Sustainable development in three newspapers: How does coverage in a particular newspaper influence other newspapers’ attention to sustainable development? Vector autoregression

1. Sustainable development in three newspapers:
How does coverage in a particular newspaper influence
other newspapers’ attention to sustainable development?
Vector autoregression
Assignment 4
Mark Boukes (markboukes@Hotmail.com)
5616298
1st semester 2010/2011
Dynamic Data Analysis
Lecturer: Dr. R. Vliegenthart
December 9, 2010
Communication Science (Research MSc)
Faculty of Social and Behavioural Sciences
University of Amsterdam

3. Table of contents
INTRODUCTION.............................................................................................................................................1
METHOD........................................................................................................................................................1
RESULTS........................................................................................................................................................2
VAR MODEL...........................................................................................................................................................2
CONCLUSION.................................................................................................................................................8
REFERENCE....................................................................................................................................................8
DO FILE:.........................................................................................................................................................9

5. Introduction
In this study I aim to investigate the influence news coverage in a particular newspaper has on
the coverage of another newspaper, and viceversa. For this purpose I have chosen a specific
topic, sustainable development, that seems to get a lot media attention in the last years. The
topic of sustainable development was chosen, because it can be related to several parts of
society, such as the economy, science and also for the man in the street this topic is relevant.
As those different parts of society are represented by different media, it is interesting to
see how they influence each other on the amount of attention that is paid to this issue. Will an
increase in attention of business men’s newspapers result in an increase of attention in
newspapers that deal for a large part with science; and is this also occurring the other way
around? How is the attention in a popular newspaper caused by or perhaps causing itself
attention in scientific or economic newspapers. The most read popular newspaper in the
Netherlands is De Telegraaf, a newspaper that has a main economic or business focus is Het
Financieele Dagblad, and NRC Handelsblad is known for its relative large attention to
scientific developments.
To study the relationship between the attention to sustainable development in news
coverage of these three newspapers, my main research question was: Does the amount of
attention in one newspaper for sustainable development cause attention in the other
newspapers and viceversa? The sub research questions were therefore the following:
• Did the amount of attention to sustainable development in De Telegraaf causes the
amount of attention in NRC Handelsblad?
• Did the amount of attention to sustainable development in NRC Handelsblad causes the
amount of attention in De Telegraaf?
• Did the amount of attention to sustainable development in Het Financieele Dagblad
causes the amount of attention in NRC Handelsblad?
• Did the amount of attention to sustainable development in NRC Handelsblad causes the
amount of attention in Het Financieele Dagblad?
• Did the amount of attention to sustainable development in Het Financieele Dagblad
causes the amount of attention in De Telegraaf?
• Did the amount of attention to sustainable development in NRC Handelsblad causes the
amount of attention in De Telegraaf?
Method
A dataset was created via a computer assisted content analysis that was conducted using the
digital archive of LexisNexis. Articles were selected via the Boolean search term duurza! OR
"groene energie" OR "zonne-energie" OR "windenergie". The period I analyzed was from 1
1

6. January 1999 until 31 December 2009. This period was chosen, because information about De
Telegraaf is only available from 1999. The search procedure was repeated three times; one time
for every newspaper, so three variables could be created by aggregating the data on a weekly
basis. A total of 35225 articles were found for 581 weeks; 18501 in Het Financieele Dagblad,
10335 in NRC Handelsblad and 6389 in De Telegraaf.
To analyse the effects of the different newspapers on each other, a vector autoregression
(VAR) analysis was conducted in Stata 10.1. A VAR analysis was chosen, because there are no
clear theories about which variables are exogenous in which relations; in a VAR analysis all our
newspaper article variables could be endogenous. To control for possible effects of the various
climate conventions that were held in the period under study, a dummy variable was created for
the weeks1 in which such conferences were held.
Results
I specify in this results section, how the VAR analysis was conducted and which results it
found. In doing this, I follow the procedure described by Brandt and Williams (2007).
VAR model
Figure 1 plots the time series of the attention in the three newspapers for the period that we are
studying. It seems that Het Financieele Dagblad (FD) pays the most attention to sustainable
development and De Telegraaf the least attention. The amount of attention seems to be quite stable
over time, and that is also what augmented Dickey-Fuller tests confirm (see Table 1). Hypotheses
for unit root are rejected, so the data is treated as stationary and I did not need to integrate the data.
Figure 1. The number of articles about sustainable development over time in the three
newspapers of interest.
1
Based on http://en.wikipedia.org/wiki/United_Nations_Framework_Convention_on_Climate_Change
2

