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Turn left or right: How the economy affects political preferences and media coverage?  -  Multivariate ARIMA models
 

Turn left or right: How the economy affects political preferences and media coverage? - Multivariate ARIMA models

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Turn left or right: How the economy affects political preferences and media coverage?

Turn left or right: How the economy affects political preferences and media coverage?
Multivariate ARIMA models

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    Turn left or right: How the economy affects political preferences and media coverage?  -  Multivariate ARIMA models Turn left or right: How the economy affects political preferences and media coverage? - Multivariate ARIMA models Document Transcript

    • Turn left or right:How the economy affects political preferences and media coverage? Multivariate ARIMA models Assignment 3 Mark Boukes (markboukes@Hotmail.com) 5616298 1st semester 2010/2011 Dynamic Data Analysis Lecturer: Dr. R. Vliegenthart December 1, 2010 Communication Science (Research MSc) Faculty of Social and Behavioural Sciences University of Amsterdam
    • Table of contentsINTRODUCTION.............................................................................................................................................1METHOD........................................................................................................................................................1RESULTS........................................................................................................................................................2 LEFT-RIGHT POLITICAL PREFERENCES..................................................................................................................................3CONCLUSION.................................................................................................................................................6REFERENCES..................................................................................................................................................6APPENDIX 1: DO FILE......................................................................................................................................I
    • IntroductionIn the previous assignment, I found a significant effect of the international financial crisis caused bythe second oil crisis in 1979/1980 on the political preferences Dutch citizens on a left-right scale. Beinginspired by the influence of economic factors on political preferences, I studied in this assignment whatthe effect of unemployment is on political preferences of the Dutch population. Hollanders andVliegenthart (2009) showed in their research how news coverage that was negative about the economy,led to a decreased consumer confidence. In this paper I would like to see if there is also an influence onpolitical preference. Soroka (2006) also found that increased negative economic news coverage leads tomore pessimistic expectations about the future of the economy. To study the effect of unemployment onpolitical preferences, I had the following research questions: o Did the amount of articles about unemployment in NRC Handelsblad affect the average political preference of Dutch citizens? o Did developments in the unemployment rates of the Netherlands affect the average political preference of Dutch citizens?MethodTo investigate which factors have an effect on the left-right preferences of Dutch citizens, I used adataset that contains information about this for a long time period. The NIPO Weeksurveys 1962-20001contained for the period 1977-2000, 1.086.336 individual answers on the following question, Here yousee seven boxes between the words left and right. Could you indicate on this scale how left, right or inbetween your political opinion lies? The observations were transformed in such a way that the meananswer for every week was reported, because the answers were reported individually and aggregate leveldata is needed to answer the research question,. This resulted in 1226 weekly items containing the valuefor the average left-right preference of Dutch citizens, as I only study the period 1990-2000, 560 itemscould be used. To construct a variable containing information about the amount of attention paid tounemployment in newspapers on different moments in time, a computer assisted content analysis wasconducted using the digital archive of LexisNexis. Articles were selected via the Boolean search termwerkloosheid OR werkeloosheid. The period that I analyzed was 1 January 1990 until 31 December2000, as the variable indicating the mean left-right preference was measured until 2000 andLexisNexis contains no data for the period before 1990. Only articles in NRC Handelsblad wereanalyzed, as this newspaper is the only that contains data from 1990 on. Using other newspapers wouldhave led to a too short period. The search resulted in 7652 articles for the whole period. The number ofarticles was aggregated, resulting in weekly visibility scores of unemployment in NRC Handelsblad.1 found on https://easy.dans.knaw.nl/dms 1
