Add to ORKGThe presence of outlying observations in panel data can affect the classical estimates in a dramatic way. Nevertheless the common practice seems to disregard the problem. The aim of this work is to study robust regression techniques in the fixed effects linear panel data framework. Robustness of the procedures is investigated by means of breakdown point computations and simulation experiments. A distinction between outlying blocks and cells in a panel is made. To show the potential of robust panel data methods an empirical example on the response of the private sector behavior to fiscal policy is presented.Breakdown point; Data; Effects; Estimator; Fixed effects model; Framework; Linear regression; Methods; Model; Panel data; Policy; Privat...
In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regressi...
In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regressi...
In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regressi...
The presence of outlying observations in panel data can affect the classical estimates in a dramatic...
The presence of outlying observations in panel data can affect the classical estimates in a dramatic...
The presence of outlying observations in panel data can affect the classical estimates in a dramatic...
The presence of outlying observations in panel data can affect the classical esti-mates in a dramati...
In empirical studies often the values of some variables for some observations are much larger or sma...
The Ordinary Least Squares (OLS) is the commonly used method to estimate the parameters of fixed eff...
The panel-data regression models are frequently applied to micro-level data, which often suffer from...
Panel data is a group of many individual units observed for a specific time period. In general, rese...
The panel-data regression models are frequently applied to micro-level data, which often suffer from...
In case of some influential observations in an econometric analysis, the classical methods, such as ...
The panel-data regression models are frequently applied to micro-level data, which often suffer from...
In different fields of applications including, but not limited to, behavioral, environmental, medica...
In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regressi...
In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regressi...
In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regressi...
The presence of outlying observations in panel data can affect the classical estimates in a dramatic...
The presence of outlying observations in panel data can affect the classical estimates in a dramatic...
The presence of outlying observations in panel data can affect the classical estimates in a dramatic...
The presence of outlying observations in panel data can affect the classical esti-mates in a dramati...
In empirical studies often the values of some variables for some observations are much larger or sma...
The Ordinary Least Squares (OLS) is the commonly used method to estimate the parameters of fixed eff...
The panel-data regression models are frequently applied to micro-level data, which often suffer from...
Panel data is a group of many individual units observed for a specific time period. In general, rese...
The panel-data regression models are frequently applied to micro-level data, which often suffer from...
In case of some influential observations in an econometric analysis, the classical methods, such as ...
The panel-data regression models are frequently applied to micro-level data, which often suffer from...
In different fields of applications including, but not limited to, behavioral, environmental, medica...
In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regressi...
In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regressi...
In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regressi...