7. Table 1. The results of augmented Dickey-Fuller tests for the amount of articles over time
Augmented Dickey-Fuller test FD NRC Telegraaf
Random walk without drift -4.990 -5.596 -6.560
Random walk with drift -10.104 -14.525 -10.878
Random walk with drift and trend -15.716 -16.751 -16.487
Note. All tests indicate the absence of a unit root.
In order to create the VAR, I had to select the appropriate number of lags for the model. Various
models were tested with lag lengths ranging from 1 to 8. Model fit statistics suggest that a model
that includes either two (AIC= 20.93, SBIC= 21.11) or seven lags (AIC = 20.90, SBIC = 21.43)
has the best fit. When lag-order selection statistics for the different VARs are studied, according
to Akaike Info Criterion (AIC) and by inspecting the differences in Log Likelihood a VAR
model with seven lags is preferred; however, according to the Schwarz's Bayesian information
criterion (SBIC) and the Hannan and Quinn information criterion (HQIC) the VAR model with
two lags should be preferred. Finally, I chose to use the VAR model with 2 lags as this model
was much more parsimonious and differences in AIC with models including more lags were
very small.
To check whether this model can be used, the residuals of the three newspaper variables
in this model were tested for autocorrelation with the Ljung–Box Q test statistic and for the
presence of conditional heteroscedasticity with the Engle-Granger test. The residuals of the
number of articles in Het Financieele Dagblad (Q = 26.33, p = .16) and NRC Handelsblad (Q =
15.28, p =.76) reflected white noise, however the residuals of the number of articles in De
Telegraaf seem to autocorrelate (Q = 32.60, p = .04). To solve this, the model was extended by
including another lag, resulting in the VAR model with three lags. This model does not seem to
the problems of autocorrelation in the residuals for Het Financieele Dagblad (Q = 24.89, p = .
21), NRC Handelsblad (Q = 15.76, p =.73) and De Telegraaf (Q = 31.38, p =.05). However, the
squared residuals of Het Financieele Dagblad (Q = 82.49, p < .01) and De Telegraaf (Q = 32.39,
p =.04) indicate the presence of heteroscedasticity; the squared residuals of NRC Handelsblad
do not reflect heteroscedasticity (Q = 10.61, p = .96). However, besides noting it, I did not pay
attention to this, but it will be solved with ARCH and GARCH models later in this Dynamic Data
Analysis course.
The VAR model with three lags explains more than half of the variation in the number of
articles per week in Het Financieele Dagblad (R2 = .577) and De Telegraaf (R2 = .558). The
proportion of explained variance of the number of articles per week in NRC Handelsblad is
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8. somewhat less, though still substantial, because almost a third of the variation is explained (R2 =
.316). The estimated impact of the various variables in the model can be found in Table 2. The
values in this table can be used to understand the direction of effects, but not to understand the
effects of particular lags on a dependent variable. The values are imprecise and standard errors
are high due to multicollinearity in the VAR model; therefore, we used Granger causality
testing to inspect the joint statistical significances of the different independent variables (see
Table 3).
Table 2. VAR estimates for the 3-lag model of the number of articles in different newspapers
Dependent variable
Het Financieele Dagblad NRC Handelsblad De Telegraaf
Het Financieele Dagblad (t-1) 0,32 (-0,04)* 0,11 (0,03)* 0,10 (0,02)*
Het Financieele Dagblad (t-2) 0,17 (0,05)* 0,03 (0,03) 0,02 (0,02)
Het Financieele Dagblad (t-3) 0,03 (0,04) -0,05 (0,03) 0,05 (0,02)*
NRC Handelsblad (t-1) 0,16 (0,07)* 0,23 (0,04)* 0,08 (0,04)*
NRC Handelsblad (t-2) -0,07 (0,07) 0,11 (0,04)* -0,01 (0,04)
NRC Handelsblad (t-3) 0,05 (0,07) 0,01 (0,04) -0,12 (0,04)*
De Telegraaf (t-1) 0,47 (0,08)* 0,08 (0,05) 0,26 (0,04)*
De Telegraaf (t-2) 0,03 (0,09) -0,01 (0,05) 0,20 (0,04)*
De Telegraaf (t-3) 0,11 (0,08) 0,09 (0,05) 0,09 (0,04)*
Climate conventions 0,35 (2,46) 0,71 (1,50) -2,25 (1,28)
Constant 4,97 (1,39)* 7,25 (0,85)* 0,86 (0,72)
Ljung-Box Q(20) residuals 24.89 15.76 31.38
Ljung-Box Q(20) residuals² 82.49* 10.61 32.39*
R2 .577 .316 .558
Note. Unstandardized coefficients. Standard errors in parentheses; * p<.05
To test whether the news coverage about sustainable development in one newspaper Granger-
causes news coverage about sustainable development in another newspaper, I look at the test for
Granger-causality. It becomes clear that De Telegraaf is Granger-caused by both Het
Financieele Dagblad and NRC Handelsblad; the chi-squared test suggest that excluding the
lagged values of Het Financieele Dagblad results in a worse prediction of De Telegraaf (χ2 =
35.67, p < .001), the prediction also becomes worse when NRC Handelsblad’s lagged values are
excluded from the model (χ2 = 14.62, p < .01). This substantially means that news coverage
about sustainability in both Het Financieele Dagblad and NRC Handelsblad cause coverage in
De Telegraaf. From Table 2 we can know that these effects will be positive; more news
coverage about sustainability in Financieele Dagblad and NRC Handelsblad will result in more
news coverage about this issue in De Telegraaf. Similar Granger causality effects are found for
4

