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To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.3982/ECTA5658 Byline: Susumu Imai (*), Neelam Jain (**), Andrew Ching (***) Keywords: Bayesian estimation; dynamic programming; discrete choice models; Markov chain Monte Carlo Abstract: We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the dynamic programming (DP) solution algorithm with the Bayesian Markov chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though the number of grid points on the state variable is small per solution-estimation iteration, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the 'curse of dimensionality.' We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values. Author Affiliation: (*)Dept. of Economics, Queen's University, 233 Dunning Hall, 94 University Avenue, Kingston, ON K7L 5M2, Canada;imais@econ.gueensu.ca (**)Dept. of Economics, City University London, Northampton Square, London EC1V 0HB, U.K. and Dept. of Economics, Northern Illinois University, 508 Zulauf Hall, DeKalb, IL 60115, U.S.A.;njain@niu.edu (***)Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, ON M5S 3E6, Canada;andrew.ching@rotman.utoronto.ca Article History: Manuscript received January, 2005; final revision received May, 2009.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.euroecorev.2009.02.001 Byline: Susumu Imai (a), Hajime Katayama (b), Kala Krishna (c) Abstract: This paper asks whether the results obtained from using the standard approach to testing the influential Grossman and Helpman 'protection for sale' model of political economy might arise from a simpler setting. A model of imports and quotas with protection occurring in response to import surges, but only for organized industries, is simulated and shown to provide parameter estimates consistent with the protection for sale framework. This suggests that the standard approach may be less of a test than previously thought. Author Affiliation: (a) Dunning Hall, 94 University Avenue, Queen's University, Kingston, Ontario, Canada K7L 3N6 (b) Room 363, H04, Merewether Building, The University of Sydney, NSW 2006, Australia (c) Department of Economics, The Pennsylvania State University, 523 Kern Graduate Building, University Park, PA 16802, USA Article History: Received 1 February 2007; Accepted 20 February 2009 Article Note: (footnote) [star] We are grateful to the associate editor, two anomyous referees, Pol Antras, Xenia Matschke, and Kei-Mu Yi for insightful comments. All remaining errors are our own.

There is little work on the inner workings of journals. What factors seem to affect the ability to publish in a journal? Could simple rules (which are already used by some journals) like the desk rejection of a significant minority of papers, help to streamline the process? At what cost? How well do journals seem to do in choosing papers? What can we say about the extent of type 1 and type 2 errors? Do editors seem to have uniform standards or are some harsher than others? We use data on submissions to the Journal of International Economics to help answer these questions. Keywords: Publishing in Economics Performance evaluation Probit model Selection bias JEL: A14

Using maximum likelihood techniques and monthly panel data we solve and estimate an explicitly dynamic model of criminal behavior where current criminal activity impacts future labor market outcomes. We show that the threat of future adverse effects in the labor market when arrested acts as a strong deterrent to crime. Moreover, such forward-looking behavior is estimated to be important. Hence, policies that weaken this deterrence will be much less effective in fighting crime. This suggests that prevention is more powerful than redemption since anticipated redemption allows criminals to look forward to negating the consequences of their crimes.

We solve and estimate a dynamic model that allows agents to optimally choose their labor hours and consumption and that allows for both human capital accumulation and savings. Estimation results and simulation exercises indicate that the intertemporal elasticity of substitution is much higher than the conventional estimates and the downward bias comes from the omission of the human capital accumulation effect. The human capital accumulation effect renders the life-cycle path of the shadow wage relatively flat, even though wages increase with age. Hence, a rather flat life-cycle labor supply path can be reconciled with a high intertemporal elasticity of substitution.

In this paper, we survey the empirical analysis of brands. The difficulty in this area of research is that even though a brand is easily identifiable, its economic roles are not directly observable. We first discuss methods where the effect of a brand is measured as the unobservable component of sales or price of a product, which researchers recover as a residual of the sales or price regression. We then describe the approaches to estimate the various roles of brands, such as providing information and increasing the utility of consumption. Finally, we provide examples of research that highlight the role of brands in exploiting the irrationality of consumers for profit. [ABSTRACT FROM AUTHOR]