Added example of perfect forsesight model (julia).

Note that PyPlot package must be installed.

tests/julia/rbc/test1pfm.jl

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