SUMMER TERM 2020

ONLINE 24-HOUR EXAMINATION

ECON0019: QUANTITATIVE ECONOMICS AND ECONOMETRICS

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Answer ALL TWO questions from Part A and answer ONE question from Part B.

Questions in Part A carry 60 per cent of the total mark and questions in Part B carry 40 per cent of

the total. Tables for the normal and F-distribution are at the end of the examination paper.

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ECON0019 1 TURN OVER

PART A

Answer all questions from this section.

A.1 You have randomly sampled n individuals whom you follow over T 2 time periods. For

individual i (= 1, ..., n) you observe (yit, xit), t = 1, ..., T , which satisfies

yit = 0 + 1xit + ai + uit, t = 1, ..., T. (1)

(a) Show that

yit = 1xit +uit, i = 1, ..., n, t = 2, ..., T. (2)

Discuss in detail the advantages and disadvantages of using eq. (2) instead of eq. (1) for

estimation and inference.

ANSWER:

• Advantages of OLS based on (2): No potential biases due to xit being correlated with

ai.

• Dis-advantages of OLS based on (2): Generally bigger variance due to fewer observa-

tions after transformation In particular, (2) requires variation in xit over time while

(1) does not. However, depending on the autocorrelation structure of uit vs the one

of uit, OLS based on (2) may still perform better even in terms of variance.

• For both regressions, clustered standard errors will generally be needed. So in this sense,

neither dominates the other.

(b) Write up the sum of squared residuals (SSR) for (2) given your sample. Suppose here and

in the following that

Pn

i=1

PT

t=2 (xit)

2 > 0 in your sample. Show that the minimizer of

SSR is

ˆ1 =

Pn

i=1

PT

t=2xityitPn

i=1

PT

t=2 (xit)

2

,

where you explain each step of your derivation.

ANSWER:

SSR =

nX

i=1

TX

t=2

(yit 1xit)2 .

The minimizer ˆ1 satisfies

0 = 1

2

@SSR

@1

=

nX

i=1

TX

t=2

⇣

yit ˆ1xit

⌘

xit

=

nX

i=1

TX

t=2

yitxit ˆ1

nX

i=1

TX

t=2

(xit)

2

ECON0019 2 CONTINUED

) ˆ1 =

Pn

i=1

PT

t=2xityitPn

i=1

PT

t=2 (xit)

2

.

(c) Suppose that

uit = uit1 + eit

where E [eit|xi1, ...., xiT ] = 0, t = 1, ...., T . Show that for any i, t,

E [uit|X1, ..., Xn] = 0,

where Xi = (xi2, ....,xiT ), i = 1, ..., n. Use this in turn to show that ˆ1 is unbiased. As

part of your proof, carefully explain each step.

ANSWER:

E [uit|X1, ..., Xn] = E [eit|X1, ..., Xn]

=

(*)

E [eit|Xi]

= E [E [eit|xi1, ...., xiT ] |Xi]

= 0,

where (*) uses that data is randomly sampled. Write

ˆ1 = 1 +

Pn

i=1

PT

t=2xituitPn

i=1

PT

t=2 (xit)

2

, (3)

and take conditional expectations on both sides,

E[ˆ1|X1, ...., Xn] = 1 +

Pn

i=1

PT

t=2xitE[uit|X1, ...., Xn]Pn

i=1

PT

t=2 (xit)

2

= 1

(d) Suppose furthermore that

E

⇥

e2it|xi1, ...., xiT

⇤

= 2,

E [eiseit|xi1, ...., xiT ] = 0, s 6= t.

Demonstrate that

Cov (uis,uit|X1, ..., Xn) =

⇢

2, s = t

0, s 6= t .

Use this in turn to derive an expression of the conditional variance of ˆ1, Var(ˆ1|X1, ...., Xn),

where you carefully explain each step of your derivation. Comment on the resulting variance

ECON0019 3 TURN OVER

expression. In particular, how is the variability of the OLS estimator a↵ected by the

variation of the error term and the regressor?

ANSWER:

Cov (uis,uit|X1, ..., Xn) = E [eiseit|X1, ..., Xn]

=

(*)

E [eiseit|Xi]

= E [E [eiseit|xi1, ...., xiT ] |Xi]

=

⇢

2, s = t

0, s 6= t ,

where (*) uses that data is randomly sampled. Thus,

Var(ˆ1|X1, ...., Xn) = Var

Pn

i=1

PT

t=2xituitPn

i=1

PT

t=2 (xit)

2

X1, ...., Xn

!

=

1hPn

i=1

PT

t=2 (xit)

2

i2Var

nX

i=1

TX

t=1

xituit

X1, ...., Xn

!

=

1hPn

i=1

PT

t=2 (xit)

2

i2 nX

i,j=1

TX

s,t=2

xisxitVar (uisujt|X1, ...., Xn)

=

1hPn

i=1

PT

t=2 (xit)

2

i2 nX

i=1

TX

t=2

(xit)

2 2

=

2Pn

i=1

PT

t=2 (xit)

2

.