    • The variable representing the unemployment rate was obtained via the website of Eurostat; alsofor 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 momentswere calculated by taking the mean of the week before and the next week measured. To analyse the effects of those events, first an adequate ARIMA model is developed for the timeseries of the average left-right preference, this was followed by adding the independent variables to theARIMA model, resulting in multivariate ARIMA models.ResultsI specify in this results section, how the ARIMA model for the average left-right preference wascreated. Thereafter will the results be described of including information about news coverage andunemployment rates into this ARIMA model, with the purpose of explaining political preferences incausal terms. Three timeseries were used in the analyses, the average left-right political preference, theamount of articles about unemployment and the unemployment rate in the Netherlands. Figure 1displays the development of those variables in the period 1990-2000.Figure 1. The development of average political preference, amount of ‘unemployment’ articles in NRC Handelsblad and the unemployment rate in the Netherlands, between 1990 and 2000. 2
    • Left-right political preferencesTo check if it was necessary to integrate the ARIMA model, the time series of the average left-rightpolitical preferences was analyzed with three augmented Dickey-Fuller tests. The results of these tests(see Table 1) indicate that the series had to be differenced, because the Dickey-Fuller test for randomwalk could not be rejected. Therefore, the dependent variable needed to be differenced. The results of thethree augmented Dickey-Fuller tests of the differenced series all could reject the null hypothesis,meaning that no random walk was present (also see Table 1). Therefore, the political preference timeseries does not need to be differenced once more.Table 1. The various augmented Dickey-Fuller tests for the average left-right political preferences Augmented Dickey-Fuller testRandom walk without drift -0.125 *Random walk with drift -11.750Random walk with drift and trend -15.312After integratingRandom walk without drift -38.053Random walk with drift -38.019Random walk with drift and trend -37.984Note. * indicates the presence of a unit root.The next step was predicting the data as good as possible by accounting for its own past, either withautoregressive (AR) terms, moving average (MA) terms or both. This was done by inspecting theautocorrelation (ACF) and partial autocorrelation functions (PACF) (see both graphs in Figure 2). TheACF graph shows a clear spike at lag 1 and little to no significant correlations for other lags, while thePACF graph displays a declining pattern for the first lags. This pattern is indicative for a process with amoving average at lag 1. A ARIMA (0,1,1) model seems thus the right choice. This model was tested forautocorrelation with the Ljung–Box Q test statistic and for the presence of conditional heteroscedasticitywith the Engle-Granger test. The insignificant result of the Ljung-Box Q-test for autocorrelation (20lags) means that the null hypothesis of white noise cannot be rejected and that the absence ofautocorrelation can be assumed (Q= 15.37, p=.755). However, the Engle-Granger test for the presenceof conditional heteroscedasticity gives a significant result, indicating the presence of heteroscedasticity(Q= 79.47, p<.001); nonetheless we paid no attention to this and hope to solve it later with ARCH andGARCH models. The values of this ARIMA (0,1,1) model can be found in Table 2; just as all comingmodels. 3
