes is to examine how parental
characteristics (including their risk attitudes) relate to the probability that a child has a
high score in terms of the risk attitude measure elicited on an 11-point scale as indicated
above. To do this, generate a variable Di = 1 if the child in household i has risk attitude
measure equal to 6 or above and Di = 0, otherwise. (While separate measures are available
for both parents, to keep matters simple we focus here on a single measure for parents.)
Taking RP
i
to be the parental score for that same measure in the household, suppose you
are interested in the model:
Di = 1(β0 + β1R
P
i + Ui ≥ 0).
Assuming that Ui follows a standard logistic distribution, write down the log-likelihood
function for this estimation problem when you have N observations. How would you estimate the difference in the probability that Di = 1 between a household where RP
i = 10
and another one where RP
(b) Because risk attitudes for children (RC
i
) and parents (RP
i
) are measured contemporaneously,
the authors worry about ‘reverse causality’ where children’s attitudes may be at least partly
shaping parents’ attitudes. To address this issue they estimate
R
C
i = α0 + α1R
P
i + Vi
,
using parental religion (Zi) as an instrumental variable for RP
i
. Describe in detail how you
would implement the TSLS estimator in this context. Discuss the validity of the instrumental variable suggested in this context. (Explain your intuition.)
ECON0019 5 TURN OVER
(c) The F-statistic for the first stage regression using the mother’s risk attitudes as covariate
in the main equation of interest and her religion as instrumental variable is 9.99. (The
F-statistic when using father’s risk attitudes and religion is 7.32.) Discuss in detail the
relevance of the instrumental variable.
(d) In a regression where the risk attitude for both mother and father are included individually
as covariates in a multiple linear regression model, both coefficients on those variables are
around 0.15 with standard errors at around 0.02 for each one of them. The TSLS estimates
on the other hand, produce estimates for the coefficient on the mother’s risk attitude at
about 0.23 and for the coefficient on the father’s risk attitude at about 0.02. (Religion for
each parent is available as an intrumental variable for each of their risk attitude variables.)
The standard error for those estimates are, in both cases, around 0.10. Why would you
expect the standard errors for the IV estimates to be larger than the standard errors for
(e) Imagine you had data on the risk attitude for successive generations of a single household
and you want to estimate the regression
RG+1 = α0 + α1RG + VG+1,
where RG+1 and RG are, once again, the risk attitudes in generation G + 1 (child) and in
generation G (parent). Assuming these are not measured contemporaneously so that the
issues raised in item (b) are not present, are there conditions under which an OLS estimator
B.2 In “Excess Capacity and Policy Interventions: Evidence from the Cement Industry,” Tetsuji
Okazaki, Ken Onishi and Naoki Wakamori estimate the demand for cement in Japan using data
on different regions across years. Their specification for the demand function is
ln(Qmt) = αP ln(Pmt) + α
>
XXmt + Umt,
where Qmt is the quantity of cement demanded in region m and year t (from 1970 to 1995), Pmt
is the price in that region and year and Xmt are year- and region-specific demand shifters. The
Ordinary Least Squares (OLS) estimate for α, denoted by αbP,OLS, equals -0.07 with a standard
error equal to 0.16.
(a) Explain in detail why the above estimate for the slope coefficient (−0.07) cannot be directly
interpreted as the price-elasticity of demand for cement.
ECON0019 6 CONTINUED
(b) To produce cement, crushed limestone, cray and other minerals are mixed and put into a
kiln to be heated. This process yields clinker, which is an intermediate cement product.
In a final stage, the grinded clinker is mixed with gypsum, another intermediate input, to
produce cement. The researchers then use the (log) price of gypsum as an instrumental
variable for the (log) price of cement to estimate the price-elasticity of demand. The OLS
regression of (log) cement prices on (log) gypsum prices (and X) yields a coefficient of 0.06
and the F-test statistic for the first stage equals 17.0. Discuss in detail the exogeneity and
relevance of this instrumental variable.
(c) To estimate the regression using the IV described above, the researchers use Two-Stage
Least Squares and obtain an estimate for α, denoted αbP,TSLS, equal to -1.11 with a standard error equal to 0.58. Describe in detail the TSLS procedure. Is it possible to test
whether the IV is exogenous? Explain in detail. What if there were two instrumental
variables? Explain in detail.