We see that the variance of the OLS estimator increases as a function of 2, which makes

sense: More noise in data makes it harder to learn about 1. Reversely, higher realised

variation of xit provides a stronger signal in data about the value of 1 and so decreases

the variance of the OLS estimator

(e) Assume that Pr

⇣PT

t=2 (xit)

2 = 0

⌘

= 0. Show that this implies E

hPT

t=2 (xit)

2

i

>

0. Show consistency of ˆ1 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.

ECON0019 4 CONTINUED

ANSWER: By the LLN for i.i.d. data,

1

n

nX

i=1

TX

t=2

xituit ! p

TX

t=2

E [xituit] =

TX

t=2

E [xitE [uit|X1, ..., Xn]] = 0,

1

n

nX

i=1

TX

t=2

(xit)

2 ! p

TX

t=2

E

h

(xit)

2

i

> 0.

Consistency now follows from (3) together with the continuous mapping theorem. We can

apply the continuous mapping theorem since f(a, b) = a/b is continuous everywhere except

at b = 0 and b = 0 is ruled out due to the assumption at the beginning of the question.

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, (4)

(0.079) (2.238) (0.294) (0.010)

with R2 = 0.775, standard error of residual SER = 0.1377, and heteroskedasticity-robust stan-

dard 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 tech-

nical assumptions.

ANSWER:

• Re. (i): First, data needs to come from the same population in order for us to set

up a model for this population. Second, random sampling is used in the analysis of

the variance of the OLS estimators, incl computation of standard errors. In terms of

validity, first note that each observational unit is a country. Thus, if we define the

population as all countries in the world, the sample is by definition drawn from this

population and so identically distributed. Data would then be i.i.d. if we had randomly

ECON0019 5 TURN OVER

sampled the 108 countries. But the question does not specify how data is collected so the

answer to this part is open ended. However, it seems unlikely that they are randomly

sampled - there are only around 200 countries in the world and many of these do not

publish reliable statistics. If they are not randomly sampled, then the independence

assumption is most likely violated due to spill-over e↵ects (trade, migration, etc): For

example, a given country’s income level will likely depend on the income levels of its

trading partners.

• Re. (ii): This assumption is needed to ensure that OLS is unbiased. In our application,

the error term here contains other factors a↵ecting GDP per worker, incl institutional

and geographical di↵erences. These are most likely correlated with gpop, sk and educ

in which case (ii) is violated.

(b) Interpret the above regression results and indicate whether or not the coecients are sig-

nificantly di↵erent from zero. Do the coecients have the expected sign? Explain.

ANSWER:

• A one percentage point decrease in the population growth rate increases GDP per worker

relative to the United States by roughly 0.06.

• An increase in the investment share of 0.1 results in an increase of GDP per worker

relative to the United States by approximately 0.07.

• For every additional year of average educational attainment, the increase is 0.055.

• The regression explains 77.5 percent of the variation in relative productivity.

• All coecients are significantly di↵erent from zero at conventional levels.

• All coecients carry the expected sign.

(c) To test for equality of the coecients 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 (5)

(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)

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 di↵erences.

ANSWER:

non-OECD :\relinc = 0.068 0.063gpop+ 0.719sk + 0.044educ.

ECON0019 6 CONTINUED

OECD :\relinc = 0.313 8.101gpop+ 0.289sk + 0.047educ.

We see that relinc in non-OECD countries tend to be much less negatively a↵ected by

population growth while increases in investment has a much bigger impact.

(d) In order to test (4) against (5), 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 coecients. What do you conclude? Explain.

ANSWER:

H0 : oecd = oecd⇥gpop = oecd⇥sk = oecd⇥educ = 0

versus

HA : At least one of the above coecients is non-zero.

We have 4 restrictions and so F -statistic follows the F4,1 distribution in large samples.

The critical value is 2.37 at the 5% level and hence we can reject the null hypothesis that

the coecients are equal. We conclude that data supports the hypothesis that there are

di↵erences in relinc between OECD and non-OECD countries after controlling for gpop,

sk and educ.

(e) You decide to investigate further and estimate a restricted version of (5) where you enforce

the same slopes across OECD and non-OECD countries, but allow their intercepts to di↵er.

In this new regression, the t-statistic for oecd is 3.17. What is the p-value of the t-statistic?

What do you conclude? Explain your answer.

ANSWER: t-statistic follows N (0, 1) distribution in large samples and so its p-value is

p = Pr (|t| > 3.17) = 2 (1 Pr (t 3.17)) = 2 (1 0.9992) = 0.16%.

That is, there’s only 0.16% probability of observing a more extreme outcome under the null.

In particular, we reject at a 1% level. We conclude that, under the maintained hypothesis

that the e↵ect of gpop, sk and educ are the same between the two groups, the level of relinc

between OECD and non-OECD countries is di↵erent.

(f) Next, you test the model described in (e) against (5). 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?

Explain your answer.

