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Tutorial on Heteroskedasticity Using Heteroskedasticityv3 SPSS Macro ¦ 2020 Vol. 16 no. 5 TEACHING Quantitative Methods Vignettes Tutorial ON HeteroskEDASTICITY USING HeteroskedasticityV3 SPSS MACRO Ahmad Daryanto A B A Lancaster University AbstrACT In THIS paper, I DEMONSTRATE HOW TO ASSESS THE HETEROSKEDASTICITY prob- ACTING Editor Het- LEMS IN cross-sectional STUDIES THAT USE LINEAR REGRESSION MODELS USING MY S´EBASTIEN B´ELAND EROSKedasticityV3 (Universit´E DE SPSS macro. I PRESENT TWO ILLUSTRATIVE EXAMPLES INSPIRED FROM Montr´eal) REAL research. This PAPER ALSO PROVIDES THE ANNOTATIONS OF THE MACRO outputs. In MY CLASSROOM DEMONSTRations, STUDENTS WERE ASKED TO ANALYSE DATA SETS USED IN Reviewers Three ANONYMOUS THIS PAPER AND DISCUSS THEIR REGRESSION RESULTS WITH AND WITHOUT IMPLEMENTING ro- REVIEWERS BUST STANDARD errors. The MERITS OF CHECKING FOR THE PRESENCE OF HETEROSKEDASTICITY PRIOR TO ADJUSTING ROBUST STANDARD ERRORS WERE ALSO DISCUSSED IN class. KEYWORDS HeteroskEDASTICITY, cross-sectional studies. TOOLS SPSS, R. B [email protected] 10.20982/tqmp.16.5.v008 LOYAL AND non-loYAL CUSTOMERS IN A population. The distri- Concept TO BE PRESENTED BUTIONS OF THE ERROR TERMS WITHIN EACH GROUP ARE ASSUMED HomoskEDASTICITY IS ONE OF THE BASIC ASSUMPTIONS IN OLS TO BE NORMAL WITH MEAN zero. (ordinary LEAST squares) regression, WHICH STATES THAT THE REGRESSION ERROR TERMS SHOULD HAVE THE SAME SPREADS HomoskEDASTICITY IS A SPECIAL CASE OF HETEROSKedastic- ACROSS ANY VALUES OF INDEPENDENT variables. FOR simplic- ITY. In THIS PARTICULAR example, ERROR TERMS µi ARE het- ITY, LET US CONSIDER A SIMPLE LINEAR REGRESSION MODEL OF cus- EROSKEDASTIC IF THE VARIANCE OF THE ERROR TERMS FOR THE LOYAL TOMERS’ SATISFACTION SCORES ON TYPES OF CUSTOMERS DESCRIBED CUSTOMERS IS NOT THE SAME AS THAT OF THE non-loYAL cus- BY THE FOLLOWING equation: tomers, i.e., V ar(µijLoyali = 1) 6= V ar(µijLoyali = 2 2 2 0) = σi , OR SIMPLY put, σloyal 6= σnon−loyal. In GENERal, Satisfactioni = β0 + β1Loyali + µi (1) HETEROSKEDASTICITY EXISTS IF THE VARIANCE OF THE ERROR IS NOT FOR i = 1; :::; N, WHERE N EQUALS THE NUMBER OF CUSTOMERS CONSTANT AT ANY VALUES OF INDEPENDENT variables. In OLS re- IN A RANDOM sample. Loyali IS A DUMMY VARIABLE THAT GRESSION THAT ASSUMES HOMOSKEDASTIC errors, THE VARIANCE EQUALS 1 FOR LOYAL CUSTOMERS AND EQUALS 0 FOR non-loYAL OF THE REGRESSION PARAMETERS CAN BE CALCULATED ACCORDING customers. The ERROR TERM µi IS NORMALLY DISTRIBUTED WITH TO THE FOLLOWING formula: MEAN EQUALS ZERO AND VARIANCE EQUALS V ar(µijLoyali) = 2 T −1 2 V ar(b) = σ (X X) (2) σi . The ERROR TERMS ARE ASSUMED TO BE independent, WHICH MEANS THAT THEY ARE NOT correlated, i.e., Cov(µi; µj) = WHERE b IS A VECTOR OF THE COEffiCIENT estimates. FOR TWO pa- T 0; for i 6= j. 1. FOR THE ABOVE model, THE HOMOSKedastic- RAMETERS AS IN Equation1, b = (b0 b1) WHERE E(b) = β. ITY ASSUMPTION REQUIRES THE VARIANCE OF THE ERROR TERMS TO X IS AN N × K MATRIX OF INDEPENDENT VARIABLES WHERE BE CONSTANT ACROSS LOYAL vs. non-loYAL customers. That is, K IS REGRESSION PARAMETERS INCLUDING A constant, WHICH 2 V ar(µijLoyali = 1) = V ar(µijLoyali = 0) = σ , OR sim- EQUALS 2 (i.e., THE INTERCEPT AND THE slope) IN Equation (1). 