85 lines
3.1 KiB
Julia
85 lines
3.1 KiB
Julia
# Modification of the path (for packages). Should be done in ~/.juliarc.jl with a fixed path instead.
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if isempty(findin([abspath("../../../julia")], LOAD_PATH))
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unshift!(LOAD_PATH, abspath("../../../julia"))
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end
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# Load packages
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using Plots
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using Dynare
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using SteadyState
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using PerfectForesightModelSolver
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plotlyjs() # Choose a backend
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# Compile the rbc.mod file -> produce a module with the model definition.
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@dynare "rbc1.mod"
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@time SteadyState.steady!(model_, oo_)
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println(oo_.steady_state)
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# Initialize paths for the endogenous variables
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endogenousvariables = repmat(oo_.steady_state, 1, options_.pfmsolver.periods+2)
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# Destroy part of the initial stock of physical capital.
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endogenousvariables[1, 1] = .8*endogenousvariables[1, 1]
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# Set path for the innovations (no shocks).
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exogenousvariables = repmat(oo_.exo_steady_state', options_.pfmsolver.periods+2, 1)
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# Simulate the transition to the steady state
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@time simulate_perfect_foresight_model!(endogenousvariables, exogenousvariables, oo_.steady_state, model_, options_)
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n = 200
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dates = collect(0:n-1)
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#plt[:figure](1)
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plot(dates, vec(endogenousvariables[1,1:n]), color="black", linewidth=2.0, linestyle="-")
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# Initialize paths for the endogenous variables
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endogenousvariables = repmat(oo_.steady_state, 1, options_.pfmsolver.periods+2)
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# Destroy part of the initial stock of physical capital...
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endogenousvariables[1, 1] = .8*endogenousvariables[1, 1]
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# ... and assume that TFP is initially above its steady state level.
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endogenousvariables[6, 1] = .5
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# Set path for the innovations (no shocks).
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exogenousvariables = repmat(oo_.exo_steady_state', options_.pfmsolver.periods+2, 1)
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# Simulate the transition to the steady state (we should have an hump shaped transition)
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@time simulate_perfect_foresight_model!(endogenousvariables, exogenousvariables, oo_.steady_state, model_, options_)
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n = 200
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dates = collect(0:n-1)
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plot(dates, vec(endogenousvariables[1,1:n]), color="red", linewidth=2.0, linestyle="-")
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# Initialize paths for the endogenous variables
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endogenousvariables = repmat(oo_.steady_state, 1, options_.pfmsolver.periods+2)
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# Set path for the innovations (no shocks).
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exogenousvariables = repmat(oo_.exo_steady_state', options_.pfmsolver.periods+2, 1)
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# Assume positive expected TFP shock in period 10
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exogenousvariables[10+1, 1] = 2
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# Simulate the paths for the endogenous variables, given the expected shock
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@time simulate_perfect_foresight_model!(endogenousvariables, exogenousvariables, oo_.steady_state, model_, options_)
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# dates = collect(0:n-1)
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# n = 200
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# plt[:figure](2)
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# subplot(221)
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# title("Efficiency")
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# plot(dates, vec(endogenousvariables[5,1:n]), color="black", linewidth=2.0, linestyle="-")
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# subplot(223)
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# title("Output")
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# plot(dates, vec(endogenousvariables[2,1:n]), color="black", linewidth=2.0, linestyle="-")
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# subplot(222)
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# title("Consumption")
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# plot(dates, vec(endogenousvariables[4,1:n]), color="black", linewidth=2.0, linestyle="-")
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# subplot(224)
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# title("Labour")
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# plot(dates, vec(endogenousvariables[3,1:n]), color="black", linewidth=2.0, linestyle="-")
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# suptitle("Expected positive expected shock")
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