MULTISTEP PREDICTION OF PANEL VECTOR AUTOREGRESSIVE PROCESSES

DOI10.1017/S0266466612000679zbMath1283.62182MaRDI QIDQ5403108

Ryan Greenaway-McGrevy

Publication date: 25 March 2014

Published in: Econometric Theory (Search for Journal in Brave)

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Monte Carlo methods (65C05)

Related Items (6)

On model selection from a finite family of possibly misspecified time series models ⋮ Evaluating panel data forecasts under independent realization ⋮ ASYMPTOTICALLY EFFICIENT MODEL SELECTION FOR PANEL DATA FORECASTING ⋮ Inverse moment bounds for sample autocovariance matrices based on detrended time series and their applications ⋮ Multistep forecast selection for panel data ⋮ Panel data nowcasting

Cites Work

• VAR forecasting under misspecification
• Bias in dynamic panel estimation with fixed effects, incidental trends and cross section dependence
• Asymptotic distribution of factor augmented estimators for panel regression
• Asymptotically efficient selection of the order of the model for estimating parameters of a linear process
• Prediction in the context of the variance-components model
• Asymptotically efficient autoregressive model selection for multistep prediction
• Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline
• Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: some empirical evidence from US electricity and natural-gas consumption.
• AIC, overfitting principles, and the boundedness of moments of inverse matrices for vector autotregressions and related models.
• On bias, inconsistency, and efficiency of various estimators in dynamic panel data models
• Order selection for same-realization predictions in autoregressive processes
• BIAS REDUCTION FOR DYNAMIC NONLINEAR PANEL MODELS WITH FIXED EFFECTS
• Inference in Linear Time Series Models with some Unit Roots
• Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
• Biases in Dynamic Models with Fixed Effects
• Panel Data Models With Interactive Fixed Effects
• Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both n and T Are Large

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