diff --git a/code/do_all.py b/code/do_all.py
index 24e4584..0d75360 100644
--- a/code/do_all.py
+++ b/code/do_all.py
@@ -45,7 +45,7 @@
 still run.
 """
 
-from estimark.estimation import estimate
+from estimark.min import estimate_min
 from estimark.options import (
     all_replications,
     high_resource,
@@ -143,7 +143,7 @@ def run_replication():
         replication_specs["subjective_labor"] = True
         print("Adding subjective labor market beliefs...")
 
-    estimate(**replication_specs)
+    estimate_min(**replication_specs)
 
 
 if __name__ == "__main__":
diff --git a/code/estimark/estimation.py b/code/estimark/estimation.py
index a4f127b..8cd322c 100644
--- a/code/estimark/estimation.py
+++ b/code/estimark/estimation.py
@@ -34,7 +34,6 @@
 # Parameters for the consumer type and the estimation
 from estimark.parameters import (
     age_mapping,
-    bootstrap_options,
     init_calibration,
     init_params_options,
     init_subjective_labor,
@@ -47,13 +46,6 @@
 from estimark.scf import scf_data
 from estimark.snp import snp_data
 
-# Pathnames to the other files:
-# Relative directory for primitive parameter files
-tables_dir = "content/tables/"
-Path(tables_dir).mkdir(parents=True, exist_ok=True)
-# Relative directory for primitive parameter files
-figures_dir = "content/figures/"
-Path(figures_dir).mkdir(parents=True, exist_ok=True)
 
 # =====================================================
 # Define objects and functions used for the estimation
@@ -149,6 +141,40 @@ def get_weighted_moments(
     return emp_moments
 
 
+def get_moments_cov(agent_name, emp_moments):
+    moments_cov = em.get_moments_cov(
+        scf_data,
+        get_weighted_moments,
+        moment_kwargs={
+            "variable": "wealth_income_ratio",
+            "weights": "weight",
+            "groups": "age_group",
+            "mapping": age_mapping,
+        },
+    )
+
+    if "Port" in agent_name:
+
+        for key1 in emp_moments:
+            # Check if key1 exists in moments_cov dictionary
+            if key1 not in moments_cov:
+                # If it doesn't exist, create a new dictionary for this key
+                moments_cov[key1] = {}
+
+            for key2 in emp_moments:
+                # Check if key2 exists in the nested dictionary under key1
+                if key2 not in moments_cov[key1]:
+                    # If it doesn't exist, we need to add it
+                    if key1 == key2:
+                        # If key1 is equal to key2, set the value to 1.0
+                        moments_cov[key1][key2] = 1.0
+                    else:
+                        # Otherwise, set the value to 0.0
+                        moments_cov[key1][key2] = 0.0
+
+    return moments_cov
+
+
 def get_empirical_moments(agent_name):
     emp_moments = get_weighted_moments(
         data=scf_data,
@@ -420,10 +446,7 @@ def calculate_se_bootstrap(
 
 
 def do_estimate_model(
-    agent,
-    emp_moments,
-    initial_guess,
-    minimize_options=None,
+    agent, emp_moments, initial_guess, minimize_options=None, tables_dir=None
 ):
     fmt_init_guess = [f"{key} = {value:.3f}" for key, value in initial_guess.items()]
     multistart_text = " with multistart" if minimize_options.get("multistart") else ""
@@ -481,7 +504,12 @@ def do_estimate_model(
 
 
 def save_results(
-    res, agent_name, time_to_estimate, dir, params_key=None, keys_to_save=None
+    res,
+    agent_name,
+    time_to_estimate,
+    dir,
+    params_key=None,
+    keys_to_save=None,
 ):
     model_estimate = getattr(res, params_key)
 
@@ -521,6 +549,7 @@ def do_compute_se_boostrap(
     time_to_estimate,
     bootstrap_size=50,
     seed=0,
+    tables_dir=None,
 ):
     # Estimate the model:
     print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
@@ -568,7 +597,7 @@ def do_compute_se_boostrap(
         )
 
 
-def do_compute_sensitivity(agent, model_estimate, emp_moments):
+def do_compute_sensitivity(agent, model_estimate, emp_moments, figures_dir=None):
     print("``````````````````````````````````````````````````````````````````````")
     print("Computing sensitivity measure.")
     print("``````````````````````````````````````````````````````````````````````")
@@ -614,7 +643,7 @@ def do_compute_sensitivity(agent, model_estimate, emp_moments):
     plt.show()
 
