Asymmetric media responses in the Dutch context: Does newspapers coverage respond to economic information? Autoregressive Distributed Lags and Error Correction Models

1. Asymmetric media responses in the Dutch context
Does newspapers coverage respond to economic information?
Autoregressive Distributed Lags and Error Correction Models
Assignment 5
Mark Boukes (markboukes@Hotmail.com)
5616298
1st semester 2010/2011
Dynamic Data Analysis
Lecturer: Dr. R. Vliegenthart
December 16, 2010
Communication Science (Research MSc)
Faculty of Social and Behavioural Sciences
University of Amsterdam

3. Table of contents
INTRODUCTION.............................................................................................................................................1
METHOD........................................................................................................................................................1
RESULTS........................................................................................................................................................2
AUTOREGRESSIVE DISTRIBUTED LAGS MODEL.......................................................................................................................4
ERROR CORRECTION MODEL.........................................................................................................................................8
CONCLUSION...............................................................................................................................................11
REFERENCE..................................................................................................................................................11
DO FILE:..........................................................................................................................................................I
Introduction
In this study, I aim to investigate the influence the economy has on newspaper coverage. With
this, I try to repeat the analysis of Soroka (2006) in a Dutch context. Soroka found that
newspaper coverage about unemployment in the United Kingdom was stimulated by real
developments in the economy, the unemployment rate. However, he also found that negative
changes in the unemployment rate had a much bigger effect on newspaper coverage than
positive developments. My expectation is that this could also be the case in the Netherlands as
Hollanders and Vliegenthart (2009) showed that news coverage was negatively affected by the
stock market’s performance.
To study the effect of unemployment on newspaper coverage, aggregate-level time-
series data for the Netherlands were used from January 1990 to December 2000. I had two
hypotheses:
o H1: Changes in the unemployment rate have an effect on the number of articles published
about unemployment.
o H2: Positive changes in the unemployment rate (economy gets worse) have a stronger
effect on the number of articles published about unemployment than negative changes.
Method
In order to investigate whether changes in unemployment rate have an effect on Dutch
newspaper coverage, a dataset was created via a computer-assisted content analysis, which
was conducted using the digital archive of the Web-based version of LexisNexis. Articles
were selected via the Boolean search term ‘werkloosheid OR werkeloosheid’. The period that

4. I analyzed was 1 January 1990 until 31 December 2000, as the analyses of Soroka (2006) also
stopped in the year 2000 and LexisNexis contains no Dutch data for the period before 1990.
Only articles in NRC Handelsblad were analyzed, as this is the only newspaper that contains
data from 1990 on in LexisNexis. Using other newspapers would have led to a too short
period. The search resulted in 7652 articles for the whole period. The number of articles was
aggregated, resulting in weekly visibility scores of unemployment in NRC Handelsblad.
The variable representing the unemployment rate was obtained via the website of
Eurostat; also for the period 1990-2000. Unemployment rate was measured as the percentage
of the total labour force. However, as this data was monthly and not weekly, the
unemployment rate for intervening moments were calculated by taking the mean of the week
before and the next week measured. Because I want to reproduce the study of Soroka in the
Dutch context, it was necessary to transform the unemployment rate variable in a variable that
indicates the difference in unemployment rate between two time points. In addition, this
differenced unemployment rate variable was used to create to variables ΔUnemployment rate
(negative) and ΔUnemployment rate (positive). In the first, the values are the same as the
differenced variable if changes are negative (unemployment decreases), if changes are
positive the value of this variable is zero. ΔUnemployment rate (positive) copies the values of
the differenced unemployment rate in cases when changes are positive (unemployment
increases), whereas values are zero if changes are negative (unemployment decreases).
Autoregressive Distributed Lags and Error Correction Models were conducted in Stata
10.1, to analyse the effects of the unemployment rate on newspaper coverage. Doing this, I
followed the stepwise approach described by De Boef and Keele (2008). First the general
model was built, which has the lagged dependent variable in it and the contemporary and
lagged value of the independent variable. Second, valid restrictions were imposed on this
model . Finally, the results were interpreted.
Results
In this results section, the outcomes of both an Autoregressive Distributed Lags model and an
Error Correction Model are described as both models have unique advantages; ADL models
estimated short-term effects directly, whereas ECMs are better in avoiding spurious findings
(De Boef & Keele, 2008).
Figure 1 plots the time series of the number of articles in NRC Handelsblad about
unemployment and the unemployment rate itself. The number of articles about unemployment
seems to be quite stable over time, and that is also what augmented Dickey-Fuller tests
confirm (see Table 1). Because I did not use the unemployment rate itself, but the differenced
2

