Samenvatting Computersessies Onderzoeksmethodes In Finance
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Vak
Onderzoeksmethoden In Finance (F710312A)
Instelling
Universiteit Gent (UGent)
Boek
Introductory Econometrics for Finance
Samenvatting van de computersessies met uitleg van begrippen, werkwijze, formules, interpretaties van alle oefeningen en afbeeldingen van de regressie output
prof: Koen Inghelbrecht
Vak: Onderzoeksmethoden In Finance
Empirical Finance: Complete formula overview per topic
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Universiteit Gent (UGent)
Handelswetenschappen
Onderzoeksmethoden In Finance (F710312A)
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Voorbeeld van de inhoud
Inhoud
Computersessie 1: Introduction .................................................................................................... 4
1 Inspect Excel file .......................................................................................................................... 4
2 Import data.................................................................................................................................. 4
3 Transform data ............................................................................................................................ 4
4 Plot Data ...................................................................................................................................... 5
4.1 Time series plot ................................................................................................................... 5
4.2 XY-plot ................................................................................................................................. 6
4.3 Histogram ............................................................................................................................ 6
5 Normality test.............................................................................................................................. 6
6 Descriptive statistics .................................................................................................................... 7
6.1 Mean and variance .............................................................................................................. 7
6.2 Correlations ......................................................................................................................... 8
7 Exercise: “Equity” ........................................................................................................................ 9
Computersessie 2: Classical Linear Regression Model (CLRM) ...................................................... 13
8 Simple regression ...................................................................................................................... 13
8.1 Example: CAPM ................................................................................................................. 13
8.2 OLS-regressie ..................................................................................................................... 13
8.3 Residuals ............................................................................................................................ 15
8.4 R²: goodness of fit strategies ............................................................................................ 16
8.5 Statistical tests................................................................................................................... 17
9 Exercise: “Equity” ...................................................................................................................... 23
10 Multiple regression.................................................................................................................... 31
10.1 OLS estimation of multiple regression .............................................................................. 31
10.2 Residuals ............................................................................................................................ 33
10.3 Detection of outliers.......................................................................................................... 33
10.4 Dealing with outliers.......................................................................................................... 35
11 Regression with dummy variables............................................................................................. 37
Computersessie 3: CLRM Assumptions and Diagnostic Tests ........................................................ 39
12 Pitfalls in regression models using cross-sectional data ........................................................... 39
12.1 Omitted variable basis ....................................................................................................... 39
12.2 Multicollinearity ................................................................................................................ 41
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,12.3 Omitted variable bias vs. multicollinearity ........................................................................ 43
12.4 Heteroskedasticity ............................................................................................................. 43
13 Working with time series data .................................................................................................. 52
13.1 Example: “Microsoft” ........................................................................................................ 52
13.2 Simple regression model ................................................................................................... 52
13.3 Outliers .............................................................................................................................. 54
14 Pitfalls using time series variables ............................................................................................. 57
14.1 Omitted variable bias ........................................................................................................ 57
14.2 Multicollineariteit .............................................................................................................. 59
14.3 Heteroskedasticiteit .......................................................................................................... 60
15 Additional pitfall: Residual autocorrelation .............................................................................. 63
15.1 Detection ........................................................................................................................... 63
15.2 Example: “badnews” ......................................................................................................... 68
16 Exercises .................................................................................................................................... 75
16.1 Exercise: “Wage discrimination” ....................................................................................... 75
16.2 Exercise: “Earnings” ........................................................................................................... 82
16.3 Exercise: “Philips curve” .................................................................................................... 90
Computersessie 5: Non-Stationarity and Unit Root Testing .......................................................... 95
17 Detecting nonstationary time series ......................................................................................... 95
17.1 Example: “Stock prices on NYSE” ...................................................................................... 95
17.2 Time series plot ................................................................................................................. 95
17.3 Autoregressive model........................................................................................................ 96
17.4 Unit root test ..................................................................................................................... 97
18 Exercise: “Interestrates”.......................................................................................................... 102
Computersessie 7: Models for Panel Data ..................................................................................107
19 Panel data ................................................................................................................................ 107
19.1 Example: “Airline” ........................................................................................................... 107
20 Panel data models ................................................................................................................... 109
20.1 Steps to follow ................................................................................................................. 109
20.2 Pooled OLS: Estimation ................................................................................................... 109
20.3 Individual effects models................................................................................................. 111
21 Exercise: “Panel” ..................................................................................................................... 118
Computersessie 8: Limited Dependent Variable Models .............................................................122
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,22 Limited dependent variable models (LVD) .............................................................................. 122
22.1 Example: “Split ratings” ................................................................................................... 122
22.2 Linear probability model ................................................................................................. 123
22.3 Logit and probit models .................................................................................................. 125
23 Exercise: “Default”................................................................................................................... 129
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, Computersessie 1: Introduction
1 Inspect Excel file
• Kijken hoe Excel file is opgesteld
• moeten er transformaties gedaan worden
• wat is eerste kolom/rij
• …
2 Import data
Toolbar: File/Open Data/User File
Save:
Toolbar: File/Save Data
Goed opletten welk type financiële data:
• time series (= var. varieert doorheen tijd)
• cross-sectional (= data over bv. verschillende bedrijven)
• panel (= data doorheen tijd, over verschillende bedrijven)
3 Transform data
(indien nodig!)
Generate new variable:
Toolbar: Add/Define new variable...
Compute return based on total return index (TRI) and define it as R
R = (TRI-TRI(-1))/TRI(-1) (= rendement fortis)
Exercise: Compute market return based on BEL-20 total return index (BEL20) and define it as RM:
RM= (BEL20 -BEL20(-1))/BEL20 (-1)(= rendement markt)
Exercise: Compute return based on price index (PI) and define it as R2.
R2= (PI-PI(-1))/PI(-1) (=rendement prijsindex (PI))
Build in options: log, squares, etc
Select variable(s),
Toolbar: Add/Logs of selected variables
Toolbar: Add/Squares of selected variables
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