Short-sighted managers and learnable sunspot equilibria |
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Affiliation: | 1. Department of Statistical Sciences, University of Toronto, Toronto, Ontario M5S 3G3, Canada;2. Quantitative Engineering and Development, TD Securities, Toronto, Ontario M5K 1A2, Canada;3. Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA 52242, USA;1. Department of Physics, Panskura Banamali College, Panskura-721152, India;2. Department of Physics, Vidyasagar University, Midnapore-721102, India;3. Jaypee Institute of Information Technology, A-10, Sector-62, Noida, UP-201307, India;4. Department of Physics, Vidyasagar Teachers’ Training College, Midnapore-721101, India;1. Indian Statistical Institute, Statistics and Mathematics Unit, 8th Mile, Mysore Road, Bangalore 560059, India;2. Department of Mathematics and Statistics, Fylde College, Lancaster University, Lancaster LA1 4YF, UK;1. Programa de Geriatría, Servicio de Medicina Interna, Hospital Universitari de Bellvitge, IDIBELL, L’Hospitalet de LLobregat, Barcelona, España;2. Servicio de Medicina Interna, Hospital Clínico Universitario Lozano Blesa, Facultad de Medicina, Instituto de Investigación Sanitaria de Aragón (IIS-Aragón), Zaragoza, España |
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Abstract: | This paper assumes that firm managers make choices over a finite horizon while households plan over an infinite horizon. Following Shea (2013), I assume that labor exhibits firm-specific learning by doing so that newly employed labor is less productive than experienced labor. In the model, optimization requires that firm managers make conjectures about how their choices affect the labor demand choices of their successors. The model yields two steady states; one where the firm manager behaves as if she cares only about the present period and another where she is forward looking. The former (myopic) steady state usually exhibits higher output than the non myopic steady state. The non-myopic steady state also exhibits two regions of indeterminacy where extraneous, self-fulfilling expectational errors add volatility. One of these regions of indeterminacy is usually stable under adaptive learning while the other never is stable under learning. |
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