![]() There are, though, relatively recent textbooks treating the Hausman test as a tool to decide between fixed and random effects, e.g.: Wooldridge, J. xtreg RatioofpublicdebttoGDPin RealGDPgrowthin Longtermrealinterestratein, re The role of the Hausman test and whether higher level effects should be treated as random or fixed. xtreg RatioofpublicdebttoGDPin RealGDPgrowthin Longtermrealinterestratein, feįixed-effects (within) regression Number of obs = 350Ĭorr(u_i, Xb) = -0.0246 Prob > F = 0.0469 50298312 (fraction of variance due to u_i) This is a user-written program, to install it type: ssc install xttest3/. Group variable: countrynum Number of groups = 10Ĭorr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0457 The null hypothesis of the Hausman test is that the error term of the random intercept. Random-effects GLS regression Number of obs = 350 method between the fixed effects model and the random effects model. xtreg RatioofpublicdebttoGDPin RealGDPgrowthin Longtermrealinterestratein xtsum RatioofpublicdebttoGDPin RealGDPgrowthin Longtermrealinterestratein Panel variable: countrynum (strongly balanced) list Country countrynum in 1/35, sepby (Country) Is there anything else to mind in the below mentioned output? Thank you!! Is that logical? A fixed effects model should actually make more sense. The Hausman test shows that I should definitely go for random effects. for each variable to equalize the unit value with assistance program Stata 14.0. In this context I set up a panel data set for 10 OECD countries with the corresponding data and programmed it in Stata as follows. This study adopts panel regression analysis and uses the Random Effect. In a scientific paper I would like to examine the effects of public debt on real economic growth and long-term real interest rates.
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