IV estimation of panels with factor residuals

Robertson, D. H.; Sarafidis, Vasileios

doi:10.1016/j.jeconom.2014.12.001

Cited by 62 publications

(67 citation statements)

References 28 publications

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“…This estimator coincides asymptotically with the FIVU estimator of Robertson and Sarafidis (2013), as well as with the QLD estimator of Ahn et al (2013) if all T (T + 1)(K + 1)/2 moment conditions are used. A proof for the equivalence between FIVU and QLD is given in Robertson and Sarafidis (2013).…”

Section: Gmm With Projection Hayakawa (2012)supporting

confidence: 72%

“…Robertson and Sarafidis (2013) show that FIVR is asymptotically more efficient than FIVU and procedures that involve some form of differencing. Furthermore, the restrictions imposed on a subset of the nuisance parameters provide substantial efficiency gains in finite samples.…”

Section: Restricted Estimator Fivrmentioning

confidence: 96%

“…Rather than removing the incidental parameters λ i , Robertson and Sarafidis (2013) propose a GMM estimator that makes use of centered moment conditions of the following form:…”

Section: Unrestricted Estimator Fivumentioning

confidence: 99%

“…Notwithstanding, as shown in Robertson and Sarafidis (2013) if one is only interested in the structural parameters, α and β k , it is not essential to impose any identifying normalizations on G and F ; the resulting unrestricted estimator for structural parameters is consistent and asymptotically normal, while the variance-covariance matrix can be consistently estimated using the corresponding sub-block of the generalized inverse of the unrestricted variance-covariance matrix.…”

Section: Unrestricted Estimator Fivumentioning

confidence: 99%

“…Similarly to the FIVU estimator of Robertson and Sarafidis (2013) the number of identifiable parameters is smaller than the nominal one and depends on the projected variables z i .…”

Section: Gmm With Projection Hayakawa (2012)mentioning

confidence: 99%

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Fixed T dynamic panel data estimators with multifactor errors

Juodis¹,

Sarafidis

2016

Econometric Reviews

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This paper analyzes a growing group of fixed T dynamic panel data estimators with a multi-factor error structure. We use a unified notational approach to describe these estimators and discuss their properties in terms of deviations from an underlying set of basic assumptions. Furthermore, we consider the extendability of these estimators to practical situations that may frequently arise, such as their ability to accommodate unbalanced panels. Using a large-scale simulation exercise, we consider scenarios that remain largely unexplored in the literature, albeit they are of great empirical relevance. In particular, we examine (i) the effect of the presence of weakly exogenous covariates, (ii) the effect of changing the magnitude of the correlation between the factor loadings of the dependent variable and those of the covariates, (iii) the impact of the number of moment conditions on bias and size for GMM estimators, and finally the effect of sample size. Thus, our study may serve as a useful guide to practitioners who wish to allow for multiplicative sources of unobserved heterogeneity in their model.

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Section: Gmm With Projection Hayakawa (2012)supporting

confidence: 72%

Section: Restricted Estimator Fivrmentioning

confidence: 96%

“…Rather than removing the incidental parameters λ i , Robertson and Sarafidis (2013) propose a GMM estimator that makes use of centered moment conditions of the following form:…”

Section: Unrestricted Estimator Fivumentioning

confidence: 99%

Section: Unrestricted Estimator Fivumentioning

confidence: 99%

“…Similarly to the FIVU estimator of Robertson and Sarafidis (2013) the number of identifiable parameters is smaller than the nominal one and depends on the projected variables z i .…”

Section: Gmm With Projection Hayakawa (2012)mentioning

confidence: 99%

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Fixed T dynamic panel data estimators with multifactor errors

Juodis¹,

Sarafidis

2016

Econometric Reviews

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Does export upgrading really matter to economic growth? Evidence from panel data for high‐, middle‐ and low‐income countries

2020

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The main objective of this study is to examine the long‐run relationship between export upgrading and economic growth for 67 countries over the period of 1984–2013. For this purpose, a panel cointegration framework that allows to control for parameters heterogeneity, cross‐sectional dependence and non‐stationarity has been deployed. Empirical results yield evidence of a positive and significant effect of export upgrading on economic growth for the full‐sample and high‐income panels, while this effect is negative and significant for low‐income countries and insignificant for middle‐income countries. Particularly, our findings show evidence of an inverted U‐shaped relationship for the global and high‐income panels. However, for low‐income countries relationship between export complexity and economic growth was found to be U‐shaped. These results are robust to several robustness checks and have important policy implications. In developed countries, excessive export complexity may be job‐destructive, and thereby threatens long‐run growth and prosperity. For non‐developed countries, exports' diversification should be prioritized during the first stages of development. Industrial upgrading should not be considered as a strategic economic policy before the economy reaches a minimum level of maturity.

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CCE in fixed‐T panels

Westerlund

Petrova

Norkutė

2019

J of Applied Econometrics

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Summary The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor‐augmented panel regression models. The workhorse of this literature is based on first estimating the unknown factors using the cross‐section averages of the observables, and then applying ordinary least squares conditional on the first‐step factor estimates. This is the common correlated effects (CCE) approach, the existing asymptotic theory for which is based on the requirement that both the number of time series observations, T, and the number of cross‐section units, N, tend to infinity. The obvious implication of this theory for empirical work is that both N and T should be large, which means that CCE is impossible for the typical micro panel where only N is large. In the current paper, we put the existing CCE theory and its implications to a test. This is done by developing a new theory that enables T to be fixed. The results show that many of the previously derived large‐T results hold even if T is fixed. In particular, the pooled CCE estimator is still consistent and asymptotically normal, which means that CCE is more applicable than previously thought. In fact, not only do we allow T to be fixed, but the conditions placed on the time series properties of the factors and idiosyncratic errors are also much more general than those considered previously.

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IV estimation of panels with factor residuals

Cited by 62 publications

References 28 publications

Fixed T dynamic panel data estimators with multifactor errors

Fixed T dynamic panel data estimators with multifactor errors

Does export upgrading really matter to economic growth? Evidence from panel data for high‐, middle‐ and low‐income countries

CCE in fixed‐T panels

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