9. the news coverage of De Telegraaf on Het Financieele Dagblad (χ2 = 42.98, p < .001) and of
coverage in Het Financieele Dagblad on coverage in NRC Handelsblad (χ2 = 19.10, p < .001);
these effects will be positive as can be found in table 2. Two investigated relations between
newspaper were not significant; news coverage about sustainable development in Het
Financieele Dagblad is not Granger-caused by NRC Handelsblad and news coverage about this
issue in NRC Handelsblad is also not Granger-caused by De Telegraaf. The results of the
Granger causality tests indicate that the coverage of not any of the newspapers are exogenous,
as they all are Granger-caused by one or two other newspapers.
Table 3. Granger causality tests for the number of articles in the different newspapers
Hypothesid exogenous variable Block coefficients restriced χ2 df p
Het Financieele Dagblad NRC Handelsblad 62.998 3 0.098
Het Financieele Dagblad De Telegraaf 42.983 3 0.000
NRC Handelsblad Het Financieele Dagblad 19.099 3 0.000
NRC Handelsblad De Telegraaf 73.269 3 0.062
De Telegraaf Het Financieele Dagblad 35.666 3 0.000
De Telegraaf NRC Handelsblad 14.615 3 0.002
The values of the Granger causality tests do not really help in having a good understanding of
the different effects. Therefore, impulse response analysis were used, by which it is possible to
see the over-time effects of an unexpected one-unit increase in an independent variable in the
future values for the dependent variable. Figure 2 shows the different graphs belonging to the
impulse response analysis. It shows a significant and positive effect of De Telegraaf on Het
Financieele Dagblad, which endures the complete period; one week after an unexpected one-
article increase in De Telegraaf, an increase of about half an article is found in Het Financieele
Dagblad, this effect decays but still resulted in a quarter article increase after eight weeks. The
effect of Het Financieele Dagblad on NRC Handelsblad seems smaller; one week after an
unexpected one-article increase in Het Financieele Dagblad, about a tenth of an article increase
in NRC Handelsblad is expected, this effect drops to zero but does not become insignificant. A
similar effect is found for the coverage about sustainable development in Het Financieele
Dagblad on coverage about this issue by De Telegraaf. The effect of coverage in NRC
Handelsblad on coverage in De Telegraaf is also rather weak, however this effect becomes
insignificant after one week.
5

