Dear all,
I am running fixed effect panel data analysis (cross-section: none; period; fixed). My goal is to find out drivers of firms' decision on leverage.
My data has 9 years timespan with yearly frequency and consists of 75 companies, represented by 23 industries. My dependent variables are: long-term debt, short-term debt. Independent variables: profitability(x1), size(x2), tangibility(x3), liquidity(x4), non-debt tax shield(x5) median industry leverage(x6) and some other dummy variables.
I am confused with appropriate choice of:
1. GLS weights
2. Coefficient covariance method (White cross-section, White period, White (diagonal), Cross-section SUR, Cross-section weights, Period SUR, Period weights.
Also, what appropriate tests would you suggest to run, to be sure my regression is valid (after introduction of dummy variables Eviews no longer let me conduct heteroscedasticity test).
Thank you for your time.
As in my posts to your earlier question it is much better to model heteroscedasticity rather to test for it. But here is both sides!
- He (with colleagues) has challenged the 'gold standard' that fixed effects should be the standard approach to the analysis of Panel data and that a Hausman test is an appropriate way of choosing between a Fixed effects model and a Random effects model. Somewhat controversially they argue that a particular form of the random effects model (the within-between model or the similar Mundlak model) offers all that fixed effects can provide and more.[16][17][18] They also challenge the Fixed Effects Vector Decomposition (FEVD) model of Plumper and Troeger.[19] One reaction was: "This paper and the instructive controversial over FEVD have shown me that my econometrics training had not - as I once assumed - taught me all that there is to know about fixed effects estimation. In particular, the authors' treatment of 'heterogeneity bias' clarifies the importance of addressing both 'within' and 'between' variation in the data and they make a compelling case for considering both 'individual' and 'ecological' influences".[20] Another was: "Bizarre and often incorrect paper by two political scientists on the virtues of random-effects over fixed-effects".[21] to "You can and should use a well-specified random effects model. Always.".[22]
extract from https://en.wikipedia.org/wiki/Kelvyn_Jones .
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