5. series, it was likely that a unit root was also not present in this series; augmented Dickey-
Fuller tests confirmed this. Hypotheses for unit root are rejected for all time-series conducted
in this study, so the data are treated as stationary and I did not need to integrate the data.
Table 1. The results of augmented Dickey-Fuller tests the number of articles and unemployment rate
Articles in Δ in ΔUnemployment ΔUnemployment
Augmented Dickey-Fuller test NRC Unemployment rate rate (negative) rate (positive)
Random walk without drift -5.045 -21.719 -16.057 -13.831
Random walk with drift -14.515 -21.760 -21.073 -17.029
Random walk with drift and trend -14.718 -21.817 -21.057 -17.206
Note. All tests indicate the absence of a unit root.
Next the Autoregressive Distributed Lags Model and the Error Correction Model are described
both for the independent variable ‘difference in unemployment rate’ and for the asymetric
model with the same independent variable, that was split in two (positive and negative).
3

6. Figure 1. Number of articles about unemployment and unemployment rate between 1990 and 2000.
Autoregressive Distributed Lags Model
I started my analysis with a general model as De Boef and Keele (2008) recommended,
because substantive theory does not provide enough guidance for precise dynamic
specifications; I was only sure about the exogeneity of the unemployment rate. The general
models were defined as follows:
Articles t = α0 + ( ∑i =1 αi*Articles t-i ) + β0*ΔUnemployment t + β1*ΔUnemployment t-1 + ε t
4
Articles t = α0 + ( ∑i =1 αi*Articles t-i )+ β0*ΔUnemployment(positive) t +
4
4

7. β1*ΔUnemployment(positive) t-1+ β2*ΔUnemployment(negative) t +
β3*ΔUnemployment(negative) t-1 + ε t
where Articles is the number of articles published in NRC Handelsblad about unemployment
and |αi| should be less than 1 so the time-series is stationary, ΔUnemployment is the indicator
of the differences in the unemployment rate, α0 is the constant of the model and ε is the error
term. The first model test for the simple symmetric effect of changes in unemployment on the
number of articles, whereas the second model makes a difference between positive and negative
changes in the unemployment rate, to test whether those have different effects. The general
model takes lag one to four into account, because the general model with the dependent
variable having only one lag showed considerable autocorrelation; for the symmetric model
for example, Ljung–Box Q test statistic for autocorrelation (Q = 111.69, p < .001) and the
Engle-Granger test for the presence of conditional heteroscedasticity (Q = 32.22, p = .041)
were both significant. Table 2 shows the coefficients of both the symmetric and the asymmetric
model.
Table 2. Autoregressive Distributed Lags Models: unemployment rate and news coverage
General model General model Dead Start model Dead Start model
(symmetric) (asymmetric) (symmetric) (symmetric)
Articles t-1 .330** .333** .327** .329**
(.041) (.042) (.041) (.041)
Articles t-2 .134** .132** .135** .132**
(.044) (.044) (.043) (.043)
Articles t-3 .083* .080* .083* .084*
(.044) (.044) (.043) (.043)
Articles t-4 .124** .129** .125** .135**
(.042) (.043) (.041) (.042)
ΔUnemployment t -1.949
(2.166)
ΔUnemployment t-1 -2.596 -2.788
(2.164) (2.153)
ΔUnemployment(positive) t 1.754
(4.686)
ΔUnemployment(positive) t-1 -8.462 * -7.063*
(4.655) (4.027)
ΔUnemployment(negative) t -4.585
(4.160)
ΔUnemployment(negative) t-1 2.297 1.147
(4.100) (3.801)
Constant 4.339** 4.461** 4.340** 4.510**
(.670) (.685) (.669) (.683)
5