 
-def do_make_contour_plot(agent, model_estimate, emp_moments):
+def do_make_contour_plot(agent, model_estimate, emp_moments, figures_dir=None):
     print("``````````````````````````````````````````````````````````````````````")
     print("Creating the contour plot.")
     print("``````````````````````````````````````````````````````````````````````")
@@ -655,123 +684,3 @@ def do_make_contour_plot(agent, model_estimate, emp_moments):
     plt.savefig(figures_dir + agent.name + "SMMcontour.png")
     plt.savefig(figures_dir + agent.name + "SMMcontour.svg")
     plt.show()
-
-
-def estimate(
-    init_agent_name,
-    params_to_estimate,
-    estimate_model=True,
-    compute_se_bootstrap=False,
-    compute_sensitivity=False,
-    make_contour_plot=False,
-    subjective_stock=False,
-    subjective_labor=False,
-):
-    """Run the main estimation procedure for SolvingMicroDSOP.
-
-    Parameters
-    ----------
-    estimate_model : bool
-        Whether to estimate the model using Nelder-Mead. When True, this is a low-time, low-memory operation.
-
-    compute_standard_errors : bool
-        Whether to compute standard errors on the estiamtion of the model.
-
-    make_contour_plot : bool
-        Whether to make the contour plot associate with the estiamte.
-
-    Returns
-    -------
-    None
-
-    """
-    ############################################################
-    # Make agent
-    ############################################################
-
-    agent = make_agent(
-        init_agent_name=init_agent_name,
-        subjective_stock=subjective_stock,
-        subjective_labor=subjective_labor,
-    )
-
-    ############################################################
-    # Get empirical moments
-    ############################################################
-
-    emp_moments = get_empirical_moments(agent.name)
-
-    ############################################################
-    # Get initial guess
-    ############################################################
-
-    initial_guess = get_initial_guess(
-        agent.name,
-        params_to_estimate,
-        tables_dir,
-    )
-
-    ############################################################
-    # Estimate model
-    ############################################################
-
-    if estimate_model:
-        model_estimate, time_to_estimate = do_estimate_model(
-            agent,
-            emp_moments,
-            initial_guess,
-            minimize_options=minimize_options,
-        )
-
-        # Compute standard errors by bootstrap
-        if compute_se_bootstrap:
-            do_compute_se_boostrap(
-                agent,
-                model_estimate,
-                time_to_estimate,
-                **bootstrap_options,
-            )
-
-        # Compute sensitivity measure
-        if compute_sensitivity:
-            do_compute_sensitivity(
-                agent,
-                model_estimate,
-                initial_guess,
-            )
-
-        # Make a contour plot of the objective function
-        if make_contour_plot:
-            do_make_contour_plot(
-                agent,
-                model_estimate,
-                emp_moments,
-            )
-
-
-if __name__ == "__main__":
-    # Set booleans to determine which tasks should be done
-    # Which agent type to estimate ("IndShock" or "Portfolio")
-    local_agent_name = "IndShock"
-    local_params_to_estimate = ["CRRA", "DiscFac"]
-    local_estimate_model = True  # Whether to estimate the model
-    # Whether to get standard errors via bootstrap
-    local_compute_se_bootstrap = False
-    # Whether to compute a measure of estimates' sensitivity to moments
-    local_compute_sensitivity = False
-    # Whether to make a contour map of the objective function
-    local_make_contour_plot = False
-    # Whether to use subjective beliefs
-    local_subjective_stock = False
-    local_subjective_labor = False
-
-    estimate(
-        init_agent_name=local_agent_name,
-        params_to_estimate=local_params_to_estimate,
-        estimate_model=local_estimate_model,
-        compute_se_bootstrap=local_compute_se_bootstrap,
-        compute_sensitivity=local_compute_sensitivity,
-        make_contour_plot=local_make_contour_plot,
-        subjective_stock=local_subjective_stock,
-        subjective_labor=local_subjective_labor,
-    )
diff --git a/code/estimark/min.py b/code/estimark/min.py
new file mode 100644
index 0000000..f6adea7
--- /dev/null
+++ b/code/estimark/min.py
@@ -0,0 +1,144 @@
+from estimark.estimation import (
+    do_compute_se_boostrap,
+    do_compute_sensitivity,
+    do_estimate_model,
+    do_make_contour_plot,
