Cross-sectional aggregation of non-linear models

Garderen, Kees Jan van; Lee, Kevin; Pesaran, M. Hashem

doi:10.1016/s0304-4076(99)00040-8

Cited by 57 publications

(26 citation statements)

References 29 publications

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“…In fact, even under very stringent conditions, recovering the farm-level parameters using a county-level regression would require the inclusion of squares and cross-products of the explanatory variables and very complex functional forms (Van Garderen et al [2000] provide a few examples). Given that most Ricardian analyses have been implemented on aggregated data, it is important to investigate the size of any bias inherent in such approaches and its implication for the prediction of climate change impacts.…”

Section: Testing For Aggregation Bias and Omitted Nonlinearitiesmentioning

confidence: 99%

“…While in linear models this issue can be resolved by using appropriate weights, in nonlinear specifications the aggregation process typically produces biased coefficients and predictions (Lewbel 1991;Garderen, Lee, and Pesaran 2000;Imbs et al 2005). As Ricardian analyses typically use aggregated information and report strong nonlinear climatic effects, it is of interest to test whether these results are robust to aggregation bias.…”

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confidence: 99%

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The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values

Fezzi¹,

Bateman²

2015

Journal of the Association of Environmental and Resource Econom

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Section: Testing For Aggregation Bias and Omitted Nonlinearitiesmentioning

confidence: 99%

mentioning

confidence: 99%

The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values

Fezzi¹,

Bateman²

2015

Journal of the Association of Environmental and Resource Econom

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“…Contributions to the theoretical literature on aggregation versus disaggregation in forecasting can be found in e.g. Grunfeld & Griliches (1960), Kohn (1982), Lütkepohl (1984Lütkepohl ( , 1987, Granger (1987), Pesaran, Pierse & Kumar (1989), Garderen, Lee & Pesaran (2000), Giacomini & Granger (2004); see Lütkepohl (2006) for a recent review on aggregation and forecasting. Since in practice the DGP is not known, it is largely an empirical question whether aggregating forecasts of disaggregates improves forecast accuracy of the aggregate of interest.…”

Section: Introductionmentioning

confidence: 99%

Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate

Hendry

Hubrich

2011

Journal of Business & Economic Statistics

136

109

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in WO R K I N G PA PE R S E R I E S N O 1155 / F E B R UA RY 2010This paper can be downloaded without charge from http://www.ecb.europa.eu or from the Social Science Research Network electronic library at http://ssrn.com/abstract_id=1551223In 2010 all ECB publications feature a motif taken from the €500 banknote. Abstract 4 Non-technical summary 5 COMBINING DISAGGREGATE FORECASTS OR COMBINING

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“…However, it has to be kept in mind that a macro interpretation of micro hysteresis and of uncertainty impacts on &investment' decisions cannot be performed in a straightforward way because heterogeneous "rms are characterized by di!erent &investment' thresholds [8]. Thus, any assessment of the persistent e!ects of temporary exchange rate shocks on total economy employment should be founded on an adequate aggregation approach for heterogeneous ,rms.…”

Section: Introductionmentioning

confidence: 99%

Exchange rate uncertainty and employment: an algorithm describing ‘play’

Belke

Göcke²

2001

Appl Stoch Models Bus & Ind

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A This behaviour can be described technically as a non-ideal relay. See Reference [3, p. 263 SUMMARYThe paper deals with the impacts of exchange rate uncertainty on the relationship between macroeconomic labour market variables. Under uncertainty, areas of weak reactions*so-called &play' areas*have to be considered at the macrolevel. The width of the play area is a positive function of the degree of uncertainty. When changes go beyond the play-area suddenly strong reactions (&spurts') occur. These non-linear dynamics are captured in a simpli"ed linearized way. An algorithm describing linear play hysteresis is developed and implemented into a regression framework. As an empirical application, the exchange rate impacts on German employment are analysed considering play e!ects.

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Cross-sectional aggregation of non-linear models

Cited by 57 publications

References 29 publications

The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values

The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values

Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate

Exchange rate uncertainty and employment: an algorithm describing ‘play’

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