Where do Moderation Terms Come from in Binary Choice Models?

• Autorzy: Alfredo A. Romero
• Języki publikacji: EN

Abstrakty

EN

If the most parsimonious behavioral model between an observed behavior, Y, and some factors, X, can be defined as f(Y|X₁,X₂), then f_x1 will measure the impact in behavior of a change in factor X₁. Additionally, if f_x1x2 ≠ 0, then the impact in behavior of a change in factor X₁ is qualified, or moderated by X₂. If this is the case, X₂ is said to be a moderating variable and f_x1x2 is said to be the moderating effect. When Y is modeled via a logistic regression, the moderation effect will exist regardless of whether the index function of the logit specification includes a moderation term or not. Thus, including a moderation terms in the index function will help the researcher more precisely qualify the moderation effect between X₁ and X₂. The question that naturally arises is whether the researcher must include the moderation term or not. In this document, we provide the conditions in which moderation terms will naturally arise in a logistic regression and introduce some modeling guidelines. We do so by introducing a general framework that nests models with no moderation terms in three scenarios for the independent variables, commonly found in applied research. (original abstract)

Słowa kluczowe

EN

Logit model; Models of choice; Regression models

PL

Model logitowy; Modele wyboru; Modele regresji

• Czasopismo: Central European Journal of Economic Modelling and Econometrics (CEJEME)
• Rocznik: 2014
• Tom: 6
• Numer: nr 1
• Strony: 57--68

Twórcy

autor

Alfredo A. Romero

• North Carolina A&T State University

Bibliografia

• [1] Ai, Chunrong and Edward C. Norton (2003). Interaction terms in logit and probit models, Economics Letters, 80, 123-129.
• [2] Arnold, Barry C.; Enrique Castillo; Jose M. Sarabia (1999). Conditional Specification of Statistical Models, Lecture Notes in Statistics, Vol. 73. Springer Series in Statistics.
• [3] Bergtold, Jason S.; Aris Spanos; Ebere Onukwugha (2010). Bernoulli Regression Models: Revisiting the Specification of Statistical Models with Binary Dependent Variables. Journal of Choice Modeling, 3(2), 1-28.
• [4] Boskin, Michael J. (1974). A Conditional Logit Model of Occupational Choice, The Journal of Political Economy, 82(2-1), pp. 389-398.
• [5] Chen, Sean X. and Jun S. Liu (1997). Statistical Applications of the Poisson-Binomial and Conditional Bernoulli Distributions, Statistica Sinica, 7, 875-892.
• [6] Faridi, Muhammad Zahir; Shahnawaz Malik; A.B. Basit (2009). Impact of Education on Female Labour Force Participation in Pakistan: Empirical Evidence from Primary Data Analysis, Pakistan Journal of Social Sciences, 29(1), 127-140.
• [7] Hansen, Jörgen; Roger Wahlberg; Sharif Faisal (2010). Wages and immigrant occupational composition in Sweden, IZA Discussion Papers, No. 4823.
• [8] Hayes, Andrew F. and Jörg Matthes (2009). Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations, Behavior Research Methods, 41, 924-936.
• [9] Hyslop, Dan R. (1999). State Dependence, Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women, Econometrica, 67(6).
• [10] Kay, R. and S. Little (1987). Transformations of the explanatory variables in the logistic regression model for binary data, Biometrika, 74(3), 495-501.
• [11] Molina, Isabel; Ayoub Saei; M. Jose Lombardia (2007). Small area estimates of labour force participation under a multinomial logit mixed model, Journal for the Royal Statistical Society, 170(4), 975-1000.
• [12] Mood, Carina (2010). Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It, European Sociological Review, 26(1), 67-82.
• [13] Mroz, Thomas A. (1987). The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions, Econometrica, 55(4), 765-99.
• [14] Nakosteen, Robert and Michael Zimmer (1980). Migration and Income: The Question of Self-Selection, Southern Economic Journal, 46, 840-851.
• [15] Norton Edward C.; Hua Wang; Chunrong Ai (2004). Computing interaction effects and standard errors in logit and Probit models, The Stata Journal, 4(2), 154-767.
• [16] Romero, Alfredo A. (2011). Tetrachoric Correlation and Data Anomalies, Working Paper, North Carolina A&T State University.
• [17] Segal, Jeffrey A.; Chad Westerland; Stefanie A. Lindquist (2010). Congress, the Supreme Court, and Judicial Review: Testing a Constitutional Separation of Powers Model, American Journal of Political Science, 55(1), 89-104.
• [18] Spanos, Aris (1999). Probability Theory and Statistical Inference: Econometric Modeling with Observational Data. Cambridge, UK: Cambridge University Press.
• [19] Spector, Lee C. and Michael Mazzeo (1980). Probit Analysis and Economic Education, Journal of Economic Education, 11(2), 37-44.
• [20] Theeuwes, J. (1981). Family labour force participation: Multinomial logit estimates, Applied Economics, 13(4).
• [21] Theilmann, John and Allen Wilhite (1987). A Southern Strategy for Labor, A Pac Connection? Southeastern Political Review, 15(1), 69-87.
• [22] Tunali, Insan (1986). A General Structure for Models of Double-Selection and an Application to a Joint Migration/Earnings Process with Re-Migration, [in:] Ronald G. Ehrenberg (ed.), Research in Labor Economics, 8(B), JAI Press, 235-84.

Identyfikatory

DOI

DOI: 10.24425/cejeme.2014.119230