Inference on trending panel data
Peter M. Robinson,Carlos Velasco +1 more
3
TL;DR: In this paper, a model for semi-parametric panel data modelling and statistical inference with fractional stochastic trends, nonparametrically time-trending individual effects, and general cross-sectional correlation and heteroscedasticity in innovations is developed.
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About: This article is published in Journal of Econometrics. The article was published on 01 Oct 2018. and is currently open access. The article focuses on the topics: Statistical inference & Covariance matrix.
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Figures
Table 1. Empirical bias ×100. I(δ0) ρ = 0.5 ρ = 0.9 
Table 7. Empirical Size (%) Corrected 5% t-test. I(δ0), ρ = 0.9, linear trend βi (t/T ) , βi ∼ IIN (0, γ2). γ = 1 γ = 3 
Table 9. Empirical bias δ̂ ×100. FARIMA(ξ0, δ0), ρ = 0.9. ξ0 = 0.5 ξ0 = 0.8 
Table 11. Empirical bias ξ̂ ×100. ARFI(ξ0, δ0), ρ = 0.9. ξ0 = 0.5 ξ0 = 0.8 ξ̂ D T ξ̂δ P T ξ̂ D T ξ̂ P T δ0 : 0.3 0.6 0.9 1.2 0.3 0.6 0.9 1.2 0.3 0.6 0.9 1.2 0.3 0.6 0.9 1.2 T NT 10 100 -8.90 -2.76 -1.46 0.31 -7.30 -1.74 -1.12 0.61 -9.91 -8.37 -7.60 -5.04 -8.26 -5.91 -5.73 -3.96 12 96 -6.00 -1.27 -0.22 1.36 -7.04 -0.71 -0.08 1.53 -9.97 -8.56 -7.80 -5.49 -8.67 -6.36 -6.27 -4.57 
Table 3. Empirical Size (%) Corrected 5% t-test. I(δ0) ρ = 0.5 ρ = 0.9 
Table 13. Empirical Size (%) Corrected 5% Wald-test. FARIMA(ξ0, δ0), ρ = 0.9. ξ0 = 0.5 ξ0 = 0.8
Citations
Exploring the impact of industrialization and electricity use on carbon emissions: The role of green FinTech in Asian countries using an asymmetric panel quantile ARDL approach
Shayan Khan Kakar,Javid Ali,Li Wang,Xihao Wu,Noman Arshed,Tran Thi Le Hien,Ravi Shankar Yadav +6 more
TL;DR: This study examines the impact of industrialization, financial development, electricity consumption, trade openness, and green FinTech on carbon emissions in Asian countries, using an asymmetric panel quantile ARDL approach to account for heterogeneity and non-linear relationships.
3
Estimation for dynamic panel data with individual effects
Peter M. Robinson,Carlos Velasco +1 more
TL;DR: In this paper, a large-cross-section inference on Gaussian pseudo maximum likelihood estimates with temporal dimension kept fixed is discussed, partially complementing and extending recent work of the authors.
References
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The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators
Javier Alvarez,Manuel Arellano +1 more
TL;DR: In this article, the authors derived the asymptotic properties of within groups (WG), GMM, and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity.
Spatial patterns in household demand
TL;DR: In this paper, the authors discuss economic processes that may give rise to spatial patterns in data and explore the relative merits of alternative modeling approaches when data are spatially correlated, focusing on cases in which such a framework may be preferred to the more general fixed effects framework that nests it.
Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects When Both n and T are Large
TL;DR: In this article, the authors considered a dynamic panel AR(1) model with fixed effects when both n and T are large and showed that a relatively simple fix to OLS or the MLE results in an asymptotically unbiased estimator.