2019
DOI: 10.1111/geer.12202
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Capital adjustment cost and inconsistency in income-based dynamic panel models with fixed effects

Abstract: After the seminal work of Nickell (1981), a vast literature demonstrates the inconsistency of ‘conditional convergence’ estimator in income‐based dynamic panel models with fixed effects when the time horizon (T) is short but the sample of countries (N) is large. Less attention is given to the economic root of inconsistency of the fixed effects estimator when T is also large. Using a variant of the Ramsey growth model with long‐run adjustment cost of capital, we demonstrate that the fixed effects estimator of s… Show more

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Cited by 5 publications
(3 citation statements)
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“…Next we test its validity, comparing the variances of parameters obtained from the random-effect model using the Hausman test. According to Baltagi (2008) and Basu et al (2019), all estimators in the FE model even with a small number of cross-sections N are consistent as time (t) increases and approaches to infinity. In the random-effects model, with the regression error term v i,t = u i + ε i,t , where u i is the time-invariant random individual effect in addition to ε i,t error term denoting all other missing elements.…”
Section: Methodsmentioning
confidence: 96%
“…Next we test its validity, comparing the variances of parameters obtained from the random-effect model using the Hausman test. According to Baltagi (2008) and Basu et al (2019), all estimators in the FE model even with a small number of cross-sections N are consistent as time (t) increases and approaches to infinity. In the random-effects model, with the regression error term v i,t = u i + ε i,t , where u i is the time-invariant random individual effect in addition to ε i,t error term denoting all other missing elements.…”
Section: Methodsmentioning
confidence: 96%
“…Next, we test its validity comparing the variances of parameters obtained from the random-effect model using the Hausman test. According to Baltagi (2008) and Basu et al (2019) , all estimators in the fixed effect model even with a small number of cross-sections N, are consistent as time (t) increases and approaches to infinity. In the random effect model, with the regression error term v i,t = u i + ε i,t, where u i is the time-invariant random individual effect in addition to ε i,t iid error term denoting all other missing elements.…”
Section: Model Methodology and Datamentioning
confidence: 96%
“…Next we test its validity, comparing the variances of parameters obtained from the random effect model using the Hausman test. According to Baltagi (2008) and Basu et al (2019), all estimators in the fixed effect model even with small number of cross sections N, are consistent as time (t) increases and approaches to infinity. In the random effect model, with the regression error term vi,t = ui + εi,t, where ui is the time-invariant random individual effect in addition to εi,t error term denoting all other missing elements.…”
Section: Methodsmentioning
confidence: 93%