10. Figure 2. Graphs of the impulse response analysis, title of graph is [impulse variable],
[response variable]
When the consequences of the effects are added together in a cumulative impulse response
analysis, it is possible to see what the result was of a one-article increase in a newspaper on the
other newspapers’ number of articles about this topic summed together over a period of time
(eight weeks in this case). Figure 2 shows the graphs belonging to the cumulative impulse
response analysis. The graph which displays the cumulative effect of De Telegraaf on Het
Financieele Dagblad, shows that an unexpected one-article increase in this popular newspaper
resulted in about three more article in Het Financieele Dagblad after eight weeks. Viceversa, an
unexpected increase of one article in Het Financieele Dagblad results after eight weeks in about
just one more article in De Telegraaf. About the same was found for the effect of Het
Financieele Dagblad on NRC Handelsblad. The weakness of the effect of NRC Handelsblad on
De Telegraaf also becomes clear in this figure; eight weeks after a one-article increase in NRC
Handelsblad this did not led to any increase in the number of articles about sustainable
development in De Telegraaf.
6

11. Figure 3. Graphs of the cumulative impulse response analysis, title of graph is [impulse
variable], [response variable]
Finally, the decomposition of the Forecast Error Variance is assessed to investigate the effects
of the different variables. The graphs belonging to this decomposition are shown in Figure 4.
The decomposition of the Forecast Error Variance determines the impact of one variable’s
forecast error on the error in forecasting other variables; it indicates how much of the series
future values cannot be explained due to unexpected shocks in all the different variables in the
system. After eight weeks for example, 13 percent of the error variance of the number of
articles about sustainable development in Het Financieele Dagblad can be attributed to shocks
in the number of article about this issue in De Telegraaf. Substantially, this means that about a
twentieth of the proportion of variance in this number of articles in Het Financieele Dagblad is
caused by variance in De Telegraaf. Remarkable is the relatively large proportion of Forecast
Error Variance in De Telegraaf that can be attributed to variance in Het Financieele Dagblad;
after eight weeks about 33 percent of this error variance of De Telegraaf can be attributed to
shock in Het Financieele Dagblad. When the diagonal of Figure 4 is assessed, it is clear that the
proportion of error variance that can be attributed to other newspapers is the largest for De
Telegraaf; the error variance of Het Financieele Dagblad and NRC Handelsblad seem to be less
influenced by other newspapers.
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12. Figure 4. Graphs of the decomposition of the Forecast Error Variance, title of graph is
[impulse variable], [response variable]
Conclusion
The answers on the various research questions are: yes, Het Financieele Dagblad Granger-
caused news coverage about sustainable development in both De Telegraaf and NRC
Handelsblad; NRC Handelsblad Granger-caused coverage in De Telegraaf, but not in Het
Financieele Dagblad; and De Telegraaf Granger-caused news coverage about sustainable
development in Het Financieele Dagblad, but not in NRC Handelsblad.
Based on these findings, I conclude that developments in the economy or business world
