Russia's accession to the WTO: the potential for trade increase

Babetskaia-Kukharchuk, Oxana; Maurel, Mathilde

doi:10.1016/j.jce.2004.08.005

Cited by 45 publications

(16 citation statements)

References 39 publications

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“…The coefficients do not differ much but our standard errors are 40 See Egger and Pfaffermayr (2004a) and the references therein. 41 The Hausman and Taylor (1981) estimator has been used before for static panel gravity equations, among others by Babetskaia- Kukharchuk and Maurel (2004), Brun, Carrère, Guillaumont, and de Melo (2005), Egger (2005), Carrère (2006), and Rault, Sova, and Sova (2009) for trade, and by Leibrecht and Scharler (2009) and Daniels and von der Ruhr (2014) for FDI. Serlenga and Shin (2007) extended the estimator to accommodate for common correlated effects in a large-T framework.…”

Section: Introductionmentioning

confidence: 99%

Estimation of linear dynamic panel data models with time‐invariant regressors

Kripfganz

Schwarz

2019

J of Applied Econometrics

102

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We present a sequential approach to estimating a dynamic Hausman-Taylor model. We first estimate the coefficients of the time-varying regressors and subsequently regress the first-stage residuals on the time-invariant regressors. In comparison to estimating all coefficients simultaneously, this two-stage procedure is more robust against model misspecification, allows for a flexible choice of the first-stage estimator, and enables simple testing of the overidentifying restrictions. For correct inference, we derive analytical standard error adjustments. We evaluate the finite-sample properties with Monte Carlo simulations and apply the approach to a dynamic gravity equation for US outward foreign direct investment. 1 Schooling itself is a time-invariant regressor in his data set. Yet it is hard to argue that its coefficient is identified because Andini uses only the first differences of time-varying regressors as instruments. These instruments are generally assumed to be uncorrelated with any time-invariant variable. 526 2 Plümper and Troeger (2007) proposed a three-stage approach for the static model that they label "fixed effects vector decomposition." In a symposium on this method, Breusch, Ward, Nguyen, and Kompas (2011) and Greene (2011) show that the first two stages can be characterized by an instrumental variables estimation with a particular choice of instruments, and that the third stage is essentially meaningless. 3 Hoeffler (2002) argues similarly. 4 As Binder, Hsiao, and Pesaran (2005) and Bun and Windmeijer (2010) emphasize, GMM estimators might suffer from a weak instruments problem when the autoregressive parameter approaches unity or when the variance of the unobserved unit-specific effects is large. Moreover, the number of instruments can rapidly become large relative to the sample size. The consequences of instrument proliferation, summarized by Roodman (2009), range from biased coefficient and standard error estimates to weakened specification tests. 5 Our two-stage procedure fits into the framework of sequential estimators discussed by Newey (1984). While our paper is only concerned with linear panel data models, Honoré and Kesina (2017) recently suggested related two-stage approaches for some nonlinear models. They use a bootstrap procedure to obtain valid standard errors in contrast to our analytical standard error correction.

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Section: Introductionmentioning

confidence: 99%

Estimation of linear dynamic panel data models with time‐invariant regressors

Kripfganz

Schwarz

2019

J of Applied Econometrics

102

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“…The literature so far explains hysteresis in former Soviet Union trade by remoteness and landlockedness 2 (Kaminski, Wang and Winters, 1996;Djankov and Freund, 2002;Grafe, Raiser and Sakatsume, 2005), poor access to markets and incomplete reforms (Havrylishin and Al-Atrash, 1998), weak institutions (Babetskaia- Kukharchuk and Maurel, 2004), poor product quality (e.g. Bevan et al, 2001), and hysteresis in consumption, production and business networks (Djankov and Freund, 2002).…”

Section: Introductionmentioning

confidence: 99%

Landlockedness, Infrastructure And Trade : New Estimates For Central Asian Countries

Grigoriou¹

2007

Policy Research Working Papers

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“…3 3 The link between trade and exchange rate volatility has been already addressed in the gravity framework as a by‐product of studies analyzing trade integration in the CEECs. This literature is rather mixed: a negative relationship is found in Babetskaia‐Kukharchuk and Maurel (2004) and the relationship is positive in Babetskii et al. (2003) and in Bussière et al.…”

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confidence: 98%

Exchange Rate Regimes, Foreign Exchange Volatility, and Export Performance in Central and Eastern Europe: Just another Blur Project?

Égert

Morales‐Zumaquero

2008

Review Development Economics

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The authors attempt to analyze the direct impact of exchange rate volatility on the export performance of ten Central and Eastern European transition economies, as well as its indirect impact via changes in exchange rate regimes. Not only aggregate but also bilateral and sectoral export flows are studied. To this end, the authors first analyze shifts in exchange rate volatility linked to changes in the exchange rate regimes and, second, they use these changes to construct the dummy variables that they include in their export function. The results suggest that the size and the direction of the impact of forex volatility and of regime changes on exports vary considerably across sectors and countries and that they may be related to specific periods. Copyright � 2008 The Authors. Journal compilation � 2008 Blackwell Publishing Ltd.

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Russia's accession to the WTO: the potential for trade increase

Cited by 45 publications

References 39 publications

Estimation of linear dynamic panel data models with time‐invariant regressors

Estimation of linear dynamic panel data models with time‐invariant regressors

Landlockedness, Infrastructure And Trade : New Estimates For Central Asian Countries

Exchange Rate Regimes, Foreign Exchange Volatility, and Export Performance in Central and Eastern Europe: Just another Blur Project?

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