2 2 2 PLY put, σloyal = σnon−loyal. This IMPLIES THAT THE VARIANCE The SYMBOL σ DENOTES THE VARIANCE OF THE ERROR terms, 2 OF THE DISTRIBUTION OF SATISFACTION SCORES IS THE SAME FOR i.e., V ar(µ) = σ . However, WHEN ERROR TERMS ARE het- 1Hereafter, I USE THE TERM RESIDUALS INSTEAD OF ERRORS IF THE PARAMETERS OF THE ABOVE MODEL ARE ESTIMATED USING A RANDOM SAMPLE T Q M P HE UANTITATIVE ETHODS FOR SYCHOLOGY v8 2 ¦ 2020 Vol. 16 no. 5 Figure 1 Scatter PLOT OF CUSTOMER TYPE AND SATISFACTION scores. (a) (b) EROSKedastic, THE ABOVE FORMULA TO COMPUTE THE STANDARD WHERE ai = xi − x¯ (i.e., SUBTRACTING THE MEAN OF xi’S PN 2 ERRORS OF THE REGRESSION COEffiCIENTS ARE NOT CORRECT ANy- FROM xi), AND Sxx = i=1 ai IS THE SUM OF SQUARES OF THE 2 MORE AS σ VALUES ARE NOT CONSTANT ACROSS VALUES OF X. The ai’s. These RESULTS ARE COVERED IN THE TEXTBOOKS MENTIONED GENERAL FORM OF THE VARIANCE OF THE REGRESSION COEffiCIENTS ABOVE AND PRESENTED HERE TO MAKE READERS AWARE OF THE CAN BE SEEN BELOW (see e.g., Stock & Watson, 2015; David- SUBTLE DIFFERENCES IN THE FORMULA FOR COMPUTING STANDARD SON & MacKinnon, 2004; Fox, 2015): ERRORS FOR b1 WHEN HETEROSKEDASTICITY EXISTS (Equation (4)) AND WHEN IT DOES NOT (Equation (5)). I PRESENT THE DETAILS OF V ar(b) = (XT X)−1XT ΩX(XT X)−1 (3) THESE DERIVATIONS IN THE appendix. The HOMOSKEDASTIC vs. HETEROSKEDASTIC REGRESSION sit- WHERE Ω IS THE COVARIANCE MATRIX OF THE ERROR terms. If 2 UATIONS ARE TYPICALLY EXPLAINED USING A GRAPHICAL method. ERROR TERMS ARE HOMOSKEDASTIC (Ω = σ I, WHERE I IS THE Figure1 PROVIDES AN ILLUSTRation. In Panel A OF Figure1, THE N × N IDENTITY matrix), Equation (3) SIMPLIfiES TO Equa- HETEROSKedasticityV3 VARIANCE OF SATISFACTION SCORES OF LOYAL CUSTOMERS IS ROUGHLY TION (2). My MACRO PRESENTED IN THIS EQUAL TO THAT OF THE LOYAL customers. This IS INDICATED BY THE PAPER IMPLEMENTS THIS GENERAL FORMULA AS A BASIS IN calcu- non-signifiCANT DIFFERENCE IN THE STANDARD DEVIATIONS OF THE LATING VARIOUS OPTIONS TO ADJUST STANDARD errors, WHICH WILL SATISFACTION SCORES BETWEEN THE TWO GROUPS OF customers. BE EXPLAINED later. In LITERature, Equation (3) IS ALSO re- As SHOWN IN THE figure, THE VERTICAL SPREADS OF SATISFACTION FERRED AS THE HETEROSKedasticity-consistent COVARIANCE ma- ACROSS THE TWO GROUPS ARE RELATIVELY equal. Equal SPREADS TRIX (HCCM) 2 AND IS ALSO KNOWN AS THE SANDWICH covari- IN THE SATISFACTION SCORES IMPLIES THAT THE REGRESSION residu- ANCE MATRIX (Davidson & MacKinnon, 2004).3. ALS WILL ALSO HAVE THE SAME spreads. Thus, Panel A EXHIBITS FOR A SIMPLE LINEAR REGRESSION model, AS WE ARE inter- 4 HOMOSKEDASTIC ERRORS situations. ESTED IN THE VARIANCE OF THE COEffiCIENT ESTIMATE OF b1, THE In Panel B OF Figure1, THE VARIANCE OF SATISFACTION VARIANCE IS GIVEN BY THE FOLLOWING formula: SCORES ACROSS THE TWO GROUPS OF CUSTOMERS ARE unequal. PN 2 2 As CAN BE SEEN IN THE figure, THE VERTICAL SPREADS OF i=1 ai σi V ar(b1) = (4) THE SATISFACTION SCORES ACROSS THE TWO GROUPS ARE un- PN s2 i=1 i EQUAL—SATISFACTION SCORES ARE MORE SPREAD OUT FOR LOYAL 2 CUSTOMERS THAN THOSE FOR non-loYAL customers. Unequal