8. Ljung-Box Q(20) residuals 22.86 24.82 21.95 24.44
Ljung-Box Q(20) residuals² 16.63 17.03 17.04 18.04
R2 / Adjusted R2 0.273 / 0.266 0.276 / 0.266 0.272 / 0.266 0.274 / 0.266
Note. Cells contain OLS unstandardized regression coefficients with standard errors in parentheses;
* p < .10, ** p < .01
The symmetric and the asymmetric model fit the data equally well; both explain 26.6 percent
of the variance in the number of articles. However, almost none of the independent variables
have a significant effect. The general effect of changes in the unemployment rate (in the
symmetric model) has no significant impact on the number of articles published in NRC
Handelsblad about unemployment. As expected the only effect that is significant is the one of
increases in unemployment (bad economic news) at lag 1 in the asymmetric model. However,
this effect is in the opposite direction as I expected; a 1-point increase in unemployment will
result in about 8 fewer articles in the next week. The effect of negative changes in the
unemployment rate (when unemployment decreases) is not significant.
Because none of the contemporary effects of changes in unemployment are significant
and it seems more likely that newspaper coverage is affected by previous unemployment rates
than contemporary ones, because journalists plan their articles some days or a week before
(e.g., arranging interviews), I restricted those to be zero; resulting in a Dead Start model (see
De Boef & Keele, 2008, p.187). To be sure the estimates of the Dead Start models are not
worse than those of the general models, the differences in R2 between the general models and
the Dead Start models are taken into account. Those differences are very minor (see Table 2);
therefore, the restrictions can be assumed to be appropriate. Results of the Dead Start models
can be found in Table 2.
These results lead to the same conclusions as the ones from the general model: the
effect of changes in unemployment rate are not significant in the symmetric model, and in the
asymmetric model are only the positive changes significant. A 1 percent increase of the
unemployment in the Netherlands, would lead to a decrease of 7 articles about this topic in the
next week.
The Dead Start model thus looks like this for the symmetric model:
Articles t = 4.340 + .327*Articles t-1 + .135*Articles t-2 + .083*Articles t-1 + .125*Articles t-4 +
-2.788*ΔUnemployment t-1 + ε t
The asymmetric Dead Start model looks like this:
Articles t = 4.510 + .329*Articles t-1 + .132*Articles t-2 + .084*Articles t-1 + .135*Articles t-4 +
6

9. -7.063*ΔUnemployment(positive) t-1+ 1.147*ΔUnemployment(negative) t-1 + ε t
As I have interpreted the short run effect of changes in unemployment before, now I will
focus on the effects in the long run. Therefore, I use the long run multiplier (LRM), which
indicates the total effect of an independent variable, because it takes into account that an
effect is distributed over several future time periods. De Boef and Keele (2008) gave the
following formula to calculate the long run multiplier: k1 = (β1 + β0) / (1-α1). However, this
formula does not take into account that the dependent variable has multiple lags in the model,
like here is the cases with Articles t-1 to Articles t-4 in the model. Therefore, it was necessary to
calculate the LRM by hand. The LRM of differences in unemployment in the symmetric Dead
Start model was -5.23. This means that a one percent increase in unemployment, leads in the
long run to about five articles less being published about unemployment. The median lag
length of this effect is 1; this means that half of the total effect is already reached within the
first lag. The mean lag length, how long it takes to move back to the equilibrium is 6, after
this lag the LRM increases with less than 0.01 points. How the LRM is distributed over time
and what the effects are per lag is shown in Figure 2.
Figure 2. Left: Long Run Multiplier Graph of the effect of an increase in unemployment on
newspaper coverage. Right: Effect of unemployment per lag.
Calculating the LRM by hand also made me understand this process better and therefore I was
able to come up with a formula for the long run multiplier in Dead Start ADL models with
multiple lags:
k1 = ( ∑i =1 (β1) / (1 – α i) ) - β1*(j - 1)
j
where i > 0 (to not take the constant into account) and j is the number of independent variables.
7