    • Figure 2. ACF and PCF for the differenced mean score of the average political preference. Table 2. ARIMA model for the differenced mean score of the average political preference. ARIMA (0,1,1) News coverage Unemployment rate Unemployment rateConstant -.000 (.000) -.000 (.000) -.000 (.000) -.000 (.000)Moving average (t - 1) -.838 (.023)* -.835 (.023)* -.838 (.023)* -.835 (.023)*Unemployment coverage (t - 5) -.001 (.000) -.001 (.000)Unemployment rate (t – 1) -.001 (.011) -.002 (.011)Ljung-Box Q(20) residuals 15.37 14.81 15.56 14.77Ljung-Box Q(20) residuals² 79.47 * 83.71* 79.30* 83.67*AIC -1776.91 -1758.14 -1770.89 -1756.18BIC -1763.84 -1740.75 -1753.47 -1734.43Note. Unstandardized coefficients. Standard errors in parentheses; * p<.001 Now we built a model that properly accounts for its own past, I could go on with the next step: assessing the impact of the amount of news coverage in NRC Handelsblad about unemployment on the average political preference of Dutch citizens. As the effect of news coverage is expected to set in within a time-span of 3 months, I considered lags ranging from 1 to 13. The cross-correlation function for the amount of unemployment news coverage and the residuals of the ARIMA(0,1,1) model for average political preference, indicate that the strongest association is present when news coverage is lagged 5 weeks (r = -.086). The ARIMA(0,1,1) model which included the amount of unemployment news coverage, did find similar results for the Ljung-Box Q-test (Q = 14.81, p =.787) and the Engle- Granger test (Q = 83.71, p < .001); indicating the absence of autocorrelation and the presence of conditional heteroscedasticity. Including this variable as an independent variable in the original ARIMA model for average political preference, indicates that the amount of ‘unemployment news coverage’ seems not to influence the political preference of Dutch citizens; the unstandardized coefficient is -.001 (p = .113). The Akaike Info Criterion (AIC) increases with 18.77 points (= -1776.91 ─ -1758.14), which also 4
    • indicates that the model did not get better. However, the model which includes the amount ofunemployment news coverage is better than the model without, according to the difference in log-likelihood, which decreased with 8.39 points, while losing one degree of freedom (p < .01). Thoughthe model did explain variance in average political preference little better, I prefer the standard andmore parsimonious ARIMA(0,1,1) model, because of the insignificant effect of amount ofunemployment news coverage and the increase in AIC.To check whether the real economy had more effect on the political preferences of citizens, I repeat theprocess of including an independent variable, but this time with the unemployment rate. Again Iexpected a potential effect to set in within three months (13 weeks). I analyzed the cross-correlationfunction for this period for the unemployment rate and the residuals of the ARIMA(0,1,1) model foraverage political preference. This indicated that the strongest association is present when theunemployment rate is lagged 1 weeks (r= .063). Including this variable as an independent variable inthe original ARIMA model for the average political preference, indicates that the unemployment rateis also not causing differences in the average political preferences; the unstandardized coefficient is-.001 (p = .963). According to the AIC, did including the unemployment rate to the model not improvemodel fit; this value increased with 6.02 points. The difference in log-likelihood was a decrease with2.02 points (p = .16) while losing one degree of freedom; not significant and thus no indication that themodel fits better. Including the unemployment rate harms model fit thus, just like including the amountof articles in NRC Handelsblad seems to do. The model which included the amount of unemploymentnews coverage, did find similar results for the Ljung-Box Q-test (Q = 15.56, p =.744) and the Engle-Granger test (Q = 79.30, p < .001); indicating the absence of autocorrelation and the presence ofconditional heteroscedasticity.The final model that I tested was the ARIMA(0,1,1) model for the political preferences, whichincluded both the amount of unemployment coverage in NRC Handelsblad at lag 5 and theunemployment rate as independent variables at lag 1 (unemployment rate had also the strongestcorrelation at lag 1 with the residuals for the ARIMA(0,1,1) model which included the amount of newscoverage). In this way, a potential effect of news coverage could be controlled for the real worldcircumstances of the economy. This model again found comparable results for the Ljung-Box Q-test(Q = 14.77, p =.790) and the Engle-Granger test (Q = 83.67, p < .001); indicating the absence ofautocorrelation and the presence of conditional heteroscedasticity. Including both independentvariables, again and as could be expected, led to two insignificant effects: the amount ofunemployment news coverage (b = -.001, p = .111) and unemployment rate (b = -.002, p = .871). Thismodel also made the AIC increase, from -1776.91 to -1756.18; 20.75 points. The difference in log-likelihood also did not point to a significantly better fitted model; a decrease of 8.37 poins while losing 5
