Most empirical and theoretical econometric studies of dynamic discrete choice models assume the discount factor to be known. We show the knowledge of the discount factor is not necessary to identify parts, or even all, of the payo¤ function. We show the discount factor can be generically identi…ed jointly with the payo¤ parameters. On the other hand it is known the payo¤ function cannot be nonparametrically identi…ed without any a priori restrictions. Our identi…cation of the discount factor is robust to any normalization choice on the payo¤ parameters. In IO applications normalizations are usually made on switching costs, such as entry costs and scrap values. We also show that switching costs can be nonparametrically identi…ed, in closed-form, independently of the discount factor and other parts of the payo¤ function. Our identi…cation strategies are constructive. They lead to easy to compute estimands that are global solutions. We illustrate with a Monte Carlo study and the dataset used in Ryan (2012).

The purpose of this article is to examine the causality between government size and corruption, and to verify if there is a different pattern of causality between developed Organization for Economic Co-operation and Development (OECD) countries (excluding Mexico) and developing countries (Latin American countries) during the period 1996 to 2003. Applying Granger and Huang's (1997) methodology we find evidence that size of government Granger causes corruption in both samples. Since a larger government involvement in private markets today will be followed in future by a higher level of corruption a policy advice would be to enhance governance. The promotion of good governance helps to combat corruption given that it complements efforts to reduce corruption more directly, and it is strongly recommended by the International Monetary Fund, other multilateral institutions, and all worried with the negative impacts of corruption on economic activity.

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This is the accepted version of the paper.This version of the publication may differ from the final published version. (2006) show equilibrium restrictions in a search model can be used to identify quantiles of the search cost distribution from observed prices alone. These quantiles can be di¢ cult to estimate in practice. This paper uses a minimum distance approach to estimate them that is easy to compute. A version of our estimator is a solution to a nonlinear least squares problem that can be straightforwardly programmed on softwares such as STATA. Permanent repository linkWe show our estimator is consistent and has an asymptotic normal distribution. Its distribution can be consistently estimated by a bootstrap. Our estimator can be used to estimate the cost distribution nonparametrically on a larger support when prices from heterogeneous markets are available. We propose a two-step sieve estimator for that case. The …rst step estimates quantiles from each market. They are used in the second step as generated variables to perform nonparametric sieve estimation. We derive the uniform rate of convergence of the sieve estimator that can be used to quantify the errors incurred from interpolating data across markets. To illustrate we use online bookmaking odds for English football leagues'matches (as prices) and …nd evidence that suggests search costs for consumers have fallen following a change in the British law that allows gambling operators to advertise more widely. JEL Classification Numbers: C13, C15, D43, D83, L13Keywords: Bootstrap, Generated Variables, M-Estimation, Search Cost, Sieve EstimationWe are very grateful to the authors of Moraga-González, Sándor and Wildenbeest (2013) for sharing their MATLAB code with us. We thank an anonymous referee for helpful comments and suggestions. We would also like to thank

Most empirical and theoretical econometric studies of dynamic discrete choice models assume the discount factor to be known. We show the knowledge of the discount factor is not necessary to identify parts, or even all, of the payoff function. We show the discount factor can be generically identified jointly with the payoff parameters. On the other hand, it is known the payoff function cannot be nonparametrically identified without any a priori restrictions. Our identification of the discount factor is robust to any normalization choice on the payoff parameters. In IO applications, normalizations are usually made on switching costs, such as entry costs and scrap values. We also show that switching costs can be nonparametrically identified, in closed‐form, independently of the discount factor and other parts of the payoff function. Our identification strategies are constructive. They lead to easy to compute estimands that are global solutions. We illustrate with a Monte Carlo study and the dataset used in Ryan ().