10. Applying this formula (or calculating by hand) to the asymmetric model, finds that an
increase in unemployment with one percent, leads in the long run to a total of -13.35 fewer
articles that are published (Figure 3 displays the Long Run Multiplier of the different lags).
The median lag length of this effect is 1; this means that already more than half of the effect
takes place during the first lag (between t0 and t1). The mean lag length, how long it takes to
move back to the equilibrium is 6, after this lag the LRM increases with less than 0.03 points.
Figure 3. Left: Long Run Multiplier Graph of the effect of an increase in unemployment on
newspaper coverage. Right: Effect of unemployment per lag.
On the other hand, a decrease of one percent in unemployment (negative change) results in
the long run to a total of only 2.17 articles fewer articles being published. The median lag
length of this effect is also 1. The mean lag length, how long it takes to move back to the
equilibrium is 5, after this lag the LRM increases with less than 0.03 points.
Error Correction Model
The analyses above are repeated here with the same data, but now with Error Correction
Models instead of Autoregressive Distributed Lags Models. General models were again
starting points of the procedure, they were defined respectively for the symmetric and the
asymmetric models as follows:
ΔArticles t = α0 + ( ∑i =1 αi*Articles t-i) + β0*ΔΔUnemployment t + β1*ΔUnemployment t-1 + ε t
4
ΔArticles t = α0 + ( ∑i =1 αi*Articles t-i ) + β0*ΔΔUnemployment(positive) t +
4
β1*ΔUnemployment(positive) t-1+ β2*ΔΔUnemployment(negative) t +
β3*ΔUnemployment(negative) t-1 + ε t
where ΔArticles is the difference in the number of articles published in NRC Handelsblad
about unemployment and |αi| should be less than 1 so the time-series is stationary,
8

11. ΔΔUnemployment is the difference in the indicator of the differenced unemployment rate, α0
is the constant of the model and ε is the error term. The first model test for the simple
symmetric effect of changes in unemployment on the number of articles, while the second
model makes a difference between positive and negative changes in unemployment rate, to
test whether those have different effects. The models take lag one to four into account,
because the general model with the dependent variable being lagged only once, showed
considerable autocorrelation, just as in the ADL models. Table 3 presents the coefficients of
both the symmetric and the asymmetric model.
Table 3. Error Corrections Models: unemployment rate and news coverage
General model General model Dead Start model Dead Start model
(symmetric) (asymmetric) (symmetric) (symmetric)
Articles t-1 -.670** -.666** -.672** -.671**
(.041) (.042) (.041) (.041)
Articles t-2 .134** .132** .135** .132**
(.044) (.043) (.043) (.043)
Articles t-3 .083* .079* .083* .084*
(.044) (.044) (.043) (.043)
Articles t-4 .123** .129** .125** .135**
(.041) (.0425) (.041) (.042)
ΔΔUnemployment t -1.949
(2.165)
ΔUnemployment t-1 -4.546 -2.788
(2.907) (2.153)
ΔΔUnemployment(positive) t 1.754 -7.064*
(4.686) (4.027)
ΔUnemployment(positive) t-1 -6.708
(4.725)
ΔΔUnemployment(negative) t -4.586
(4.160)
ΔUnemployment(negative) t-1 -2.288 1.147
(4.890) (3.801)
Constant 4.338** 4.461** 4.340** 4.510**
(.670) (.685) (.670) (.683)
Ljung-Box Q(20) residuals 22.86 24.82 21.95 24.44
Ljung-Box Q(20) residuals² 16.62 17.03 17.04 18.05
R2 / Adjusted R2 0.322 / 0.315 0.325 / 0.315 0.321 / 0.315 0.323 / 0.316
Note. Cells contain OLS unstandardized regression coefficients with standard errors in parentheses;
* p < .10, ** p < .01
9