+    get_empirical_moments,
+    get_initial_guess,
+    make_agent,
+)
+from estimark.parameters import bootstrap_options, minimize_options
+from pathlib import Path
+
+
+# Pathnames to the other files:
+# Relative directory for primitive parameter files
+tables_dir = "content/tables/min/"
+Path(tables_dir).mkdir(parents=True, exist_ok=True)
+# Relative directory for primitive parameter files
+figures_dir = "content/figures/min/"
+Path(figures_dir).mkdir(parents=True, exist_ok=True)
+
+
+def estimate_min(
+    init_agent_name,
+    params_to_estimate,
+    estimate_model=True,
+    compute_se_bootstrap=False,
+    compute_sensitivity=False,
+    make_contour_plot=False,
+    subjective_stock=False,
+    subjective_labor=False,
+):
+    """Run the main estimation procedure for SolvingMicroDSOP.
+
+    Parameters
+    ----------
+    estimate_model : bool
+        Whether to estimate the model using Nelder-Mead. When True, this is a low-time, low-memory operation.
+
+    compute_standard_errors : bool
+        Whether to compute standard errors on the estiamtion of the model.
+
+    make_contour_plot : bool
+        Whether to make the contour plot associate with the estiamte.
+
+    Returns
+    -------
+    None
+
+    """
+    ############################################################
+    # Make agent
+    ############################################################
+
+    agent = make_agent(
+        init_agent_name=init_agent_name,
+        subjective_stock=subjective_stock,
+        subjective_labor=subjective_labor,
+    )
+
+    ############################################################
+    # Get empirical moments
+    ############################################################
+
+    emp_moments = get_empirical_moments(agent.name)
+
+    ############################################################
+    # Get initial guess
+    ############################################################
+
+    initial_guess = get_initial_guess(
+        agent.name,
+        params_to_estimate,
+        tables_dir,
+    )
+
+    ############################################################
+    # Estimate model
+    ############################################################
+
+    if estimate_model:
+        model_estimate, time_to_estimate = do_estimate_model(
+            agent,
+            emp_moments,
+            initial_guess,
+            minimize_options=minimize_options,
+            tables_dir=tables_dir,
+        )
+
+        # Compute standard errors by bootstrap
+        if compute_se_bootstrap:
+            do_compute_se_boostrap(
+                agent,
+                model_estimate,
+                time_to_estimate,
+                tables_dir=tables_dir,
+                **bootstrap_options,
+            )
+
+        # Compute sensitivity measure
+        if compute_sensitivity:
+            do_compute_sensitivity(
+                agent,
+                model_estimate,
+                initial_guess,
+                figures_dir=figures_dir,
+            )
+
+        # Make a contour plot of the objective function
+        if make_contour_plot:
+            do_make_contour_plot(
+                agent,
+                model_estimate,
+                emp_moments,
+                figures_dir=figures_dir,
+            )
+
+
+if __name__ == "__main__":
+    # Set booleans to determine which tasks should be done
+    # Which agent type to estimate ("IndShock" or "Portfolio")
+    local_agent_name = "Portfolio"
+    local_params_to_estimate = ["CRRA", "DiscFac"]
+    local_estimate_model = True  # Whether to estimate the model
+    # Whether to get standard errors via bootstrap
+    local_compute_se_bootstrap = False
+    # Whether to compute a measure of estimates' sensitivity to moments
+    local_compute_sensitivity = False
+    # Whether to make a contour map of the objective function
+    local_make_contour_plot = False
+    # Whether to use subjective beliefs
+    local_subjective_stock = False
+    local_subjective_labor = False
+
+    estimate_min(
+        init_agent_name=local_agent_name,
+        params_to_estimate=local_params_to_estimate,
+        estimate_model=local_estimate_model,
+        compute_se_bootstrap=local_compute_se_bootstrap,
+        compute_sensitivity=local_compute_sensitivity,
+        make_contour_plot=local_make_contour_plot,
+        subjective_stock=local_subjective_stock,
+        subjective_labor=local_subjective_labor,
+    )
diff --git a/code/estimark/msm.py b/code/estimark/msm.py
index 370418c..3134352 100644
--- a/code/estimark/msm.py
+++ b/code/estimark/msm.py
@@ -1,58 +1,32 @@
-import csv
 from pathlib import Path
 from time import time
 
