Econometric Analysis Of Panel Data

By M. Weisz. 2016

4,265 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2cbbbb0d65b08befac8ef6dfa91c19e8892f4bea

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Citations in the corpus

Risk in Islamic banking and corporate governance (2018, methodology): “Furthermore, the random-effects model is more appropriate given the presence of several dummy variables in our empirical models, as the fixed effects model wipes out the effects of individual dummy variables (Wooldridge, 2010; Baltagi, 2008).”
Determinants of bank profitability in transition countries: What matters most? (2016, background): “Least squares estimates produce biased and inconsistent estimates for the models with dynamic nature (Baltagi, 2008).”
Audit Committee, Corporate Governance and Firm Performance: Empirical Evidence from India (2016, methodology): “Firms that have more independent members in their audit committees have a lesser probability of becoming victims of fraud (Beasley, 1996). Bukit and Iskandar (2009) suggested that earnings management was toned-down by independent audit committees. Abbott (2002) also found an inverse relationship bet”
In Search of Robust Methods for Dynamic Panel Data Models in Empirical Corporate Finance (2015, unknown): “To begin with, due to the correlation between the fixed effects and the lagged dependent variable, the pooled OLS (hereafter POLS) estimator is biased and inconsistent.”
Measuring the effects of extreme weather events on yields (2016, background): “It is common practice to include time effects in panel regressions to account for changes through time not otherwise identified (Baltagi, 2008).”
The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: Evidence from panel quantile regression (2016, background): “As noted by Baltagi (2008), time-period fixed effects control for all time specific, spatial-invariant variables whose omission could bias the estimates in a typical time series study.”
EC3SLS Estimator for a Simultaneous System of Spatial Autoregressive Equations with Random Effects (2015, unknown): “The structural disturbances for each equation j follow an error component specification which is defined as follows, see Baltagi (2008): uj = Zµµj + vj for j = 1, 2, where µj is an N × 1 vector of random individual effects which are assumed to be i.i.d.(0, σ2µjjIN ), vj is an NT × 1 vector of…”
Firm capabilities and growth: the moderating role of market conditions (2016, unknown): “Seeking to address these concerns, we propose the following model specifications (Baltagi 2001):”
Testing for Serial Correlation in Fixed-Effects Panel Data Models (2016, background): “For example, Baltagi (2008) argues that unobserved shocks to economic relationships like investment or consumption will often have an effect for more than one period.”
Panel evidence on the impact of tourism growth on poverty, poverty gap and income inequality (2019, methodology): “The second criterion for the chosen set of countries is that for valid estimation and interpretation of panel results, there needs to be enough time-series observations of the countries in the panel (Baltagi 2008).”
Relationship between openness to trade and deforestation: : Empirical evidence from the Brazilian Amazon (2016, background): “It is well known that one of the main advantages of using panel data is to control for observed and also for unobserved characteristics (Baltagi, 1995).”
New insights into an old issue – examining the influence of tourism on economic growth: evidence from selected small island developing states (2019, background): “Contemporary studies have suggested that the panel-based unit root tests have higher power compared to individual time series unit root tests (see Baltagi, 2008).”
Sensitive industries produce better ESG performance: Evidence from emerging markets (2017, unknown): “In other words, they are endogenous (Baltagi, 2001).”
Capital Structure and Firm Performance: Empirical Evidence from India (2015, background): “Panel data provide more information, more variability and less collinearly among the variables (Baltagi, 2005).”
Investigating the environmental Kuznets curve hypothesis in seven regions: The role of renewable energy (2016, methodology): “The panel data is also more capable in controlling the heterogeneity and the serial correlation problem compared to the normal time series and cross section data (Baltagi, 2005).”
Do FinTech trigger renewable energy use? Evidence from OECD countries (2021, methodology): “It also provides different advantages as it ensures higher variability, less collin-earity between the various factors, as well as granting higher degrees of freedom and better ef ﬁ ciency [60].”