(d) Suppose the researchers were also interested in examining the time series behaviour for the
quantity of cement sold in a particular region in Japan on a given year, ln(Qt). To do so,
they obtain estimates for the following autoregressive model using data over various years
for this region of Japan:
ln(Qt) = α0 + α1 ln(Qt−1) + ηt
.
Would the OLS estimator be unbiased in this case? Under what assumptions would it be
(e) Suppose the researchers only observe whether Qmt is larger or smaller than a given fixed
threshold Q in a given year but otherwise observe prices and X. Let Dmt record whether
Qmt > Q (Dmt = 1) or not (Dmt = 0). While the regression
ln(Qmt) = αP ln(Pmt) + α
>
XXmt + Umt
is no longer estimable, they are still able to estimate the model given by
Dmt =

1 if βP ln(Pmt) + β
>
XXmt + Vmt > ln(Q)
0 if βP ln(Pmt) + β
>
XXmt + Vmt ≤ ln(Q)
Assume that the error term follows a standard normal distribution (i.e., Vmt ∼ N (0, 1))
and write down the log-likelihood function for this model assuming that the data comprises
of a random sample. If Umt ∼ N (0, σ2
in detail.
ECON0019 7 TURN OVER
5 % Critical values for the Fν1,ν2 distribution
ν2\ν1 1 2 3 4 5 6 7 8 10 12 15 20 30 50 ∞
1 161 199. 216. 225. 230. 234. 237. 239. 242. 244. 246. 248. 250. 252. 254.
2 18.5 19.0 19.2 19.2 19.3 19.3 19.4 19.4 19.4 19.4 19.4 19.4 19.5 19.5 19.5
3 10.1 9.55 9.28 9.12 9.01 8.94 8.89 8.85 8.79 8.74 8.70 8.66 8.62 8.58 8.53
4 7.71 6.94 6.59 6.39 6.26 6.16 6.09 6.04 5.96 5.91 5.86 5.80 5.75 5.70 5.63
5 6.61 5.79 5.41 5.19 5.05 4.95 4.88 4.82 4.74 4.68 4.62 4.56 4.50 4.44 4.36
10 4.96 3.52 3.13 2.90 2.74 2.63 2.54 2.48 2.38 2.31 2.23 2.16 2.07 2.00 1.88
20 4.35 3.49 3.10 2.87 2.71 2.60 2.51 2.45 2.35 2.28 2.20 2.12 2.04 1.97 1.84
30 4.17 3.32 2.92 2.69 2.53 2.42 2.33 2.27 2.16 2.09 2.01 1.93 1.84 1.76 1.62
60 4.00 3.15 2.76 2.53 2.37 2.25 2.17 2.10 1.99 1.92 1.84 1.75 1.65 1.56 1.39
80 3.97 3.11 2.72 2.49 2.33 2.21 2.13 2.06 1.95 1.88 1.79 1.70 1.60 1.51 1.32
100 3.94 3.09 2.70 2.46 2.31 2.19 2.10 2.03 1.93 1.85 1.77 1.68 1.57 1.48 1.28
120 3.91 3.07 2.68 2.45 2.29 2.18 2.09 2.02 1.91 1.83 1.75 1.66 1.55 1.46 1.25
∞ 3.85 3.00 2.60 2.37 2.21 2.10 2.01 1.94 1.83 1.75 1.67 1.57 1.46 1.35 1.00
ECON0019 8 CONTINUED
NORMAL CUMULATIVE DISTRIBUTION FUNCTION (P rob(z < za) where z ∼ N(0, 1))
za 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
0.0 0.5000 0.5040 0.5080 0.5120 0.5160 0.5199 0.5239 0.5279 0.5319 0.5359
