The document provides an overview of modeling and forecasting techniques using EViews software. It discusses the main forecasting steps, which include preparing and processing data, estimating linear equations and pool data equations, univariate time series modeling and forecasting, and using error correction models for long-term and short-term prediction. The document uses examples of consumer price index data to demonstrate preparing data, identifying appropriate time series models like ARMA, and generating forecasts using Box-Jenkins and Holt-Winters exponential smoothing methods.

1. Modeling and
forecasting on EVIEWS
TRAINING PROVIDED BY NASREDDINE DRIDI 2021
STATISTICAL ENGINEER
DIRECTOR OF MACROECONOMIC FORECASTS

2. Main forecasting steps
I. Preparing and processing the required data
II. Elaboration of linear equations (sectoral value added, production, intermediate
consumption) and exploiting them for forecasting
III. Estimate the pool data equations (IPI idustrual sectoriel production indicator)
IV. Estimate the equations system
V. Time Series: Univariate Modeling (CPI) and Forecasting
VI. Targeting ECModels for long-term and short-term prediction
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3. I. Preparing and processing the required data
Date Variable1 Variable2
D1 V11 V21
D2 V12 V22
D3 V13 V31
D4 V14 V41
Date Individu Variable1 Variable2
D1 1 V111 V211
D2 1 V112 V212
D3 1 V113 V311
D1 2 V121 V221
D2 2 V122 V222
D3 2 V123 V223
D1 3 V131 V231
D2 3 V132 V232
D3 3 V133 V233
Data for estimate Pool Data
Data for estimate Time series
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4. I. Preparing and processing the
required data
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5. I. Preparing and processing the
required data
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6. I. Preparing and processing the
required data
 For Annual Data : 1983 : 2021
 For semestriel Data : 1983S1 : 2021S2
 For trimestriel Data : 1983T1 : 2021T2
 For mensuel Data : start date 1983:1 End date 2021:12
 For weekly Data : nombre of month : nombre of day : year exp: 08:10:1983
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7. I. Preparing and processing the
required data
 Exemple
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8. I. Preparing and processing the
required data
 Import Data : Proc/Import/Read text-lotus-Excel
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9. I. Preparing and processing the
required data
 Import Data : Proc/Import/Read text-lotus-Excel
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10. I. Preparing and processing the
required data
 Convert data from annual to quarterly : TMM Money market rate
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11. I. Preparing and processing the
required data
 Convert data from annual to quarterly
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12. I. Preparing and processing the
required data
 Convert data from ammual to quarterly
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13. I. Preparing and processing the
required data
 Convert data from ammual to quarterly
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‫البيانات‬ ‫تحويل‬
‫إلى‬ ‫السنوية‬
‫أي‬ ‫ربعية‬
‫من‬ ‫تحويل‬
‫األقل‬ ‫التكرار‬
‫التكرار‬ ‫إلى‬
‫األعلى‬

14. I. Preparing and processing the
required data
 Convert data from ammual to quarterly
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15. I. Preparing and processing the
required data
 Generate a new variable
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16. II. Estmation of linear equations
 Estimate Equation: Quick/Estimate Equation
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17. II. Estmation of linear equations : Multiple regression
 Estimate Equation: Resids
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18. II. Estmation of linear equations : Multiple regression
 Residual Test: Pas d’autocorrelation des résidus
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19. II. Estmation of linear equations : Multiple regression
 white Test: Pas d’autocorrelation des résidus P-value>5% we accepte the homoscedasticity
hypothesis H0
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20. II. Estmation of linear equations : Multiple regression
 Breush-Godfrey Test: Pas d’autocorrelation des résidus
P-value>5% we don’t reject the no correlation
hypothesis H0
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21. II. Estmation of linear equations : Multiple regression
 Test of Multicollinearity :
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22. II. Estmation of linear equations : Multiple regression
 Test of Multicollinearity :
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23. II. Estmation of linear equations : Multiple regression
 Test of Multicollinearity : No multicolinearity ‫منخفض‬ ‫المتعدد‬ ‫الخطي‬ ‫التداخل‬
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24. II. Estmation of linear equations : Multiple regression
 Reset test of Ramsey: H0 :the model is well specified vs H1 : the model is incorrectly specified
P-value>5% we don’t reject the H0 hypothesis
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25. II. Estmation of linear equations : Multiple regression
 Stability test of Chow: H0 :the model is stable vs H1 : the model is not stable
P-value<5% 2001 is a brekpoint
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26. II. Estmation of linear equations : Multiple regression
 Forecast quality:
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27. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Create a pool data project
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28. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Create a pool data project
we must introduce the individual
codes
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29. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Create a pool data project
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30. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Import pool data
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31. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Import pool data
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32. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Import pool data
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33. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Data representation
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34. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Unit Root Test
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35. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Descriptive Statistics
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36. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 𝑦𝑖𝑡 = ∁ + 𝛼𝑖 + 𝛽𝑖𝑡𝑋𝑖𝑡 + 𝜀𝑖𝑡
cte Indivual effect Vector of estimators Vector of variables residue
 Random individual effect:
𝑦𝑖𝑡 = ∁ + 𝛽𝑖𝑡𝑋𝑖𝑡 + 𝛼𝑖 + 𝜀𝑖𝑡
𝑈𝑖𝑡 = 𝛼𝑖 + 𝜀𝑖𝑡
 Fixe individual effect:
𝑦𝑖𝑡 = ∁ + 𝛼𝑖 + 𝛽𝑖𝑡𝑋𝑖𝑡 + 𝜀𝑖𝑡
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37. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Pool Estimation
Estimation
Pool
Random Individual effect
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38. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Pool Estimation : Random Individual Effect
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39. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Pool Estimation : Fixed Individual Effect
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40. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Hausman Test: Random Individual Effect
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We accept H0: Random
individuel effect

41. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Random Individual Effect
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42. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Make system : Fixed Individual Effect
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43. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 Pool Estimation : Fixe Individual Effect
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44. III. Estimate the pool data equations (IPI idustrual
sectoriel production indicator)
 IPI forecasting
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45. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 View/Correlogram
Existance of Unit root
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46. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 View/Correlogram
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47. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 View/Correlogram
Unit Root
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48. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 View/Correlogram
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49. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 Process is identified from the functions of autocorrelation and partial autocorrelation
 AR(p) process : the partial
autocorrélation of ordre p+1 is null
 MA(q) process : The partial
autocorrélation of ordre q+1 is null
 The possible process are AR(1),
MA(1), ARMA(1,1)
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50. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 Estimation of ARMA(1,1) process
the
component
is not
significant
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51. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 Estimation of AR(1) process
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52. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 Make Risidual Test : unit Root Test
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53. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 Make Risidual Test : unit Root Test
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We rejet H0 hypothesis:
Resid are stationnary

54. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 Forecast : Box-jenkins
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55. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 Forecast
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56. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 Forecast : Non parametric method :Lissage expononciel Double: Holt –winters non saisonnière
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57. IV. Time Series: Univariate Modeling (CPI) and
Forecasting
 Exemple : Consumption price Index (CPI)
 Forecast : Non parametric method :Lissage expononciel Double: Holt –winters non saisonnière
IPC 2020
ECModel 6,5
Box-Jenkis 4,48
Holt-winters 6,68
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58. V. Time series: multivariate study
 Causality Test : CPI-M3-NEER
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59. V. Time series: multivariate study
 Causality Test : View/Granger Causality
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We accept the
hypothesis of
causality from
M3 to IPC

60. V. Time series: multivariate study
 Model VAR: Objects/New Objecs/VAR
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61. V. Time series: multivariate study
 Vector Autoregression Estimate
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62. V. Time series: multivariate study
 View/Lag Structure/Block Exogeneity Tests
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NEER
Exogenous
variable

63. V. Time series: multivariate study
 Model VAR: VAR Specification
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64. VI. Targeting ECModels for long-term and short-term
prediction
 Data Base
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65. VI. Targeting ECModels for long-term and short-term
prediction
 Modeling 3 equations with ECModels : 1.producer price index 2.PCI 3.private consumption
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66. VI. Targeting ECModels for long-term and short-term
prediction
 Cointegration Test : Test de johanson
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67. VI. Targeting ECModels for long-term and short-term
prediction
 Cointegration Test : Test de johanson
Existence of at least two cointegration relations
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68. VI. Targeting ECModels for long-term and short-term
prediction
 Cointegration Test : Estimation of VECM(1) Model
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𝐷 𝑌𝑡 = 𝐷 𝑋𝑡 + σ(𝑌𝑡−1 − 𝑋𝑡−1)
𝜎 < 0
𝐷 𝑌𝑡 = 𝐷 𝑋𝑡 + σ𝜀𝑡−1

69. VI. Targeting ECModels for long-term and short-term
prediction
 Cointegration Test : Estimation of VECM(1) Model
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the long-
term
relation
the Short-term
relation
the return to long-term
equilibrium takes 14 month

70. VI. Targeting ECModels for long-term and short-term
prediction
 Cointegration Test : Test of Ljung-Box
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71. VI. Targeting ECModels for long-term and short-term
prediction
 Cointegration Test : Test of Ljung-Box
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72. VI. Targeting ECModels for long-term and short-term
prediction
 Estimate ECModel : Cointegration Price Idex equation
Force de rappelle
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73. VI. Targeting ECModels for long-term and short-term
prediction
 Estimate ECModel : Cointegration real wage equation
Force de rappelle
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74. VI. Targeting ECModels for long-term and short-term
prediction
 simultaneous equation system
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75. VI. Targeting ECModels for long-term and short-term
prediction
 simultaneous equation system
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76. VI. Targeting ECModels for long-term and short-term
prediction
 simultaneous equation system : exp Production Function CES
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Bloc à estimer pour chaque secteur.
Stock de capital du secteur i:
ki
t = yi
t ― s (cki
t ― pyi
t) + (s―1) gki.t + k0
Emploi total dans le secteur i:
li
t = yi
t + s (wi
t ― pyi
t) + (s ― 1)gli.t + l0
Prix de la valeur ajoutée du secteur i:
pyi
t = π0 (cki
t ― gki.t) + (1― π0) (wi
t ― gli.t) + py0

77. VI. Targeting ECModels for long-term and short-term
prediction
 simultaneous equation system : The estimators
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78. VI. Targeting ECModels for long-term and short-term
prediction
 simultaneous equation system : All variables of Model
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Inflation
Forecast

79. VI. Targeting ECModels for long-term and short-term
prediction
 choice of hypothesis
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80. Thanks For Attention
nasreddine.dridi@yahoo.fr
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