Crowdly

The p-value from the Honda test is 0.03. The estimator with

time effects is being tested. Select the correct answer.

The

random effects

estimator is more suitable

than the fixed effects estimator bacause the random

effects estimator is inconsistent under the null hypothesis, which is rejected

using the 5% significance level.

The pooled OLS estimator

is suitable

because there are no time effects under the null hypothesis, which

is not rejected using the 5% significance level.

The estimator with time effects is suitable

because there

are no time effects under the null hypothesis, which is rejected at the 5%

significance level.

The pooled OLS estimator is suitable

because the pooled OLS estimator is consistent under the null

hypothesis, which is not rejected using the 5% significance level.

The fixed effects estimator is more suitable

than the radom

effects estimator bacause the fixed effects estimator is inconsistent under the

null hypothesis, which is rejected using the 5% significance level.

pcap) and unemployment rate (unemp

Your task is to model the gross state product (gsp

)

based on public capital stock (

) using

the within estimator with individual effects. Utilize the

plm function from the plm

package. The panel

indexes are

state and year

; however, the data is already in the form of a panel

dataframe.

library(plm)

data(Produc)

Report the coefficient that measures the impact of the public capital stock (pcap) on the gross state product (gsp).

Suppose the two variables, x and y, are integrated of order one, and their linear combination is tested using the Augmented Dickey–Fuller (ADF) test, yielding the following results: test statistic = −4.15 and critical value = −3.97.

You want to construct either

(a) an Error Correction Model (ECM), or

(b) a model in differences:

Which model is most appropriate?

Since both time series are integrated of the same order, we should use the ECM model right away, no matter what the ADF test tells us.

We should use the model on differences since no cointegration relationship was found.

Both time series are integrated of the first order. We should test the time series for cointegration, and the Breusch–Godfrey test is appropriate for this.

Since both time series are integrated of the same order and there is a cointegration relationship, the ECM model is preferred.

What is the order of integration of the time series gdp from the Wooldridge package? Use a 5% significance level and the appropriate variant of the Augmented Dickey-Fuller Unit Root Test in the ur.df function from the urca package.

library('wooldridge')

library('urca')

data(inven)

inven$gdp

Since the different types of tests give different results, the test is inconclusive.

The time series is integrated of the second or higher order.

The time series is integrated of the first order, and the test needs to be executed twice to determine this.

The time series is integrated of order zero, as the test statistic does not lie within the critical region.

lny) time series from the urca

Consider the real money (lrm1

) and real income

(

package. Both time series are integrated of

order 1.

Use a classical linear regression model and the Augmented Dickey-Fuller Unit Root (ADF) test via the ur.df

function to determine whether there is a

cointegration
relationship
between these two time series.

library(urca)

data(finland)

lm(lny ~ lrm1, data = finland)

Yes, a cointegration relationship is found

when a 1% significance level is used for the ADF test.

No cointegration relationship is found even

when a 10% significance level is used.

Yes, a

cointegration relationship is found when a 10% significance level is used for

the ADF test. However, no cointegration relationship is found using the 5%

level.

Yes, a

cointegration relationship is found when a 5% significance level is used for

the ADF test. However, no cointegration relationship is found using the 1%

level.

finite
distributed lag
model:

Model the daily closing prices of DAX (Deutsche Aktienindex)

on the prices of FTSE (Financial Times Stock Exchange) using a

data(EuStockMarkets)

m3 <- lm(DAX[3:100] ~ FTSE[3:100] + FTSE[2:99] +

FTSE[1:98], data=EuStockMarkets)

Report the impact multiplier

(propensity)

using at least 2 decimal places.

outliers package and select the correct answer.

Suppose you want to model the stopping distances of cars (dist) based on their speed (speed). Your task is to test for the presence of outliers in the data using Grubbs' test.

library(outliers)

data("cars")

head(cars)

Use the grubbs.test

function from

the

The lowest value of speed (4) and the highest value of dist

(120)

are significant outliers.

There are no significant outliers

present in either variable

at a

1% significance level.

There is no strict rule to decide, but some literature suggests that points for which Grubbs’s value is higher than 1 should be considered as outliers. Other literature provides a threshold of 4/n or 4/(n−k−1).

, the variable speed includes at least one outlier, while dist does not.

At a

5% significance

level

Select all correct statements about Grubbs’ test. Note that multiple answers may be correct, and incorrect answers will result in point deductions.

Grubbs' test is based on the assumption of normality.

For Grubbs' test, all variables need to be log-transformed.

Grubbs' test is not a statistical test. T

here is no strict rule to decide, but some literature

suggests that points for which Grubbs’s value is higher than 1 should be

considered as outliers. Other literature provides a threshold of 4/n or

4/(n−k−1).

Since the Grubbs' test detects influential observations in the model, the model must be estimated first.

Grubbs' test detects one outlier at a time.

Your task is to model the stopping distances of cars (dist) using a classical linear model (lm function), with the speed of the cars (speed) as the explanatory variable.

data("cars")

head(cars)

Report the Cook's distance value for the most influential observation using the cooks.distance function.