Least Absolute Deviation Estimation of Linear Econometric Models: A Literature Review

Dasgupta, Madhuchhanda; Mishra, SK

doi:10.2139/ssrn.552502

Cited by 28 publications

(20 citation statements)

References 42 publications

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“…-Fit all lidar signal vertical points (noted as a vector y) with a weighted least-absolute-deviations (LAD) regression (DasGupta and Mishra, 2007); the LAD regression is employed here because we are interested in the linear regression of lidar signal points at higher altitudes, e.g. the points between 2 and 3 km above the ground.…”

Section: Y Wang Et Al: Comparison Of Lidar Observations With Aerosomentioning

confidence: 99%

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Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign

et al. 2014

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Abstract. In this study, we investigate the ability of the chemistry transport model (CTM) POLAIR3D of the air quality modelling platform POLYPHEMUS to simulate lidar backscattered profiles from model aerosol concentration outputs. This investigation is an important preprocessing stage of data assimilation (validation of the observation operator). To do so, simulated lidar signals are compared to hourly lidar observations performed during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer experiment in July 2009, when a ground-based mobile lidar was deployed around Paris onboard a van. The comparison is performed for six different measurement days, 1, 4, 16, 21, 26 and 29 July 2009, corresponding to different levels of pollution and different atmospheric conditions. Overall, POLYPHEMUS well reproduces the vertical distribution of lidar signals and their temporal variability, especially for 1, 16, 26 and 29 July 2009. Discrepancies on 4 and 21 July 2009 are due to high-altitude aerosol layers, which are not well modelled. In the second part of this study, two new algorithms for assimilating lidar observations based on the optimal interpolation method are presented. One algorithm analyses PM 10 (particulate matter with diameter less than 10 µm) concentrations. Another analyses PM 2.5 (particulate matter with diameter less than 2.5 µm) and PM 2.5−10 (particulate matter with a diameter higher than 2.5 µm and lower than 10 µm) concentrations separately. The aerosol simulations without and with lidar data assimilation (DA) are evaluated using the Airparif (a regional operational network in charge of air quality survey around the Paris area) database to demonstrate the feasibility and usefulness of assimilating lidar profiles for aerosol forecasts. The evaluation shows that lidar DA is more efficient at correcting PM 10 than PM 2.5 , probably because PM 2.5 is better modelled than PM 10 . Furthermore, the algorithm which analyses both PM 2.5 and PM 2.5−10 provides the best scores for PM 10 . The averaged root-mean-square error (RMSE) of PM 10 is 11.63 µg m −3 with DA (PM 2.5 and PM 2.5−10 ), compared to 13.69 µg m −3 with DA (PM 10 ) and 17.74 µg m −3 without DA on 1 July 2009. The averaged RMSE of PM 10 is 4.73 µg m −3 with DA (PM 2.5 and PM 2.5−10 ), against 6.08 µg m −3 with DA (PM 10 ) and 6.67 µg m −3 without DA on 26 July 2009.

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Section: Y Wang Et Al: Comparison Of Lidar Observations With Aerosomentioning

confidence: 99%

“…points at lower altitudes). In such cases, the least-squares method fails and the LAD method performs well (DasGupta and Mishra, 2007). In detail, we minimise…”

Section: Y Wang Et Al: Comparison Of Lidar Observations With Aerosomentioning

confidence: 99%

Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign

et al. 2014

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“…In certain cases where data contain outliers or input figures contain large sporadic errors, etc the least squares (LS) approach to estimation falters but the least absolute deviation (LAD) method yields good estimates of parameters (Taylor, 1974). In any case, LAD often gives estimated parameters that are comparable to or better than those given by the LS (Dasgupta and Mishra, 2004).…”

Section: Methods Of Analysismentioning

confidence: 95%

Globalization and Structural Changes in the Indian Industrial Sector: An Analysis of Production Functions

Mishra

2006

SSRN Journal

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“…In all other cases, tail events affect less the estimators compared to OLS, in which the residuals are squared. Hence, LAD provides more reliable results than OLS in particular for fat-tailed error distributions (Dasgupta and Mishra 2004;Maasoumi et al 2007).…”

Section: Further Empirical Aspects Concerning the Implementationmentioning

confidence: 99%

Causal dynamic effects in regional systems of technological activities: a SVAR approach

Brenner

Duschl

2015

Ann Reg Sci

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This paper analyzes the causal relationships in regional technological systems within a structural vector autoregression framework. Applying a data-driven independent component analysis, it shows how the regional dynamics of economic, research, innovation and educational activities affect each other instantaneously and over time. By matching differently classified data on employees, patents and graduates, the analysis is based on a unique database which embraces multi-dimensional aspects for five clearly separated industries. The findings on how industry specific growth processes unfold are explained by referring to the type of industry and its knowledge base. For instance, it is found that more engineering-oriented industries like machine tools show a success-driven pattern, whereas science-based industries show a stronger dependence on universities. Such knowledge on the causal relations is of utmost relevance to the design and implementation of policy instruments. JEL Classification

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Least Absolute Deviation Estimation of Linear Econometric Models: A Literature Review

Cited by 28 publications

References 42 publications

Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign

Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign

Globalization and Structural Changes in the Indian Industrial Sector: An Analysis of Production Functions

Causal dynamic effects in regional systems of technological activities: a SVAR approach

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