Using Split Samples to Improve Inference about Causal Effects / Marcel Fafchamps, Julien Labonne.

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Fafchamps, Marcel

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w21842.Publication details: Cambridge, Mass. National Bureau of Economic Research 2016.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: We discuss a method aimed at reducing the risk that spurious results are published. Researchers send their datasets to an independent third party who randomly generates training and testing samples. Researchers perform their analysis on the former and once the paper is accepted for publication the method is applied to the latter and it is those results that are published. Simulations indicate that, under empirically relevant settings, the proposed method significantly reduces type I error and delivers adequate power. The method - that can be combined with pre-analysis plans - reduces the risk that relevant hypotheses are left untested.

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Item type	Home library	Collection	Call number	Status	Date due	Barcode	Item holds
Working Paper	Biblioteca Digital	Colección NBER	nber w21842 (Browse shelf(Opens below))	Not For Loan

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January 2016.

We discuss a method aimed at reducing the risk that spurious results are published. Researchers send their datasets to an independent third party who randomly generates training and testing samples. Researchers perform their analysis on the former and once the paper is accepted for publication the method is applied to the latter and it is those results that are published. Simulations indicate that, under empirically relevant settings, the proposed method significantly reduces type I error and delivers adequate power. The method - that can be combined with pre-analysis plans - reduces the risk that relevant hypotheses are left untested.