person(given = "Pantelis",
family = "Karapanagiotis",
role = c("aut", "cre"),
email = "pikappa.devel@gmail.com",
comment = c(ORCID = "0000-0001-9871-1908"))
'equation_base.R' 'system_base.R' 'equation_basic.R'
'system_basic.R' 'derivatives_basic.R'
'equation_deterministic_adjustment.R'
'system_deterministic_adjustment.R'
'derivatives_deterministic_adjustment.R'
'equation_directional.R' 'system_directional.R'
'derivatives_directional.R' 'system_fiml.R'
'derivatives_fiml.R' 'equation_stochastic_adjustment.R'
'system_stochastic_adjustment.R'
'derivatives_stochastic_adjustment.R' 'diseq.R'
'model_logger.R' 'market_model.R' 'disequilibrium_model.R'
'diseq_basic.R' 'diseq_deterministic_adjustment.R'
'diseq_directional.R' 'diseq_stochastic_adjustment.R'
'equilibrium_model.R' 'model_simulation.R'

Estimation Methods for Markets in Equilibrium and Disequilibrium

Provides estimation methods for markets in equilibrium and
disequilibrium. Supports the estimation of an equilibrium and
four disequilibrium models with both correlated and independent shocks.
Also provides post-estimation analysis tools, such as aggregation,
marginal effect, and shortage calculations. The estimation methods are
based on full information maximum likelihood techniques given in
Maddala and Nelson (1974) <doi:10.2307/1914215>. They are implemented
using the analytic derivative expressions calculated in
Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>. Standard
errors can be estimated by adjusting for heteroscedasticity or clustering.
The equilibrium estimation constitutes a case of a system of linear,
simultaneous equations. The disequilibrium models,
instead, replace the market-clearing condition with a non-linear,
short-side rule and allow for different specifications of price dynamics.
bbmle (>= 1.0.20), dplyr (>= 0.7.6), grid, magrittr (>= 1.5),
MASS (>= 7.3-50), methods, rlang (>= 0.2.1), systemfit (>=
1.1), tibble (>= 1.4.2), tidyr (>= 1.0.2), png, Rcpp, RcppGSL,
RcppParallel

Provides estimation methods for markets in equilibrium and
disequilibrium. Supports the estimation of an equilibrium and
four disequilibrium models with both correlated and independent shocks.
Also provides post-estimation analysis tools, such as aggregation,
marginal effect, and shortage calculations. The estimation methods are
based on full information maximum likelihood techniques given in
Maddala and Nelson (1974) <doi:10.2307/1914215>. They are implemented
using the analytic derivative expressions calculated in
Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>. Standard
errors can be estimated by adjusting for heteroscedasticity or clustering.
The equilibrium estimation constitutes a case of a system of linear,
simultaneous equations. Instead, the disequilibrium models replace the
market-clearing condition with a non-linear,
short-side rule and allow for different specifications of price dynamics.
'data.R' 'diseq.R' 'equation_base.R' 'system_base.R'
'model_logger.R' 'market_model.R' 'disequilibrium_model.R'
'diseq_basic.R' 'diseq_deterministic_adjustment.R'
'diseq_directional.R' 'diseq_stochastic_adjustment.R'
'equation_basic.R' 'equation_deterministic_adjustment.R'
'equation_directional.R' 'equation_stochastic_adjustment.R'
'equilibrium_model.R' 'system_basic.R' 'gradient_basic.R'
'system_deterministic_adjustment.R'
'gradient_deterministic_adjustment.R' 'system_directional.R'
'gradient_directional.R' 'system_equilibrium.R'
'gradient_equilibrium.R' 'system_stochastic_adjustment.R'
'gradient_stochastic_adjustment.R' 'hessian_basic.R'
'hessian_directional.R' 'likelihood_basic.R'
'likelihood_deterministic_adjustment.R'
'likelihood_directional.R' 'likelihood_equilibrium.R'
'likelihood_stochastic_adjustment.R' 'model_simulation.R'