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Asymmetric media responses in the Dutch context Does newspapers coverage respond to economic information?Autoregressive Di...
Table of contentsINTRODUCTION................................................................................................
I analyzed was 1 January 1990 until 31 December 2000, as the analyses of Soroka (2006) alsostopped in the year 2000 and Le...
series, it was likely that a unit root was also not present in this series; augmented Dickey-Fuller tests confirmed this. ...
Figure 1. Number of articles about unemployment and unemployment rate between 1990 and 2000.Autoregressive Distributed Lag...
β1*ΔUnemployment(positive) t-1+ β2*ΔUnemployment(negative) t +               β3*ΔUnemployment(negative) t-1 + ε twhere Art...
Ljung-Box Q(20) residuals               22.86              24.82                21.95                    24.44Ljung-Box Q(...
-7.063*ΔUnemployment(positive) t-1+ 1.147*ΔUnemployment(negative) t-1 + ε tAs I have interpreted the short run effect of c...
Applying this formula (or calculating by hand) to the asymmetric model, finds that anincrease in unemployment with one per...
ΔΔUnemployment is the difference in the indicator of the differenced unemployment rate, α0is the constant of the model and...
Again and logically, both models have almost an equal fit; they both explain about one thirdof the variance in the differe...
ConclusionThis study has found that changes in the unemployment rate do not have an effect on thenumber of articles being ...
Do File:*Left rightdrop if yrwk<199002drop if yrwk>200051* declare data to be time seriesreplace nr2 = nr2 + 898tsset nr2,...
*with driftdfuller plus_unempl_rate*random walkdfuller plus_unempl_rate, noconstant*trenddfuller plus_unempl_rate, trendtw...
*ECM*general modelsregress d.N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAKd.diff_unempl_rate l.diff_unempl_ratepredic...
Asymmetric media responses in the Dutch context: Does newspapers coverage respond to economic information? - Autoregressiv...
Asymmetric media responses in the Dutch context: Does newspapers coverage respond to economic information? - Autoregressiv...
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Asymmetric media responses in the Dutch context: Does newspapers coverage respond to economic information? - Autoregressive Distributed Lags and Error Correction Models

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

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

  1. 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
  2. 2. Table of contentsINTRODUCTION.............................................................................................................................................1METHOD........................................................................................................................................................1RESULTS........................................................................................................................................................2 AUTOREGRESSIVE DISTRIBUTED LAGS MODEL.......................................................................................................................4 ERROR CORRECTION MODEL.........................................................................................................................................8CONCLUSION...............................................................................................................................................11REFERENCE..................................................................................................................................................11DO FILE:..........................................................................................................................................................IIntroductionIn this study, I aim to investigate the influence the economy has on newspaper coverage. Withthis, I try to repeat the analysis of Soroka (2006) in a Dutch context. Soroka found thatnewspaper coverage about unemployment in the United Kingdom was stimulated by realdevelopments in the economy, the unemployment rate. However, he also found that negativechanges in the unemployment rate had a much bigger effect on newspaper coverage thanpositive developments. My expectation is that this could also be the case in the Netherlands asHollanders and Vliegenthart (2009) showed that news coverage was negatively affected by thestock 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 twohypotheses: 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.MethodIn order to investigate whether changes in unemployment rate have an effect on Dutchnewspaper coverage, a dataset was created via a computer-assisted content analysis, whichwas conducted using the digital archive of the Web-based version of LexisNexis. Articleswere selected via the Boolean search term ‘werkloosheid OR werkeloosheid’. The period that
