{ "_id": "6aca71eafe5cc25786e8947e", "Package": "markets", "Title": "Estimation Methods for Markets in Equilibrium and Disequilibrium", "Version": "1.1.7", "Date": "2026-02-04", "Authors@R": "person(given = \"Pantelis\",\nfamily = \"Karapanagiotis\",\nrole = c(\"aut\", \"cre\"),\nemail = \"pikappa.devel@gmail.com\",\ncomment = c(ORCID = \"0000-0001-9871-1908\"))", "Description": "Provides estimation methods for markets in equilibrium and\ndisequilibrium. Supports the estimation of an equilibrium and\nfour disequilibrium models with both correlated and independent\nshocks. Also provides post-estimation analysis tools, such as\naggregation, marginal effect, and shortage calculations. See\nKarapanagiotis (2024) for an\noverview of the functionality and examples. The estimation\nmethods are based on full information maximum likelihood\ntechniques given in Maddala and Nelson (1974)\n. They are implemented using the analytic\nderivative expressions calculated in Karapanagiotis (2020)\n. Standard errors can be estimated by\nadjusting for heteroscedasticity or clustering. The equilibrium\nestimation constitutes a case of a system of linear,\nsimultaneous equations. Instead, the disequilibrium models\nreplace the market-clearing condition with a non-linear,\nshort-side rule and allow for different specifications of price\ndynamics.", "Language": "en-US", "URL": "https://github.com/pi-kappa-devel/markets/,\nhttps://markets.pikappa.eu/", "BugReports": "https://github.com/pi-kappa-devel/markets/issues", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Collate": "'data.R' 'equation_base.R' 'system_base.R' 'model_logger.R'\n'market_model.R' 'disequilibrium_model.R' 'diseq_basic.R'\n'diseq_deterministic_adjustment.R' 'diseq_directional.R'\n'diseq_stochastic_adjustment.R' 'equation_basic.R'\n'equation_deterministic_adjustment.R' 'equation_directional.R'\n'equation_stochastic_adjustment.R' 'equilibrium_model.R'\n'system_basic.R' 'gradient_basic.R'\n'system_deterministic_adjustment.R'\n'gradient_deterministic_adjustment.R' 'system_directional.R'\n'gradient_directional.R' 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