12. Again and logically, both models have almost an equal fit; they both explain about one third
of the variance in the difference of the number of articles that are published every week.
However, the model fit is different than those found with ADL models; ECM models explain
about 5% more variance of the number of articles. There are two more differences in these
findings compared with the results found with the ADL models: now there is not any effect of
unemployment significant; and, the coefficient of the first lag of the dependent variable is
negative. As the (differenced) contemporary effects again are not significant and for the same
reason as mentioned above, it was appropriate to make the model more parsimonious by
constraining those to be zero. This creates a Dead Start Error Correction Models, which
makes it possible to compare the results with the findings of the ADL models (see Table 3 for
the coefficients).
In the symmetric Dead Start model, unemployment again had no significant effect;
conversely, the effect of positive changes in unemployment (when it increases) became
significant, just as in the ADL model. An increase of the unemployment by one percent,
would lead to about seven articles less being published in the next week. Following the results
form the Dead Start models, the difference in the number of articles can be defined as this for
the symmetric model:
ΔArticles = 4.340 + -.672*Articles t-1 + .135*Articles t-2 + .083*Articles t-1 + .125*Articles t-4 +
-2.788*ΔUnemployment t-1 + ε t
And the asymmetric Dead Start model looks like this:
ΔArticles = 4.510 + -.671*Articles t-1 + .132*Articles t-2 + .084*Articles t-1 + .135*Articles t-4 +
-7.064*ΔUnemployment(positive) t-1 + 1.147*ΔUnemployment(negative) t-1 + ε t
Those models are almost exactly the same as the models obtained via the ADL model; they
differ only on the coefficient for the first lag of the number of articles about unemployment.
Calculating by hand also leads to the same long run multipliers (LRM) for both models as
found for the results of the ADL models. Therefore, it is not necessary to describe them here
again. That the results are the same, once again proves the equivalence of both models; and,
thus also the claim of De Boef and Keele (2008) that ADL and ECM can be used for the same
data and that the choice for one of both depends on the coefficients that you want to calculate
directly.
10

13. Conclusion
This study has found that changes in the unemployment rate do not have an effect on the
number of articles being published about unemployment. This effect is insignificant in all
symmetric models that were studied. Therefore, the first hypothesis needs to be rejected. The
second hypothesis expected that negative changes in the unemployment rate have a stronger
effect on the number of articles being published about unemployment than the effects of
positive changes. Asymmetric models were used to study the difference between both effects.
The hypothesis was confirmed, positive changes (when the unemployment increased) indeed
had a stronger effect than negative changes. However this effect was negative, meaning that
increases in unemployment in the Netherlands, lead to decreases in Dutch newspaper
coverage. This is opposite to the expectation and also contrary to the results of Soroka (2006).
How this can be explained is a good question for further research.
Reference
De Boef, S., & Keele, L. (2008). Taking time seriously. American Journal of Political
Science, 52(1), 184-200.
Hollanders, D., & Vliegenthart, R. (2009). The Influence of Negative Newspaper Coverage on
Consumer Confidence: The Dutch Case, CentER Discussion Paper Series (Vol. 2009).
Tilburg: University of Tilburg.
Soroka, S. N. (2006). Good news and bad news: Asymmetric responses to economic
information. Journal of Politics 68(2), 372-385.
11