attract both the attention of popular media and media with a focus on science. In addition,
attention in popular newspapers also causes coverage in business media. On the other hand,
coverage in the scientifically oriented NRC Handelsblad did not causes differences in attention
of business newspaper Het Financieele Dagblad, and NRC Handelsblad, for its part, seems not
to be influenced by the popular medium De Telegraaf. To extend this discussion: the world of
science seems thus not to be interested in what the ordinary people think and business people
are not interested in what science has to offer them. The man in the street on the other hand is
interested in developments of both science and economics, while business people also follow
what ordinary people think is interesting.
Reference
Brandt, P., & Williams, J. T. (2007). Multiple time series models. Thousand Oaks: Sage
Publications.
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13. Do File:
*Climate conventions
gen convention=0
replace convention=1 if week==43 |week== 44|week==99 |week== 100|week==
135|week==136|week== 150|week== 151 |week==201 |week==202 |week==260 |
week==261 |week==314 |week==315 |week==365 |week==366 |week==415 |week==416
|week==471 |week==472 |week==524 |week==525 |week==578 |week==579
replace week = week + 2027
tsset week, weekly
twoway (tsline FD, lcolor(red)) (tsline NRC, lcolor(green) lpattern(dash)
lwidth(medthick)) (tsline Telegraaf, lcolor(blue) lpattern(dash)
lwidth(medium))
*Data for FD was double entered in 2006
replace FD=FD/2 if week>=2398 & week<=2449
twoway (tsline FD, lcolor(red)) (tsline NRC, lcolor(green) lpattern(dash)
lwidth(medthick)) (tsline Telegraaf, lcolor(blue) lpattern(dash)
lwidth(medium))
*with drift
dfuller FD
*random walk
dfuller FD, noconstant
*trend
dfuller FD, trend
*with drift
dfuller NRC
*random walk
dfuller NRC, noconstant
*trend
dfuller NRC, trend
*with drift
dfuller Telegraaf
*random walk
dfuller Telegraaf, noconstant
*trend
dfuller Telegraaf, trend
varsoc FD NRC Telegraaf, maxlag(8) ex(convention)
*two models are prefered: with 2 lags or with 7 lags
*Test the VAR model with 2 lags
var FD NRC Telegraaf, lags(1,2) ex(convention)
vargranger
*Is an independent variable Granger-causing the dependent variable?When we
exclude a variable, does the model get signicantly worse?
*df is the number of variables the model gains.
predict rv1, resid equation (FD)
predict rv2, resid equation (NRC)
predict rv3, resid equation (Telegraaf)
9

14. gen rv1_s = rv1* rv1
gen rv2_s = rv2* rv2
gen rv3_s = rv3* rv3
*test for white noise; p>.05
wntestq rv1, lags(20)
wntestq rv2, lags(20)
wntestq rv3, lags(20)
wntestq rv1_s, lags(20)
wntestq rv2_s, lags(20)
wntestq rv3_s, lags(20)
*autocorrelation in the residuals of De Telegraaf; so one more lag included
*Test the VAR model with 3 lags
var FD NRC Telegraaf, lags(1,2,3) ex(convention)
vargranger
drop rv1 rv2 rv3
drop rv1_s rv2_s rv3_s
predict rv1, resid equation (FD)
predict rv2, resid equation (NRC)
predict rv3, resid equation (Telegraaf)
gen rv1_s = rv1* rv1
gen rv2_s = rv2* rv2
gen rv3_s = rv3* rv3
*test for white noise; p>.05
wntestq rv1, lags(20)
wntestq rv2, lags(20)
wntestq rv3, lags(20)
wntestq rv1_s, lags(20)
wntestq rv2_s, lags(20)
wntestq rv3_s, lags(20)
* no autocorrelation, heteroscedasticity for FD and Telegraaf
var FD NRC Telegraaf, lags(1,2,3) ex(convention)
*impulse response analysis
irf create cms, set(cms)
irf graph irf
irf table irf
irf table cirf
irf graph cirf
*Decompositition of forecast variance
irf graph fevd
irf table fevd
10