0.1 0.5398 0.5438 0.5478 0.5517 0.5557 0.5596 0.5636 0.5675 0.5714 0.5753
0.2 0.5793 0.5832 0.5871 0.5910 0.5948 0.5987 0.6026 0.6064 0.6103 0.6141
0.3 0.6179 0.6217 0.6255 0.6293 0.6331 0.6368 0.6406 0.6443 0.6480 0.6517
0.4 0.6554 0.6591 0.6628 0.6664 0.6700 0.6736 0.6772 0.6808 0.6844 0.6879
0.5 0.6915 0.6950 0.6985 0.7019 0.7054 0.7088 0.7123 0.7157 0.7190 0.7224
0.6 0.7257 0.7291 0.7324 0.7357 0.7389 0.7422 0.7454 0.7486 0.7517 0.7549
0.7 0.7580 0.7611 0.7642 0.7673 0.7703 0.7734 0.7764 0.7794 0.7823 0.7852
0.8 0.7881 0.7910 0.7939 0.7967 0.7995 0.8023 0.8051 0.8078 0.8106 0.8133
0.9 0.8159 0.8186 0.8212 0.8238 0.8264 0.8289 0.8315 0.8340 0.8365 0.8389
1.0 0.8413 0.8438 0.8461 0.8485 0.8508 0.8531 0.8554 0.8577 0.8599 0.8621
1.1 0.8643 0.8665 0.8686 0.8708 0.8729 0.8749 0.8770 0.8790 0.8810 0.8830
1.2 0.8849 0.8869 0.8888 0.8907 0.8925 0.8944 0.8962 0.8980 0.8997 0.9015
1.3 0.9032 0.9049 0.9066 0.9082 0.9099 0.9115 0.9131 0.9147 0.9162 0.9177
1.4 0.9192 0.9207 0.9222 0.9236 0.9251 0.9265 0.9279 0.9292 0.9306 0.9319
1.5 0.9332 0.9345 0.9357 0.9370 0.9382 0.9394 0.9406 0.9418 0.9429 0.9441
1.6 0.9452 0.9463 0.9474 0.9484 0.9495 0.9505 0.9515 0.9525 0.9535 0.9545
1.7 0.9554 0.9564 0.9573 0.9582 0.9591 0.9599 0.9608 0.9616 0.9625 0.9633
1.8 0.9641 0.9649 0.9656 0.9664 0.9671 0.9678 0.9686 0.9693 0.9699 0.9706
1.9 0.9713 0.9719 0.9726 0.9732 0.9738 0.9744 0.9750 0.9756 0.9761 0.9767
2.0 0.9772 0.9778 0.9783 0.9788 0.9793 0.9798 0.9803 0.9808 0.9812 0.9817
2.1 0.9821 0.9826 0.9830 0.9834 0.9838 0.9842 0.9846 0.9850 0.9854 0.9857
2.2 0.9861 0.9864 0.9868 0.9871 0.9875 0.9878 0.9881 0.9884 0.9887 0.9890
2.3 0.9893 0.9896 0.9898 0.9901 0.9904 0.9906 0.9909 0.9911 0.9913 0.9916
2.4 0.9918 0.9920 0.9922 0.9925 0.9927 0.9929 0.9931 0.9932 0.9934 0.9936
2.5 0.9938 0.9940 0.9941 0.9943 0.9945 0.9946 0.9948 0.9949 0.9951 0.9952
2.6 0.9953 0.9955 0.9956 0.9957 0.9959 0.9960 0.9961 0.9962 0.9963 0.9964
2.7 0.9965 0.9966 0.9967 0.9968 0.9969 0.9970 0.9971 0.9972 0.9973 0.9974
2.8 0.9974 0.9975 0.9976 0.9977 0.9977 0.9978 0.9979 0.9979 0.9980 0.9981
2.9 0.9981 0.9982 0.9982 0.9983 0.9984 0.9984 0.9985 0.9985 0.9986 0.9986
3.0 0.9987 0.9987 0.9987 0.9988 0.9988 0.9989 0.9989 0.9989 0.9990 0.9990
3.1 0.9990 0.9991 0.9991 0.9991 0.9992 0.9992 0.9992 0.9992 0.9993 0.9993
3.2 0.9993 0.9993 0.9994 0.9994 0.9994 0.9994 0.9994 0.9995 0.9995 0.9995
ECON0019 9 END OF PAPER