ANSWER: We here test

H0 : oecd⇥gpop = oecd⇥sk = oecd⇥educ = 0,

and so so F -statistic follows the F3,1 distribution in large samples with corresponding

critical value of 2.68 at the 5% level. Thus, we cannot reject the null hypothesis, and so

there appears to be no di↵erences in the impact of gpop, sk and educ on relinc between

OECD and non-OECD countries. The over-all conclusion is that there only seems to be a

level di↵erence between the two groups of countries.

ECON0019 7 TURN OVER

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 Hu↵man and Uwe Sunde (“The Intergenera-

tional 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 par-

ents 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 RPi to be the parental score for that same measure in the household, suppose you

are interested in the model:

Di = 1(0 + 1R

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 es-

timate the di↵erence in the probability that Di = 1 between a household where RPi = 10

and another one where RPi = 0? Please explain your answer.

ANSWER: The log-likelihood function is:

nX

j=1

{b0 b1RPi ln[1 + exp(b0 b1RPi )]}⇥ (1Di) ln[1 + exp(b0 b1RPi )]⇥Di

or equivalently

nX

j=1

{b0 + b1RPi ln[1 + exp(b0 + b1RPi )]}⇥Di ln[1 + exp(b0 + b1RPi )]⇥ (1Di).

ECON0019 8 CONTINUED

Once estimates are obtained, the estimated di↵erence in probabilities is given by:

exp(ˆ0)/(1 + exp(ˆ0)) exp(ˆ0 ˆ110)/(1 + exp(ˆ0 ˆ110))

or, equivalently,

exp(ˆ0 + ˆ110)/(1 + exp(ˆ0 + ˆ110)) exp(ˆ0)/(1 + exp(ˆ0)).

(b) Because risk attitudes for children (RCi ) and parents (R

P

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

RCi = ↵0 + ↵1R

P

i + Vi,

using parental religion (Zi) as an instrumental variable for RPi . Describe in detail how you

would implement the TSLS estimator in this context. Discuss the validity of the instru-

mental variable suggested in this context. (Explain your intuition.)

ANSWER: TSLS: ( 1 ) Regress RPi on Zj. ( 2 ) Regress R

C

i on Rˆ

P

i . The instrumental

variable is valid if cov(Zi, Vi) = 0. This means that any unobserved variables that a↵ect the

child’s risk attitudes should be uncorrelated with parental religion.

(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.

ANSWER: The values for the F-statistics suggest that the instrument may not be su-

ciently strong and thus present substantial bias.

(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 coecients 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 coecient on the mother’s risk attitude at

about 0.23 and for the coecient 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

ECON0019 9 TURN OVER

expect the standard errors for the IV estimates to be larger than the standard errors for

the OLS estimates? Explain your answer.

ANSWER: Under homoscedasticity,

dvar(ˆIV ) = ˆ2

SSTxR2x,z

>

ˆ2

SSTx

=dvar(ˆOLS)

(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 = ↵0 + ↵1RG + 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

is unbiased? Elaborate on your answer.

ANSWER: The estimator would be biased since the model does not satisfy strict exo-

geneity.

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 di↵erent regions across years. Their specification for the demand function is

ln(Qmt) = ↵P ln(Pmt) + ↵

>

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 ↵, denoted by b↵P,OLS, equals -0.07 with a standard

error equal to 0.16.

(a) Explain in detail why the above estimate for the slope coecient (0.07) cannot be directly

interpreted as the price-elasticity of demand for cement.

ANSWER: Simultaneity bias.

ECON0019 10 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 coecient 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.

ANSWER: First stage F stat indicates that the IV is suciently correlated with the

endogenous variable. Validity holds if the price of gypsum is not correlated with other un-

observed determinants of demand for cement.

(c) To estimate the regression using the IV described above, the researchers use Two-Stage

Least Squares and obtain an estimate for ↵, denoted b↵P,TSLS, equal to -1.11 with a stan-

dard 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.

ANSWER: Textbook. With two IVs one could use overidentification restrictions to test

exogeneity, but not with only one IV.

(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) = ↵0 + ↵1 ln(Qt1) + ⌘t.

Would the OLS estimator be unbiased in this case? Under what assumptions would it be

consistent? Explain your answers in detail.

ANSWER: The OLS estimator is biased, but consistent.

(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) = ↵P ln(Pmt) + ↵

>

XXmt + Umt

ECON0019 11 TURN OVER

is no longer estimable, they are still able to estimate the model given by

Dmt =

⇢

1 if P ln(Pmt) + >XXmt + Vmt > ln(Q)

0 if P ln(Pmt) + >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 (0,2) how are P and ↵P related? Explain your answer

in detail.

ANSWER: Log-likelihood function for the probit. P = ↵P /.

ECON0019 12 CONTINUED

5 % Critical values for the F⌫1,⌫2 distribution

⌫2\⌫1 1 2 3 4 5 6 7 8 10 12 15 20 30 50 1

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

1 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 13 TURN OVER

NORMAL CUMULATIVE DISTRIBUTION FUNCTION (Prob(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 14 END OF PAPER

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