    • two degrees of freedom (p = .015). Including both the amount of unemployment articles and theunemployment rate in the ARIMA(0,1,1), thus does not improve model fit.ConclusionBecause I found in the previous assignment an effect of the financial crisis in 1979/1980 on thepolitical preferences of people, I tried to find a comparable effect in this paper, by investigatingpotential effects of news coverage and real world developments of unemployment. My main aim wasto study the influence news coverage about unemployment had on the average political preference on aleft-right scale of the Dutch population. The results make clear that such an effect seems not to exist;changes in political preference are not caused by changes in the amount of political coverage. To see ifthe average political preference was on the other hand affected by real world developments, I looked tothe unemployment rate as another independent variable. However, this also did not seem to have animpact on the average political preference. To check if the oil crisis in 1979 and 1980 was anexception as an economic factor that influenced the political preference, future research should try touse other economic indicators as independent variables.ReferencesHollanders, 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. 6
    • Appendix 1: 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_BREAK*Model buildingtwoway (tsline leftright, lcolor(black))twoway (tsline N_BREAK, lcolor(black))twoway (tsline unumpl_rate, lcolor(black))*with driftdfuller leftright*random walkdfuller leftright, noconstant*trenddfuller leftright, trend*not necessary, but just to check the datadfuller N_BREAK*random walkdfuller N_BREAK, noconstant*trenddfuller N_BREAK, trenddfuller unumpl_rate*random walkdfuller unumpl_rate, noconstant*trenddfuller unumpl_rate, trend*As DF for random walk is not significant, I assume the data show a randomwalk pattern and therefore it is necessary to integrate (differenciate) thedatatwoway (tsline d.leftright, lcolor(black)) i
    • twoway (tsline d.N_BREAK, lcolor(black))twoway (tsline d.unumpl_rate, lcolor(black))*with driftdfuller d.leftright*random walkdfuller d.leftright, noconstant*trenddfuller d.leftright, trend*not necessary, but just to check the datadfuller d.N_BREAK*random walkdfuller d.N_BREAK, noconstant*trenddfuller d.N_BREAK, trend*with driftdfuller d.unumpl_rate*random walkdfuller d.unumpl_rate, noconstant*trenddfuller d.unumpl_rate, trend*Building the ARIMA model for d.leftrightac d.leftrightpac d.leftrightcorrgram d.leftright*The ACF graph shows a clear spike at lag 1 and little to non significantcorrelations for other lags, while the PACF graph displays a decliningpattern for the first lags.*A ARIMA (0,1,1) model is thus the right choicearima d.leftright, ma(1)estat icpredict r, resgen r_s= r*rwntestq r, lags(20)wntestq r_s, lags(20)drop r r_s*The Ljung-Box Q-test for autocorrelation (20 lags) on the residualsindicates that the null hypothesis of white noise cannot be rejected, thisindicate the absence of autocorrelation (Q=xxxxx, p=xxxx). The Engle-Granger test for the presence of conditional heteroscedasticity indicatesthe presence of this as the test does reject the null hypothesis of havingwhite noise (Q=xxxxx, p=xxxxxx).*Cross-correlation function which lag works best statisticallyxcorr r d.N_BREAK, lags(13)correlate r l.d.N_BREAK l2.d.N_BREAK l3.d.N_BREAK l4.d.N_BREAK l5.d.N_BREAKl6.d.N_BREAKcorrelate r l5.d.N_BREAK*Strongest correlation at a lag of 5 weeks (one month)arima d.leftright l5.d.N_BREAK, ma(1)estat icpredict r2, resgen r2_s=r2*r2wntestq r2, lags(20)wntestq r2_s, lags(20)xcorr r d.unumpl_rate, lags(13) ii
    • drop r2 r2_s*N_Breaks effect is not significantarima d.leftright, ma(1)estat icpredict r, resgen r_s= r*rwntestq r, lags(20)wntestq r_s, lags(20)xcorr r d.unumpl_rate, lags(13)correlate r l.d.unumpl_rate l2.d.unumpl_rate l3.d.unumpl_ratel4.d.unumpl_rate l5.d.unumpl_rate l6.d.unumpl_rate*Strongest correlation at lag 1 and lag 2drop r r_sarima d.leftright l1.d.unumpl_rate, ma(1)estat icpredict r2, resgen r2_s=r2*r2wntestq r2, lags(20)wntestq r2_s, lags(20)drop r2 r2_sarima d.leftright l5.d.N_BREAK l.d.unumpl_rate, ma(1)estat icpredict r2, resgen r2_s=r2*r2wntestq r2, lags(20)wntestq r2_s, lags(20)drop r2 r2_s iii