  3. 3. I analyzed was 1 January 1990 until 31 December 2000, as the analyses of Soroka (2006) alsostopped 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 containsdata from 1990 on in LexisNexis. Using other newspapers would have led to a too shortperiod. The search resulted in 7652 articles for the whole period. The number of articles wasaggregated, resulting in weekly visibility scores of unemployment in NRC Handelsblad. The variable representing the unemployment rate was obtained via the website ofEurostat; also for the period 1990-2000. Unemployment rate was measured as the percentageof the total labour force. However, as this data was monthly and not weekly, theunemployment rate for intervening moments were calculated by taking the mean of the weekbefore and the next week measured. Because I want to reproduce the study of Soroka in theDutch context, it was necessary to transform the unemployment rate variable in a variable thatindicates the difference in unemployment rate between two time points. In addition, thisdifferenced 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 thedifferenced variable if changes are negative (unemployment decreases), if changes arepositive the value of this variable is zero. ΔUnemployment rate (positive) copies the values ofthe differenced unemployment rate in cases when changes are positive (unemploymentincreases), whereas values are zero if changes are negative (unemployment decreases). Autoregressive Distributed Lags and Error Correction Models were conducted in Stata10.1, to analyse the effects of the unemployment rate on newspaper coverage. Doing this, Ifollowed the stepwise approach described by De Boef and Keele (2008). First the generalmodel was built, which has the lagged dependent variable in it and the contemporary andlagged value of the independent variable. Second, valid restrictions were imposed on thismodel . Finally, the results were interpreted.ResultsIn this results section, the outcomes of both an Autoregressive Distributed Lags model and anError Correction Model are described as both models have unique advantages; ADL modelsestimated 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 aboutunemployment and the unemployment rate itself. The number of articles about unemploymentseems to be quite stable over time, and that is also what augmented Dickey-Fuller testsconfirm (see Table 1). Because I did not use the unemployment rate itself, but the differenced 2
  4. 4. 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 conductedin 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 ΔUnemploymentAugmented Dickey-Fuller test NRC Unemployment rate rate (negative) rate (positive)Random walk without drift -5.045 -21.719 -16.057 -13.831Random walk with drift -14.515 -21.760 -21.073 -17.029Random walk with drift and trend -14.718 -21.817 -21.057 -17.206Note. All tests indicate the absence of a unit root.Next the Autoregressive Distributed Lags Model and the Error Correction Model are describedboth for the independent variable ‘difference in unemployment rate’ and for the asymetricmodel with the same independent variable, that was split in two (positive and negative). 3
  5. 5. Figure 1. Number of articles about unemployment and unemployment rate between 1990 and 2000.Autoregressive Distributed Lags ModelI started my analysis with a general model as De Boef and Keele (2008) recommended,because substantive theory does not provide enough guidance for precise dynamicspecifications; I was only sure about the exogeneity of the unemployment rate. The generalmodels were defined as follows:Articles t = α0 + ( ∑i =1 αi*Articles t-i ) + β0*ΔUnemployment t + β1*ΔUnemployment t-1 + ε t 4Articles t = α0 + ( ∑i =1 αi*Articles t-i )+ β0*ΔUnemployment(positive) t + 4 4
  6. 6. β1*ΔUnemployment(positive) t-1+ β2*ΔUnemployment(negative) t + β3*ΔUnemployment(negative) t-1 + ε twhere Articles is the number of articles published in NRC Handelsblad about unemploymentand |αi| should be less than 1 so the time-series is stationary, ΔUnemployment is the indicatorof the differences in the unemployment rate, α0 is the constant of the model and ε is the errorterm. The first model test for the simple symmetric effect of changes in unemployment on thenumber of articles, whereas the second model makes a difference between positive and negativechanges in the unemployment rate, to test whether those have different effects. The generalmodel takes lag one to four into account, because the general model with the dependentvariable having only one lag showed considerable autocorrelation; for the symmetric modelfor example, Ljung–Box Q test statistic for autocorrelation (Q = 111.69, p < .001) and theEngle-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 asymmetricmodel.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
  7. 7. Ljung-Box Q(20) residuals 22.86 24.82 21.95 24.44Ljung-Box Q(20) residuals² 16.63 17.03 17.04 18.04R2 / Adjusted R2 0.273 / 0.266 0.276 / 0.266 0.272 / 0.266 0.274 / 0.266Note. Cells contain OLS unstandardized regression coefficients with standard errors in parentheses;* p < .10, ** p < .01The symmetric and the asymmetric model fit the data equally well; both explain 26.6 percentof the variance in the number of articles. However, almost none of the independent variableshave a significant effect. The general effect of changes in the unemployment rate (in thesymmetric model) has no significant impact on the number of articles published in NRCHandelsblad about unemployment. As expected the only effect that is significant is the one ofincreases 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 willresult in about 8 fewer articles in the next week. The effect of negative changes in theunemployment rate (when unemployment decreases) is not significant. Because none of the contemporary effects of changes in unemployment are significantand it seems more likely that newspaper coverage is affected by previous unemployment ratesthan 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 (seeDe Boef & Keele, 2008, p.187). To be sure the estimates of the Dead Start models are notworse than those of the general models, the differences in R2 between the general models andthe 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 modelscan be found in Table 2. These results lead to the same conclusions as the ones from the general model: theeffect of changes in unemployment rate are not significant in the symmetric model, and in theasymmetric model are only the positive changes significant. A 1 percent increase of theunemployment in the Netherlands, would lead to a decrease of 7 articles about this topic in thenext 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 tThe 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
  8. 8. -7.063*ΔUnemployment(positive) t-1+ 1.147*ΔUnemployment(negative) t-1 + ε tAs I have interpreted the short run effect of changes in unemployment before, now I willfocus on the effects in the long run. Therefore, I use the long run multiplier (LRM), whichindicates the total effect of an independent variable, because it takes into account that aneffect is distributed over several future time periods. De Boef and Keele (2008) gave thefollowing formula to calculate the long run multiplier: k1 = (β1 + β0) / (1-α1). However, thisformula 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 tocalculate the LRM by hand. The LRM of differences in unemployment in the symmetric DeadStart model was -5.23. This means that a one percent increase in unemployment, leads in thelong run to about five articles less being published about unemployment. The median laglength of this effect is 1; this means that half of the total effect is already reached within thefirst lag. The mean lag length, how long it takes to move back to the equilibrium is 6, afterthis lag the LRM increases with less than 0.01 points. How the LRM is distributed over timeand 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 wasable to come up with a formula for the long run multiplier in Dead Start ADL models withmultiple lags: k1 = ( ∑i =1 (β1) / (1 – α i) ) - β1*(j - 1) jwhere i > 0 (to not take the constant into account) and j is the number of independent variables. 7
  9. 9. Applying this formula (or calculating by hand) to the asymmetric model, finds that anincrease in unemployment with one percent, leads in the long run to a total of -13.35 fewerarticles 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 effecttakes place during the first lag (between t0 and t1). The mean lag length, how long it takes tomove 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 inthe long run to a total of only 2.17 articles fewer articles being published. The median laglength of this effect is also 1. The mean lag length, how long it takes to move back to theequilibrium is 5, after this lag the LRM increases with less than 0.03 points.Error Correction ModelThe analyses above are repeated here with the same data, but now with Error CorrectionModels instead of Autoregressive Distributed Lags Models. General models were againstarting points of the procedure, they were defined respectively for the symmetric and theasymmetric models as follows:ΔArticles t = α0 + ( ∑i =1 αi*Articles t-i) + β0*ΔΔUnemployment t + β1*ΔUnemployment t-1 + ε t 4ΔArticles 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 twhere ΔArticles is the difference in the number of articles published in NRC Handelsbladabout unemployment and |αi| should be less than 1 so the time-series is stationary, 8
  10. 10. ΔΔUnemployment is the difference in the indicator of the differenced unemployment rate, α0is the constant of the model and ε is the error term. The first model test for the simplesymmetric effect of changes in unemployment on the number of articles, while the secondmodel makes a difference between positive and negative changes in unemployment rate, totest 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, showedconsiderable autocorrelation, just as in the ADL models. Table 3 presents the coefficients ofboth 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 24.44Ljung-Box Q(20) residuals² 16.62 17.03 17.04 18.05R2 / Adjusted R2 0.322 / 0.315 0.325 / 0.315 0.321 / 0.315 0.323 / 0.316Note. Cells contain OLS unstandardized regression coefficients with standard errors in parentheses;* p < .10, ** p < .01 9
