Multilevel modelling and time series analysis in ... - ERSO - Swov

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Chapter 3manual's objective: "even those who are inexperienced with structural timeseries modelling can use STAMP to familiarise themselves with this approach".It was this, its relatively small size (and price), and its dedicatedness to statespace analysis which made us choose STAMP for structural time seriesmodelling.
3.2 Classical linear and non-linear regression models3.2.1 Classical linear regression modelsChristian Brandstätter and Andrea Angermann (KfV)3.2.1.1. IntroductionThe goal of this chapter is to demonstrate how to apply linear regressionmodels to Austrian road accident fatalities data and how to determine if a trendin the counts of road accident fatalities can be derived. Some practicalcomputations using SPSS software (version 14.0) on these data are presented.However, the different views on the assumptions underlying data testing areconsidered more important than the resulting regression line with itsparameters.Screenshots and descriptions are used to explain the steps and results in theprocess of data analysis; for more detailed information on the theoreticalbackground, please see the corresponding theorie chapter. For furtherexplanations regarding SPSS, we refer the reader to the SPSS user manuals.3.2.1.2. Dataset descriptionAustrian data from the period from 1987 to 2004 is analyzed in this tutorial. Theraw dataset imported to SPSS is shown in the following table. The time variable“Y/M” and number of “Fatalities” can be seen.
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Deliverable D7.5Multilevel modellin
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3.6 State space models.............
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Chapter 1 - IntroductionHeike Marte
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IntroductionFigure 1.2: Structure o
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2.1 Introduction multilevel modelli
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2.2 Multilevel linear regression mo
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2.2 Linear multilevel models Click
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2.2 Linear multilevel modelsResults
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2.2 Linear multilevel models• Cli
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2.2 Linear multilevel modelsThe mod
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2.2 Linear multilevel modelsThe siz
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Chapter 2intercept between location
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Chapter 2Results and interpretation
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2.3 Discrete response models2.3.1 I
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Chapter 2• Click on “Add Term
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Chapter 2Predictor Coefficient π j
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2.3.3 Multinomial responsesHeike Ma
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Chapter 2A new response variable ha
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Chapter 2ResultsAs can be seen in t
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Chapter 2• Select “age” from
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2.3.4 CountsGeorge Yannis, Eleonora
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Page 72 and 73: 2.4 Longitudinal dataHeike Martense
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Page 82 and 83: Chapter 2Now a variable that contai
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Page 106 and 107: Chapter 3The option “Variable Vie
Page 108 and 109: Chapter 3The figure displayed below
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Page 130 and 131: Chapter 33.2.2 Generalized linear m
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Page 146 and 147: Chapter 3In addition to the precedi
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Chapter 33.4.4 ARIMA models for sea
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Chapter 33.4.4.2. Model identificat
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Chapter 3The ACF plot indicates obv
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Chapter 33.4.4.3. Model estimation
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Chapter 3• Click on Plots tab and
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Chapter 3Model Description ARIMA (2
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Chapter 33.4.4.4. Graphical results
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Chapter 3Kolmogorov-Smirnov TestIn
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Chapter 3Here are the SPSS results
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Chapter 3of the residuals, up to or
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Chapter 3HistogramQQ-plot
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Chapter 3As the intervention variab
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Chapter 3The addition of the two ex
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Chapter 3Histogram
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Chapter 3Kolmogorov-Smirnov TestIn
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Chapter 33.5 DRAG modelsThe DRAG mo
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Chapter 3a summary of results and s
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Chapter 3This data file consists of
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Chapter 3 Then click on the Finish
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Chapter 3Independence Q(6,6) 28.8 1
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Chapter 3Step 4: Graphics of model
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Chapter 3 Shortly examine the figur
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Chapter 3The STAMP graphics window
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Chapter 334, we better use the Door
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Chapter 3The auxiliary residuals ar
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Chapter 33.6.2.2 Stochastic level m
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Chapter 3convergence criteria used
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Chapter 36.25Log_NO_fatTrend_Log_NO
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Chapter 3 Go to the STAMP window ag
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Chapter 3 Click OK.
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Chapter 3The list of forecast resul
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Chapter 33.6.3 Local linear trend m
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Chapter 3Irr 0.021360 ( 1.0000) Che
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Chapter 3 In the Residual graphics
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Chapter 33.6.3.2. Stochastic linear
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Chapter 3Estimation sample is 1970.
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Chapter 37.0 Log_FI_fat Trend_Log_F
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Chapter 3The goodness-of-fit is cle
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Chapter 3The STAMP graphics window
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Chapter 33.6.4 Local linear trend p
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Chapter 3Prediction error variance
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Chapter 3Parameter estimation sampl
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Chapter 38.007.757.507.257.000.2Log
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Chapter 3Goodness-of-fit results fo
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Chapter 3interpretation of the test
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Chapter 3The text "Lvl 1983.2" appe
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Chapter 3Normality N 2.40 5.99 +Tab
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Chapter 32Residual Log_UKdriversKSI
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Chapter 32IrrRes Log_UKdriversKSI0.
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Chapter 33.6.6 Explanatory variable
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Chapter 3function has increased fro
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Chapter 3Figure 3.6.18: Observed lo
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Chapter 3 Use the menu or to save
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Chapter 32IrrRes Log_UKdriversKSI0.
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Chapter 3for this period. The actua
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Chapter 3the top figure and the ori
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Chapter 4 - ConclusionThe present d
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Chapter 4researchers conduct valid
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ReferencesLISREL for Windows. Scien
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