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 2010/2011Dynamic Data Analysis

Lecturer: Dr. R. VliegenthartDecember 9, 2010

Communication Science (Research MSc) Faculty of Social and Behavioural Sciences

University of Amsterdam

Table of contents

INTRODUCTION...................................................................................................................................................1

METHOD.................................................................................................................................................................1

RESULTS.................................................................................................................................................................2

VAR MODEL...........................................................................................................................................................2

CONCLUSION........................................................................................................................................................8

Reference...................................................................................................................................................................8

IntroductionIn 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?

MethodA 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

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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.

ResultsI 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 modelFigure 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

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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 -10.878Random 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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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 newspapersDependent 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 31.38Ljung-Box Q(20) residuals² 82.49* 10.61 32.39*

R2 .577 .316 .558Note. 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

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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 pHet Financieele Dagblad NRC Handelsblad 62.998 3 0.098Het Financieele Dagblad De Telegraaf 42.983 3 0.000

NRC Handelsblad Het Financieele Dagblad 19.099 3 0.000NRC Handelsblad De Telegraaf 73.269 3 0.062

De Telegraaf Het Financieele Dagblad 35.666 3 0.000De 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.

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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.

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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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Figure 4. Graphs of the decomposition of the Forecast Error Variance, title of graph is [impulse variable], [response variable]

ConclusionThe 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.

ReferenceBrandt, P., & Williams, J. T. (2007). Multiple time series models. Thousand Oaks: Sage

Publications.

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Do File:*Climate conventionsgen convention=0replace 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 2006replace 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 driftdfuller FD*random walkdfuller FD, noconstant*trenddfuller FD, trend

*with driftdfuller NRC*random walkdfuller NRC, noconstant*trenddfuller NRC, trend

*with driftdfuller Telegraaf*random walkdfuller Telegraaf, noconstant*trenddfuller 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 lagsvar 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)

gen rv1_s = rv1* rv1gen rv2_s = rv2* rv2gen rv3_s = rv3* rv3

*test for white noise; p>.05wntestq 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 lagsvar FD NRC Telegraaf, lags(1,2,3) ex(convention)vargranger

drop rv1 rv2 rv3drop 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* rv1gen rv2_s = rv2* rv2gen rv3_s = rv3* rv3

*test for white noise; p>.05wntestq 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 irfirf table irf

irf table cirfirf graph cirf

*Decompositition of forecast varianceirf graph fevdirf table fevd

ii