15. Do File:
*Left right
drop if yrwk<199002
drop if yrwk>200051
* declare data to be time series
replace nr2 = nr2 + 898
tsset nr2, weekly
codebook leftright
codebook N_BREAK
*Missing values, leftright is average of the two points coming before and
after, articles is 0 as it means there were no articles about unemployment
replace leftright= (leftright[_n-1]+leftright[_n+1])/2 if leftright>= .
replace leftright= (leftright[_n-1]+leftright[_n+2])/2 if leftright>= .
replace N_BREAK = 0 if N_BREAK>= .
replace unumpl_rate = (unumpl_rate[_n-1]+unumpl_rate[_n+3])/2 if
unumpl_rate>= .
replace unumpl_rate = (unumpl_rate[_n-1]+unumpl_rate[_n+2])/2 if
unumpl_rate>= .
replace unumpl_rate = (unumpl_rate[_n-1]+unumpl_rate[_n+1])/2 if
unumpl_rate>= .
replace unumpl_rate = (unumpl_rate[_n-1]+unumpl_rate[_n+4])/2 if
unumpl_rate>= .
replace unumpl_rate = (unumpl_rate[_n-1]+unumpl_rate[_n+5])/2 if
unumpl_rate>= .
replace unumpl_rate = unumpl_rate[_n-1] if unumpl_rate>= .
codebook unumpl_rate leftright N_BREAK
twoway (tsline N_BREAK, lcolor(black))
twoway (tsline unumpl_rate, lcolor(black))
gen diff_unempl_rate = d.unumpl_rate
twoway (tsline diff_unempl_rate, lcolor(black))
gen minus_unempl_rate = 0
replace minus_unempl_rate = diff_unempl_rate if diff_unempl_rate<=0
gen plus_unempl_rate = 0
replace plus_unempl_rate = diff_unempl_rate if diff_unempl_rate>=0
*with drift
dfuller N_BREAK
*random walk
dfuller N_BREAK, noconstant
*trend
dfuller N_BREAK, trend
*with drift
dfuller diff_unempl_rate
*random walk
dfuller diff_unempl_rate, noconstant
*trend
dfuller diff_unempl_rate, trend
*with drift
dfuller minus_unempl_rate
*random walk
dfuller minus_unempl_rate, noconstant
*trend
dfuller minus_unempl_rate, trend

16. *with drift
dfuller plus_unempl_rate
*random walk
dfuller plus_unempl_rate, noconstant
*trend
dfuller plus_unempl_rate, trend
twoway (tsline d.N_BREAK, lcolor(black))
twoway (tsline d.unumpl_rate, lcolor(black))
twoway (tsline minus_unempl_rate, lcolor(black))
twoway (tsline plus_unempl_rate, lcolor(black))
*with drift
dfuller d.N_BREAK
*random walk
dfuller d.N_BREAK, noconstant
*trend
dfuller d.N_BREAK, trend
dfuller d.unumpl_rate
*random walk
dfuller d.unumpl_rate, noconstant
*trend
dfuller d.unumpl_rate, trend
*most general ADL model
regress N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAK diff_unempl_rate
l.diff_unempl_rate
predict r, res
wntestq r, lags(20)
gen r_s = r*r
wntestq r_s, lags(20)
drop r r_s
regress N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAK
minus_unempl_rate l.minus_unempl_rate plus_unempl_rate l.plus_unempl_rate
predict r, res
wntestq r, lags(20)
gen r_s = r*r
wntestq r_s, lags(20)
drop r r_s
*Dead start model
regress N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAK
l.diff_unempl_rate
predict r, res
wntestq r, lags(20)
gen r_s = r*r
wntestq r_s, lags(20)
drop r r_s
regress N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAK
l.minus_unempl_rate l.plus_unempl_rate
predict r, res
wntestq r, lags(20)
gen r_s = r*r
wntestq r_s, lags(20)
drop r r_s
********************

17. *ECM
*general models
regress d.N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAK
d.diff_unempl_rate l.diff_unempl_rate
predict r, res
wntestq r, lags(20)
gen r_s = r*r
wntestq r_s, lags(20)
drop r r_s
regress d.N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAK
d.minus_unempl_rate l.minus_unempl_rate d.plus_unempl_rate
l.plus_unempl_rate
predict r, res
wntestq r, lags(20)
gen r_s = r*r
wntestq r_s, lags(20)
drop r r_s
*Dead Start
regress d.N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAK
l.diff_unempl_rate
predict r, res
wntestq r, lags(20)
gen r_s = r*r
wntestq r_s, lags(20)
drop r r_s
regress d.N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAK
l.minus_unempl_rate l.plus_unempl_rate
predict r, res
wntestq r, lags(20)
gen r_s = r*r
wntestq r_s, lags(20)
drop r r_s