market_aggregation | R Documentation |
Market side aggregation.
aggregate_demand(fit, model, parameters) ## S4 method for signature 'missing,market_model,ANY' aggregate_demand(model, parameters) aggregate_supply(fit, model, parameters) ## S4 method for signature 'missing,market_model,ANY' aggregate_supply(model, parameters) ## S4 method for signature 'market_fit,missing,missing' aggregate_demand(fit) ## S4 method for signature 'market_fit,missing,missing' aggregate_supply(fit)
fit |
A fitted market model object. |
model |
A model object. |
parameters |
A vector of model's parameters. |
Calculates the sample's aggregate demand or supply using the estimated coefficients of a fitted model. Alternatively, the function calculates aggregates using a model and a set of parameters passed separately. If the model's data have multiple distinct subjects at each date, aggregation is calculated over subjects per unique date. If the model has time series data, namely a single subject per time point, aggregation is ululated over all time pints.
The sum of the estimated demanded or supplied quantities evaluated at the given parameters.
aggregate_demand
: Demand aggregation.
aggregate_supply
: Supply aggregation.
demanded_quantities, supplied_quantities
fit <- diseq_basic( HS | RM | ID | TREND ~ RM + TREND + W + CSHS + L1RM + L2RM + MONTH | RM + TREND + W + L1RM + MA6DSF + MA3DHF + MONTH, fair_houses(), correlated_shocks = FALSE ) # get estimated aggregate demand aggregate_demand(fit) # simulate the deterministic adjustment model model <- simulate_model( "diseq_deterministic_adjustment", list( # observed entities, observed time points nobs = 500, tobs = 3, # demand coefficients alpha_d = -0.6, beta_d0 = 9.8, beta_d = c(0.3, -0.2), eta_d = c(0.6, -0.1), # supply coefficients alpha_s = 0.2, beta_s0 = 4.1, beta_s = c(0.9), eta_s = c(-0.5, 0.2), # price equation coefficients gamma = 0.9 ), seed = 1356 ) # estimate the model object fit <- estimate(model) # get estimated aggregate demand aggregate_demand(fit) # get estimated aggregate demand aggregate_supply(fit)
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