1. Executive Popularity in France: The Promise and Pitfalls of Time Series Data Research and Methods Symposium, October 1 st 2004

2. The problems  The substantive problem: how do macroeconomic conditions affect support for the dual executive (president and prime minister) in France?  The methodological problem: What techniques are best suited to modeling time- series data? Do any of these models have a reliable predictive component (forecasting)

3. Some hypothetical data series

4. Stationary series Y(t) = α, where the estimated constant α is the sample mean

5. Linear trend Y(t) = α + β(t), where α is the intercept and β the slope of the trend line

6. “Random walk” Y(t) = Y(t-1) + α, where α is the mean of the first difference (i.e. average change from one period to the next)

7. France: Executive Popularity

8. France: the dual executive  In de Gaulle’s formulation, the president has responsibility for ‘high politics’, while the prime minister is responsible for ‘day to day affairs’  The president is directly elected (since 1965), for a five year term (since 2002, seven years previously)  The president appoints the prime minister, who is ‘responsible’ to parliament  The president has no constitutional authority to fire the prime minister, but has acquired the de facto capacity to do this  The president may dismiss the national assembly and call for new elections (no more than once a year)  The possibility exists that the president and prime minister may be drawn from different ideological camps (cohabitation)  Cohabitation has occurred three times: 1986-88 (Mitterrand/Chirac), 1993-5 (Mitterrand/Balladur), and 1997-2002 (Chirac/Jospin).

9. Executive popularity in France Lewis-Beck (1980) finds that Prime Ministers suffer a greater decline in popularity than Presidents due to negative effects of inflation and unemployment Hibbs (1981) finds negative effects of unemployment on presidential approval (but positive effect of inflation!) Appleton (1986) finds negative relationship between unemployment and both presidential and prime ministerial approval, but no inflation effect Anderson (1995) suggests that the relationship is more complex, and depends upon the ideological placement of the prime minister vis a vis the president Lecaillon (1980) finds no impact of macroeconomic variables on executive popularity Anonymous (2004) finds that: presidential approval linked to unemployment, prime ministerial approval suffers from higher inflation, and that presidential popularity rises during cohabitation

10. Executive Popularity Indicators in France IFOP (Since 1958, aperiodic until 1968, then monthly): “Are you satisfied with [name] as [President, Prime Minister] of France?” June 2004: Chirac 45% Raffarin 32% SOFRES (Since 1978): “How reliable do you think [name] is in dealing with France’s current problems?” (1974-78): “How effective do you think [name] is in dealing with France’s current problems?” June 2004Chirac 35% Raffarin 28% Also BULL-BVA series (1982-1990’s)

11. Presidential Popularity in France, 1978-2004 Source: SOFRES Mitterrand Chirac Giscard d’Estaing

12. Prime Ministerial Popularity in France, 1978-2004 Source: SOFRES 1.Barre 2.Mauroy 3.Fabius 4.Chirac 5.Rocard 6.Cresson 7.Bérégovoy 8.Balladur 9.Juppé 10.Jospin 11.Raffarin 124 3 10 11 9 8 7 6 5