  11. 11. Again and logically, both models have almost an equal fit; they both explain about one thirdof 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 explainabout 5% more variance of the number of articles. There are two more differences in thesefindings compared with the results found with the ADL models: now there is not any effect ofunemployment significant; and, the coefficient of the first lag of the dependent variable isnegative. As the (differenced) contemporary effects again are not significant and for the samereason as mentioned above, it was appropriate to make the model more parsimonious byconstraining those to be zero. This creates a Dead Start Error Correction Models, whichmakes it possible to compare the results with the findings of the ADL models (see Table 3 forthe coefficients). In the symmetric Dead Start model, unemployment again had no significant effect;conversely, the effect of positive changes in unemployment (when it increases) becamesignificant, 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 resultsform the Dead Start models, the difference in the number of articles can be defined as this forthe 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 tAnd 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 tThose models are almost exactly the same as the models obtained via the ADL model; theydiffer 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 asfound for the results of the ADL models. Therefore, it is not necessary to describe them hereagain. 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 samedata and that the choice for one of both depends on the coefficients that you want to calculatedirectly. 10
  12. 12. ConclusionThis study has found that changes in the unemployment rate do not have an effect on thenumber of articles being published about unemployment. This effect is insignificant in allsymmetric models that were studied. Therefore, the first hypothesis needs to be rejected. Thesecond hypothesis expected that negative changes in the unemployment rate have a strongereffect on the number of articles being published about unemployment than the effects ofpositive changes. Asymmetric models were used to study the difference between both effects.The hypothesis was confirmed, positive changes (when the unemployment increased) indeedhad a stronger effect than negative changes. However this effect was negative, meaning thatincreases in unemployment in the Netherlands, lead to decreases in Dutch newspapercoverage. 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.ReferenceDe 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
  13. 13. Do File:*Left rightdrop if yrwk<199002drop if yrwk>200051* declare data to be time seriesreplace nr2 = nr2 + 898tsset nr2, weeklycodebook leftrightcodebook N_BREAK*Missing values, leftright is average of the two points coming before andafter, articles is 0 as it means there were no articles about unemploymentreplace 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 ifunumpl_rate>= .replace unumpl_rate = (unumpl_rate[_n-1]+unumpl_rate[_n+2])/2 ifunumpl_rate>= .replace unumpl_rate = (unumpl_rate[_n-1]+unumpl_rate[_n+1])/2 ifunumpl_rate>= .replace unumpl_rate = (unumpl_rate[_n-1]+unumpl_rate[_n+4])/2 ifunumpl_rate>= .replace unumpl_rate = (unumpl_rate[_n-1]+unumpl_rate[_n+5])/2 ifunumpl_rate>= .replace unumpl_rate = unumpl_rate[_n-1] if unumpl_rate>= .codebook unumpl_rate leftright N_BREAKtwoway (tsline N_BREAK, lcolor(black))twoway (tsline unumpl_rate, lcolor(black))gen diff_unempl_rate = d.unumpl_ratetwoway (tsline diff_unempl_rate, lcolor(black))gen minus_unempl_rate = 0replace minus_unempl_rate = diff_unempl_rate if diff_unempl_rate<=0gen plus_unempl_rate = 0replace plus_unempl_rate = diff_unempl_rate if diff_unempl_rate>=0*with driftdfuller N_BREAK*random walkdfuller N_BREAK, noconstant*trenddfuller N_BREAK, trend*with driftdfuller diff_unempl_rate*random walkdfuller diff_unempl_rate, noconstant*trenddfuller diff_unempl_rate, trend*with driftdfuller minus_unempl_rate*random walkdfuller minus_unempl_rate, noconstant*trenddfuller minus_unempl_rate, trend
  14. 14. *with driftdfuller plus_unempl_rate*random walkdfuller plus_unempl_rate, noconstant*trenddfuller plus_unempl_rate, trendtwoway (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 driftdfuller d.N_BREAK*random walkdfuller d.N_BREAK, noconstant*trenddfuller d.N_BREAK, trenddfuller d.unumpl_rate*random walkdfuller d.unumpl_rate, noconstant*trenddfuller d.unumpl_rate, trend*most general ADL modelregress N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAK diff_unempl_ratel.diff_unempl_ratepredict r, reswntestq r, lags(20)gen r_s = r*rwntestq r_s, lags(20)drop r r_sregress N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAKminus_unempl_rate l.minus_unempl_rate plus_unempl_rate l.plus_unempl_ratepredict r, reswntestq r, lags(20)gen r_s = r*rwntestq r_s, lags(20)drop r r_s*Dead start modelregress N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAKl.diff_unempl_ratepredict r, reswntestq r, lags(20)gen r_s = r*rwntestq r_s, lags(20)drop r r_sregress N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAKl.minus_unempl_rate l.plus_unempl_ratepredict r, reswntestq r, lags(20)gen r_s = r*rwntestq r_s, lags(20)drop r r_s********************
  15. 15. *ECM*general modelsregress d.N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAKd.diff_unempl_rate l.diff_unempl_ratepredict r, reswntestq r, lags(20)gen r_s = r*rwntestq r_s, lags(20)drop r r_sregress d.N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAKd.minus_unempl_rate l.minus_unempl_rate d.plus_unempl_ratel.plus_unempl_ratepredict r, reswntestq r, lags(20)gen r_s = r*rwntestq r_s, lags(20)drop r r_s*Dead Startregress d.N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAKl.diff_unempl_ratepredict r, reswntestq r, lags(20)gen r_s = r*rwntestq r_s, lags(20)drop r r_sregress d.N_BREAK l.N_BREAK l2.N_BREAK l3.N_BREAK l4.N_BREAKl.minus_unempl_rate l.plus_unempl_ratepredict r, reswntestq r, lags(20)gen r_s = r*rwntestq r_s, lags(20)drop r r_s

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