FOR HOMOSKEDASTIC ERRORS WHERE σi IS THE SAME FOR ALL SPREADS IN THE SATISFACTION SCORES IMPLIES THAT THE regres- i, THE VARIANCE OF b1 IS GIVEN BY SION RESIDUALS WILL ALSO HAVE UNEQUAL SPREADS (Stock & Wat- σ2 son, 2015). Thus, Panel B EXHIBITS HETEROSKEDASTIC ERROR sit- V ar(b ) = 1 2 (5) uations. In BOTH panels, SIMPLE REGRESSION LINES AND COEffi- Sxx 2The WORD ’CORRECTED’ IS ALSO POPULAR INSTEAD OF ’CONSISTENT’. T T −1 3Due TO THE FACT THAT X ΩX IS PLACED BETWEEN TWO SLICES OF (X X) . 4Instead OF PLOTTING THE ACTUAL values, BOXPLOTS ARE RECOMMENDED TO USE AS IT CONTAINS MORE INFORMATION ABOUT DATA e.g., SKewness, outliers, median, MIN AND MAX values. T Q M P HE UANTITATIVE ETHODS FOR SYCHOLOGY v9 2 ¦ 2020 Vol. 16 no. 5 CIENT ESTIMATES WERE ALSO plotted. Readers CAN EASILY VERIFY DEVELOPED BY MacKinnon AND White (1985). HC1 IMPROVES N THE RELATIONSHIP BETWEEN THE COEffiCIENT ESTIMATES AND THE HC0 BY ADJUSTING FOR THE DEGREES OF freedom, i.e., N−K . MEAN SATISFACTION SCORES OF LOYAL vs. non-loYAL customers. HC2 MODIfiED HC1 BY ADJUSTING FOR THE LEVERAGE VALUE OF 0 When HETEROSKEDASTICITY PROBLEM exists, THE STANDARD OBSERVATIONS (hi). These his LIE IN THE MAIN DIAGONAL OF THE T −1 T ERRORS OF THE OLS REGRESSION COEffiCIENTS WILL BE biased. PROJECTION MATRIX P = X(X X) X . HC3 AND HC4 WAS However, THE REGRESSION COEffiCIENTS WILL STILL BE UNBIASED DEVELOPED BY Davidson AND MacKinnon (1993) AND Cribari- (i.e., OVER REPEATED samples, ON AVERAGE THE ESTIMATES WILL Neto (2004), RESPECTIVELY. HC3 MODIfiED HC2 SLIGHTLY AND BE EQUAL TO POPULATION PARameters) AND OLS IS STILL consis- HC4 MODIfiED HC3 BY ADJUSTING RESIDUALS WITH A LEVERAGE TENT (i.e., WILL GET CLOSER TO POPULATION PARAMETERS AS sam- factor. I PRESENT THE MODIfiCATIONS OF THE MATRIX Ω TO esti- T PLE GETS larger) (Stock & Watson, 2015). If REGRESSION er- MATE X ΩX TO CALCULATE EACH OF THESE OPTIONS below: RORS ARE HETEROSKEDASTIC BUT STANDARD OLS IS applied, THE 2 ΩHC0 = diag(ei ) (6) T AND F TEST USED TO TEST HYPOTHESES AND CONfiDENCE inter- VALS ASSOCIATED WITH REGRESSION COEffiCIENTS ARE WRONG be- N 2 ΩHC1 = diag(ei ) (7) CAUSE THE STANDARD ERRORS ARE wrong. Subsequently, OLS N − K 2 RESULTS COULD LEAD TO AN ERRONEOUS CONCLUSION IN REGARD TO ei ΩHC2 = diag (8) HYPOTHESIS testing. FOR example, non-signifiCANT relation- 1 − hi SHIP IN A POPULATION CAN BE SIGNIfiCANT IN A SAMPLE WITH het- 2 2 ei EROSKEDASTIC errors. A SIMPLE REMEDY THAT HAS BEEN recom- Ω = diag HC3 1 − h (9) MENDED IN THE LITERATURE IS TO ADJUST THE STANDARD ERRORS us- i 2 ING THE HETEROSKedasticity-adjusted OR ROBUST STANDARD er- ei n Nhi o 5 ΩHC4 = diag δ ; δi = min 4; (10) RORS (Stock & Watson, 2015). (1 − hi) i K Het- This TUTORIAL AIMS TO DEMONSTRATE THE USE OF MY EROSKedasticityV3 Based ON SIMULATION STUDY OF Long AND Ervin (2000), SPSS MACRO TO ASSESS THE IMPACT OF THE THEY FOUND THAT HC3 PERFORMS WELL IN A SMALL SAMPLE (N ≤ HETEROSKEDASTICITY PROBLEMS ON HYPOTHESIS testing. The 250) - THE STATISTICAL POWER ASSOCIATED WITH TESTING A Hy- MACRO ALLOW USERS TO EYEBALL THE OLS RESULTS WITH OR with- POTHESIS IS HIGHER THAN THAT OF OTHER adjustments.
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