CEMFI

About: CEMFI is a based out in . It is known for research contribution in the topics: Unemployment & Estimator. The organization has 71 authors who have published 499 publications receiving 46553 citations. The organization is also known as: Center for Monetary and Financial Studies.

Identification of games of incomplete information with multiple equilibria and unobserved heterogeneity

Abstract: This paper deals with identification of discrete games of incomplete information when we allow for three types of unobservables: payoff‐relevant variables, both players' private information and common knowledge, and nonpayoff‐relevant variables that determine the selection between multiple equilibria. The specification of the payoff function and the distributions of the common knowledge unobservables is nonparametric with finite support (i.e., finite mixture model). We provide necessary and sufficient conditions for the identification of all the primitives of the model. Two types of conditions play a key role in our identification results: independence between players' private information, and an exclusion restriction in the payoff function. When using a sequential identification approach, we find that the up‐to‐label‐swapping identification of the finite mixture model in the first step creates a problem in the identification of the payoff function in the second step: unobserved types have to be correctly matched across different values of observable explanatory variables. We show that this matching‐type problem appears in the sequential estimation of other structural models with nonparametric finite mixtures. We derive necessary and sufficient conditions for identification, and show that additive separability of unobserved heterogeneity in the payoff function is a sufficient condition to deal with this problem. We also compare sequential and joint identification approaches. Discrete games of incomplete information multiple equilibria in the data unobserved heterogeneity finite mixture models identification up to label swapping C13 C35 C57

New Imported Inputs, New Domestic Products

Abstract: We study the effects of new imported inputs on the entry of new domestic products and their characteristics. To this purpose, we construct a novel, comprehensive and extremely detailed dataset, which contains product-level information on foreign trade and domestic production for 25 EU countries over 1995-2007. Using these data, we identify new domestic goods and new imported inputs, controlling for all changes in commodity classifications over time. We then show that new imported inputs substantially boost the introduction of new domestic products. We also show that this effect is directly proportional to the quality of new imported inputs and inversely related to their price (conditional on quality). Finally, we document that new products are characterized by higher prices and higher quality relative to existing goods, and that such premia are larger the greater is the use of new imported inputs in production.

Does Idiosyncratic Business Risk Matter

Abstract: Financial market imperfections can prevent entrepreneurs from diversifying away the idiosyncratic risk of their business. As a result idiosyncratic risk discourages entrepreneurial activity and hinders growth, with the effects being stronger in economies with lower risk diversification opportunities. In accordance with this prediction we find that OECD countries with low levels of risk diversification opportunities (as measured by the relevance of family firms or of widely held companies) perform relatively worse (in terms of productivity, investment, and business creation) in sectors characterized by high idiosyncratic volatility. Given that volatility is endogenous with respect to risk diversification opportunities, we instrument its value at the country-sector level with the corresponding sectoral volatility in the US, a country where financial imperfections are less relevant than elsewhere. Diversification measures are instrumented using demographic changes induced by World War II. We also provide firm-level evidence suggesting that firms controlled by less diversified owners display lower mean and dispersion of productivity growth.

Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models

Abstract: This paper proposes a procedure for the estimation of discrete Markov decision models and studies its statistical and computational properties. Our Nested Pseudo-Likelihood method (NPL) is similar to Rust's Nested Fixed Point algorithm (NFXP), but the order of the two nested algorithms is swapped. First, we prove that NPL produces the Maximum Likelihood Estimator under the same conditions as NFXP. Our procedure requires fewer policy iterations at the expense of more likelihood-climbing iterations. We focus on a class of infinite-horizon, partial likelihood problems for which NPL results in large computational gains. Second, based on this algorithm we define a class of consistent and asymptotically equivalent Sequential Policy Iteration (PI) estimators, which encompasses both Hotz-Miller's CCP estimator and the partial Maximum Likelihood estimator. This presents the researcher with a "menu" of sequential estimators reflecting a trade-off between finite-sample precision and computational cost. Using actual and simulated data we compare the relative performance of these estimators. In all our experiments the benefits in terms of precision of using a 2-stage PI estimator instead of 1-stage (i.e., Hotz-Miller) are very significant. More interestingly, the benefits of MLE relative to 2-stage PI are negligible.