13. Executive Popularity in France, 1978-2004 Source: SOFRES

14. Executive Popularity in France – Giscard d’Estaing Source: SOFRES

15. Executive Popularity in France – Mitterrand Source: SOFRES Chirac Balladur

16. Executive Popularity in France – Chirac Source: SOFRES Jospin

17. Relating executive popularity to macroeconomic conditions

18. Inflation and Unemployment in France, 1978-2004 Source: INSEE Giscard Mitterrand Chirac

19. Bivariate correlations of presidential and prime ministerial popularity, inflation, and unemployment

20. Simple OLS predicting presidential popularity

21. Extended OLS predicting presidential popularity

22. Extended OLS predicting presidential popularity with lagged dependent variable as predictor

23. Alternative extended OLS predicting presidential popularity with lagged dependent variable as predictor

24. Extended OLS predicting prime ministerial popularity with lagged dependent variable as predictor

25. Alternative extended OLS predicting prime ministerial popularity with lagged dependent variable as predictor

26. Predicted versus actual values from extended OLS model of presidential popularity

27. Predicted versus actual values from extended OLS model of presidential popularity (Giscard)

28. Predicted versus actual values from extended OLS model of presidential popularity (Mitterrand)

29. Predicted versus actual values from extended OLS model of presidential popularity (Chirac)

30. Moving from OLS to ARIMA

31. The autoregressive integrated moving average model (ARIMA) The model incorporates:  The autoregressive term p, which is the order of the autoregressive component  The number of differences d, which is used to discount trends over time  The moving average term q, which is the moving average of the prediction error To fit the model, we need to examine the autocorrelation and the partial autocorrelation functions (ACF and PACF)

32. Differenced series for presidential popularity, 1978-2004

33. Differenced series for prime ministerial popularity, 1978-2004

34. ACF and PACF plots for presidential popularity

35. ACF and PACF plots for presidential popularity (1 difference)

36. ARIMA (1,0,1) model predicting presidential popularity Number of residuals 279 Standard error 3.2791659 Log likelihood -724.93328 AIC 1469.8666 SBC 1506.1787 Analysis of Variance: DF Adj. Sum of Squares Residual Variance Residuals 269 2950.8636 10.752929 Variable B SEBT-RATIO APPROX. PROB. AR1.939614.021920 42.865332.00000000 MA1.225668.065157 3.463472.00062027 MITTERRA 2.522298 4.175431.604081.54629903 CHIRAC.621881 7.942852.078294.93765206 INFLAT -.059998.059933 -1.001086.31768488 UNEMPLOY -2.521530 1.267940 -1.988682.04774939 PM YES.320467. 037488 8.548599.00000000 Time in Office-.143606.034891 -4.115908.00005133 Cohabitation.486023 1.525982.318498.75035403 CONSTANT 66.649067 10.639860 6.264092.00000000

37. Alternative ARIMA (1,0,1) model predicting presidential popularity Number of residuals 273 Standard error 3.2141376 Log likelihood -701.86056 AIC 1425.7211 SBC 1465.4253 Analysis of Variance: DF Adj. Sum of Squares Residual Variance Residuals 262 2732.3972 10.330681 Variables in the Model: B SEB T-RATIO APPROX. PROB. AR1.937443.024247 38.661481.00000000 MA1.283545.066458 4.266519.00002776 MITTERRA -2.919352 4.294587 -.679775.49724719 CHIRAC -5.153437 7.609262 -.677258.49883955 TIO -.080188.036839 -2.176729.03039386 COHAB.957904 1.469957.651655.51519511 POSTELEC 3.371065.881233 3.825394.00016328 PMYES.290923.037170 7.826751.00000000 UNEMPL_2 -1.903296 1.265069 -1.504500.13365712 INFLAT_2 -.076970.141287 -.544777. 58637056 CONSTANT 62.296313 11.302350 5.511802.00000008

38. Predicted versus actual values of presidential popularity from ARIMA model

39. ARIMA versus OLS predicted values

40. Predicted versus actual values of presidential popularity from ARIMA and OLS models (Giscard)

41. Predicted versus actual values of presidential popularity from ARIMA and OLS models (Mitterrand)

42. Predicted versus actual values of presidential popularity from ARIMA and OLS models (Chirac)

43. ARIMA and OLS predicted versus actual values, 1980

44. ARIMA and OLS predicted versus actual values, 1999

45. Correlation matrix of predicted values with actual value of presidential popularity

46. Why choose ARIMA? Linear forecast

47. Random walk forecast

48. ARIMA forecast

49. Pitfalls of ARIMA  Complexities of model specification  Difficulties of interpretation  Sensitivity of data (e.g. missing values)