Cross-section Regression with Common Shocks

By Donald W. K. Andrews. 2003

398 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9cf06f672948d366d14abe870d8735ba29d2db30

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Liangjun Su
2. M. Pesaran
3. Jushan Bai
4. Zhenlin Yang
5. Serena Ng
6. Badi H. Baltagi
7. C. Kao
8. Muhammad Shahbaz

In Statistics and Probability

1. Liangjun Su
2. Serena Ng
3. M. Pesaran
4. Jushan Bai
5. Jiti Gao
6. Jungyoon Lee
7. C. Kao
8. Patrick Gagliardini

Citations in the corpus

• THE MOVING BLOCKS BOOTSTRAP FOR PANEL LINEAR REGRESSION MODELS WITH INDIVIDUAL FIXED EFFECTS (2011, background): “See Andrews (2005) for more discussion of common shocks and their effects on the properties of OLS estimators in the context of cross-section regression models.”
• A diagnostic criterion for approximate factor structure (2016, background): “1 is satisfied for instance when there are unobserved industry-specific factors independent among industries and over time, as in Ang et al. (2010). In empirical applications, blocks can match industrial sectors.”
• Series estimation under cross-sectional dependence (2013, background): “One class of models postulates unobserved common factors that affect some or all individual units, see e.g Andrews (2005), Pesaran (2006) and Bai (2009), and entail persistent cross-sectional dependence.”
• TESTING HOMOGENEITY IN PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS (2013, background): “Here the cross-sectional dependence is similar to the type of cross-sectional dependence generated by common shocks studied by Andrews (2005).”
• Greenhouse gas emissions, energy consumption and economic growth: A panel cointegration analysis from Canadian industrial sector perspective (2012, background): “As has been documented in the literature, failure to adequately account for the presence of crosssection dependence in panel data study could lead to serious bias problem, see for instance O'Connell (1998), Andrews (2005), Pesaran (2006) and Bai and Ng (2010).”
• Asymptotic theory for nonparametric regression with spatial data (2011, background): “Andrews (2005) has developed the theory to allow for arbitrarily strong forms of dependence in the disturbances, but with the data then generated by random sampling, an assumption that is not necessarily plausible in practice.”
• Large Panels with Common Factors and Spatial Correlation (2011, methodology): “…instance, a rise in the interest rate may a¤ect household consumption and rm investment decisions; oil price shocks may in uence rm production costs; real shocks, such as a decline in the aggregate demand and employment could simultaneously slow growth in a number of countries (see Andrews (2005)).”
• Panel Data Models With Interactive Fixed Effects (2009, background): “The normalization(4) F ′F/T = Ir (7) yields r(r + 1)/2 restrictions.”
• Time-Varying Risk Premium in Large Cross-Sectional Equity Data Sets (2016, methodology): “We combine the constructions of Hansen and Richard (1987) and Andrews (2005) to define a multi-period economy with a continuum of assets having strictly stationary and ergodic return processes.”