system_classes: System classes

In diseq: Estimation Methods for Markets in Equilibrium and Disequilibrium

System classes

Description

System classes

Details

Classes with data and functionality describing systems of models.

Functions

• system_base-class: System base class

• system_basic-class: Basic model's system class

• system_deterministic_adjustment-class: Deterministic adjustment model's system class

• system_directional-class: Directional system class

• system_equilibrium-class: Equilibrium model's system class

• system_stochastic_adjustment-class: Stochastic adjustment model's system class

Slots

demand

Demand equation.

supply

Supply equation.

correlated_shocks

Boolean indicating whether the shock of the equations of the system are correlated.

sample_separation

Boolean indicating whether the sample of the system is separated.

quantity_vector

A vector with the system's observed quantities.

price_vector

A vector with the system's observed prices.

rho

Correlation coefficient of demand and supply shocks.

rho1

ρ_{1} = \frac{1}{√{1 - ρ}}

rho2

ρ_{2} = ρρ_{1}

lh

Likelihood values for each observation.

gamma

Excess demand coefficient.

delta

δ = γ + α_{d} - α_{s}

mu_P

μ_{P} = \mathrm{E}P

var_P

V_{P} = \mathrm{Var}P

sigma_P

σ_{P} = √{V_{P}}

h_P

h_{P} = \frac{P - μ_{P}}{σ_{P}}

lagged_price_vector

A vector with the system's observed prices lagged by one date.

mu_Q

μ_{Q} = \mathrm{E}Q

var_Q

V_{Q} = \mathrm{Var}Q

sigma_Q

σ_{Q} = √{V_{Q}}

h_Q

h_{Q} = \frac{Q - μ_{Q}}{σ_{Q}}

rho_QP

ρ_{QP} = \frac{\mathrm{Cov}(Q,P)}{√{\mathrm{Var}Q\mathrm{Var}P}}

rho_1QP

ρ_{1,QP} = \frac{1}{√{1 - ρ_{QP}^2}}

rho_2QP

ρ_{2,QP} = ρ_{QP}ρ_{1,QP}

z_QP

z_{QP} = \frac{h_{Q} - ρ_{QP}h_{P}}{√{1 - ρ_{QP}^2}}

z_PQ

z_{PQ} = \frac{h_{P} - ρ_{PQ}h_{Q}}{√{1 - ρ_{PQ}^2}}

price_equation

Price equation.

zeta

ζ = √{1 - ρ_{DS}^2 - ρ_{DP}^2 - ρ_{SP}^2 + 2 ρ_DP ρ_DS ρ_SP}

zeta_DD

ζ_{DD} = 1 - ρ_{SP}^2

zeta_DS

ζ_{DS} = ρ_{DS} - ρ_{DP}ρ_{SP}

zeta_DP

ζ_{DP} = ρ_{DP} - ρ_{DS}ρ_{SP}

zeta_SS

ζ_{SS} = 1 - ρ_{DP}^2

zeta_SP

ζ_{SP} = ρ_{SP} - ρ_{DS}ρ_{DP}

zeta_PP

ζ_{PP} = 1 - ρ_{DS}^2

mu_D

μ_{D} = \mathrm{E}D

var_D

V_{D} = \mathrm{Var}D

sigma_D

σ_{D} = √{V_{D}}

mu_S

μ_{S} = \mathrm{E}S

var_S

V_{S} = \mathrm{Var}S

sigma_S

σ_{S} = √{V_{S}}

sigma_DP

σ_{DP} = \mathrm{Cov}(D, P)

sigma_DS

σ_{DS} = \mathrm{Cov}(D, S)

sigma_SP

σ_{SP} = \mathrm{Cov}(S, P)

rho_DS

ρ_{DS} = \frac{\mathrm{Cov}(D,S)}{√{\mathrm{Var}D\mathrm{Var}S}}

rho_DP

ρ_{DP} = \frac{\mathrm{Cov}(D,P)}{√{\mathrm{Var}D\mathrm{Var}P}}

rho_SP

ρ_{SP} = \frac{\mathrm{Cov}(S,P)}{√{\mathrm{Var}S\mathrm{Var}P}}

h_D

h_{D} = \frac{D - μ_{D}}{σ_{D}}

h_S

h_{S} = \frac{S - μ_{S}}{σ_{S}}

z_DP

z_{DP} = \frac{h_{D} - ρ_{DP}h_{P}}{√{1 - ρ_{DP}^2}}

z_PD

z_{PD} = \frac{h_{P} - ρ_{PD}h_{D}}{√{1 - ρ_{PD}^2}}

z_SP

z_{SP} = \frac{h_{S} - ρ_{SP}h_{P}}{√{1 - ρ_{SP}^2}}

z_PS

z_{PS} = \frac{h_{P} - ρ_{PS}h_{S}}{√{1 - ρ_{PS}^2}}

omega_D

ω_{D} = \frac{h_{D}ζ_{DD} - h_{S}ζ_{DS} - h_{P}ζ_{DP}}{ζ_{DD}}

omega_S

ω_{S} = \frac{h_{S}ζ_{SS} - h_{S}ζ_{SS} - h_{P}ζ_{SP}}{ζ_{SS}}

w_D

w_{D} = - \frac{h_{D}^2 - 2 h_{D} h_{P} ρ_{DP} + h_{P}^2}{2ζ_{SS}}

w_S

w_{S} = - \frac{h_{S}^2 - 2 h_{S} h_{P} ρ_{SP} + h_{P}^2}{2ζ_{DD}}

psi_D

ψ_{D} = φ≤ft(\frac{ω_{D}}{ζ}\right)

psi_S

ψ_{S} = φ≤ft(\frac{ω_{S}}{ζ}\right)

Psi_D

Ψ_{D} = 1 - Φ≤ft(\frac{ω_{D}}{ζ}\right)

Psi_S

Ψ_{S} = 1 - Φ≤ft(\frac{ω_{S}}{ζ}\right)

g_D

g_{D} = \frac{ψ_{D}}{Ψ_{D}}

g_S

g_{S} = \frac{ψ_{S}}{Ψ_{S}}

rho_ds

Shadows rho in the diseq_stochastic_adjustment model

rho_dp

Correlation of demand and price equations' shocks.

rho_sp

Correlation of supply and price equations' shocks.

L_D

Likelihood conditional on excess supply.

L_S

Likelihood conditional on excess demand.

diseq documentation built on June 2, 2022, 1:10 a.m.