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Books on the topic 'Reusing and reconciling models'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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1

Leith, Brenda. Reconciling models of the American lobster fishery. Peterborough, Ont: Trent University. Dept. of Economics, 1990.

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2

Lettau, Martin. Reconciling the return predictability evidence. Cambridge, Mass: National Bureau of Economic Research, 2006.

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3

Saaty, Thomas L. Group decision making: Drawing out and reconciling differences. Pittsburgh, PA: RWS Publications, 2008.

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4

Waldfogel, Joel. Reconciling asymmetric information and divergent expectations theories of litigation. Cambridge, MA: National Bureau of Economic Research, 1998.

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5

Martin, Antoine. Reconciling Bagehot with the Fed's response to September 11. [New York, N.Y.]: Federal Reserve Bank of New York, 2005.

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6

Roel M. W. J. Beetsma. Reconciling stability and growth: Smart pacts and structural reforms. Washington, D.C: International Monetary Fund, Research Department, 2003.

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7

Croushore, Dean. The optimal inflation tax when income taxes distort: Reconciling MUF and shopping-time models. Philadelphia: Federal Reserve Bank of Philadelphia, Economic Research Division, 1994.

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8

Attanasio, Orazio P. intertemporal consumption choices, transaction costs and limited participation to financial markets: Reconciling data and theory. Cambridge, Mass: National Bureau of Economic Research, 2006.

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9

Rauch, James E. Reconciling the pattern of trade with the pattern of migration. Cambridge, MA: National Bureau of Economic Research, 1991.

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10

Kronenberg, Tobias. Reconciling environmental conservation with economic prosperity: The feasibility of double dividends in the short and long run. Jülich: Forschungszentrum, Zentralbibliothek, 2007.

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11

ESOMAR/EMAC/AFM, Symposium on Information Based Decision Making in Marketing (1993 Paris France). ESOMAR/EMAC/AFM Symposium on Information Based Decision Making in Marketing: Reconciling the needs and interests of decision makers, data collectors and data providers : Paris (France) 17th-19th November 1993. Amsterdam: ESOMAR, 1993.

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12

Protocol for reconciling differences among receptor and dispersion models. Research Triangle Park NC: U.S. Environmental Protection Agency, Office of Air and Radiation, Office of Air Quality Planning and Standards, 1987.

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13

Ghaemi, S. Nassir. Rise and Fall of the Biopsychosocial Model: Reconciling Art and Science in Psychiatry. Johns Hopkins University Press, 2012.

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14

Carvalho, Carlos, and Jill Rickershauser. Characterizing the uncertainty of climate change projections using hierarchical models. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.20.

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This article focuses on the use of Bayesian hierarchical models for integration and comparison of predictions from multiple models and groups, and more specifically for characterizing the uncertainty of climate change projections. It begins with a discussion of the current state and future scenarios concerning climate change and human influences, as well as various models used in climate simulations and the goals and challenges of analysing ensembles of opportunity. It then introduces a suite of statistical models that incorporate output from an ensemble of climate models, referred to as general circulation models (GCMs), with the aim of reconciling different future projections of climate change while characterizing their uncertainty in a rigorous fashion. Posterior distributions of future temperature and/or precipitation changes at regional scales are obtained, accounting for many peculiar data characteristics. The article confirms the reasonableness of the Bayesian modelling assumptions for climate change projections' uncertainty analysis.
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ESOMAR/EMAC/AFM Symposium on Information Based Decision Making in Marketing: Reconciling the needs and interests of decision makers, data collectors and ... : Paris (France) 17th-19th November 1993. AFM, 1993.

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