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In doing so, we extend the approach developed in Albert and Chib (<jats:italic>Journal of the American Statistical Association<\/jats:italic> 1993; <jats:bold>88<\/jats:bold>: 669\u2013679; in <jats:italic>Bayesian Biostatistics<\/jats:italic>, Berry DA, Stangl DK (eds), Marcel Dekker: New York, 1996), and in Chib and Carlin (<jats:italic>Statistics and Computing<\/jats:italic> 1999; <jats:bold>9<\/jats:bold>: 17\u201326) by releasing restrictive parametric assumptions on the latent individual effect and eliminating potential spurious state dependence with latent time effects. The model is found to outperform more traditional approaches in an extensive series of Monte Carlo simulations. We then apply the model to the estimation of a patent equation using firm\u2010level data on research and development (R&amp;D). We find a strong effect of technology spillovers on R&amp;D but little evidence of product market spillovers, consistent with economic theory. The distribution of latent firm effects is found to have a multimodal structure featuring within\u2010industry firm clustering. Copyright \u00a9 2012 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/jae.2285","type":"journal-article","created":{"date-parts":[[2012,6,27]],"date-time":"2012-06-27T05:51:46Z","timestamp":1340776306000},"page":"956-981","source":"Crossref","is-referenced-by-count":11,"title":["PANEL PROBIT WITH FLEXIBLE CORRELATED EFFECTS: QUANTIFYING TECHNOLOGY SPILLOVERS IN THE PRESENCE OF LATENT HETEROGENEITY"],"prefix":"10.1002","volume":"28","author":[{"given":"Martin","family":"Burda","sequence":"first","affiliation":[{"name":"Department of Economics University of Toronto  Ontario Canada"},{"name":"IES, Charles University  Prague Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Harding","sequence":"additional","affiliation":[{"name":"Department of Economics Stanford University  CA USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2012,6,26]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-4076(99)00009-3"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1993.10476321"},{"key":"e_1_2_7_4_1","first-page":"577","volume-title":"Bayesian Biostatistics","author":"Albert J","year":"1996"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1994.10476863"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA6895"},{"key":"e_1_2_7_7_1","volume-title":"A likelihood\u2010based approximate solution to the incidental parameter problem in dynamic nonlinear models with multiple effects","author":"Arellano M","year":"2006"},{"key":"e_1_2_7_8_1","doi-asserted-by":"crossref","unstructured":"BerryST HailePA>2009.Nonparametric identification of multinomial choice demand models with heterogeneous consumers. 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