 import estimagic as em
-import numpy as np
 
 from estimark.estimation import (
     get_empirical_moments,
     get_initial_guess,
-    get_weighted_moments,
+    get_moments_cov,
     make_agent,
-    simulate_moments,
     save_results,
+    simulate_moments,
 )
 
 # Parameters for the consumer type and the estimation
 from estimark.parameters import (
-    age_mapping,
     init_params_options,
     minimize_options,
 )
 
 # SCF 2004 data on household wealth
-from estimark.scf import scf_data
-
-msm_dir = "content/tables/msm/"
-Path(msm_dir).mkdir(parents=True, exist_ok=True)
-
-
-def get_moments_cov(agent_name, emp_moments):
-    moments_cov = em.get_moments_cov(
-        scf_data,
-        get_weighted_moments,
-        moment_kwargs={
-            "variable": "wealth_income_ratio",
-            "weights": "weight",
-            "groups": "age_group",
-            "mapping": age_mapping,
-        },
-    )
-
-    if "Port" in agent_name:
-        # how many keys in emp_moments contain "_port"
-        n_port = sum("_port" in key for key in emp_moments.keys())
-        share_moments_cov = np.diag(np.ones(n_port))
-
-        moments_cov = np.block(
-            [
-                [moments_cov, np.zeros((moments_cov.shape[0], n_port))],
-                [np.zeros((n_port, moments_cov.shape[1])), share_moments_cov],
-            ],
-        )
 
-    return moments_cov
+# Pathnames to the other files:
+# Relative directory for primitive parameter files
+tables_dir = "content/tables/msm/"
+Path(tables_dir).mkdir(parents=True, exist_ok=True)
+# Relative directory for primitive parameter files
+figures_dir = "content/figures/msm/"
+Path(figures_dir).mkdir(parents=True, exist_ok=True)
 
 
 def estimate_msm(
@@ -93,7 +67,7 @@ def estimate_msm(
     # Get initial guess
     ############################################################
 
-    initial_guess = get_initial_guess(agent.name, params_to_estimate, msm_dir)
+    initial_guess = get_initial_guess(agent.name, params_to_estimate, tables_dir)
 
     print("Estimating MSM...")
 
@@ -126,13 +100,13 @@ def estimate_msm(
 
     print("MSM estimation complete.")
 
-    save_results(res, agent.name, time_to_estimate, msm_dir, "_params")
+    save_results(res, agent.name, time_to_estimate, tables_dir, "_params")
 
 
 if __name__ == "__main__":
     # Set booleans to determine which tasks should be done
     # Which agent type to estimate ("IndShock" or "Portfolio")
-    local_agent_name = "IndShock"
+    local_agent_name = "Portfolio"
 
     # Whether to use subjective beliefs
     local_subjective_stock = False
diff --git a/code/estimark/parameters.py b/code/estimark/parameters.py
index 7bc6954..689c420 100644
--- a/code/estimark/parameters.py
+++ b/code/estimark/parameters.py
@@ -15,7 +15,7 @@
 # Assets grid
 exp_nest = 1  # Number of times to "exponentially nest" when constructing a_grid
 aXtraMin = 0.001  # Minimum end-of-period "assets above minimum" value
-aXtraMax = 20  # Maximum end-of-period "assets above minimum" value
+aXtraMax = 100  # Maximum end-of-period "assets above minimum" value
 aXtraCount = 20  # Number of points in the grid of "assets above minimum"
 
 # Artificial borrowing constraint
@@ -145,8 +145,10 @@
     "algo_options": {
         "convergence.absolute_params_tolerance": 1e-3,
         "convergence.absolute_criterion_tolerance": 1e-3,
+        "stopping.max_iterations": 50,
+        "stopping.max_criterion_evaluations": 100,
+        # "n_cores": 12,
     },
-    # "algorithm": "scipy_lbfgsb",
     # "numdiff_options": {"n_cores": 12},
 }
 
diff --git a/code/run_all.py b/code/run_all.py
index b0f9565..b122acf 100644
--- a/code/run_all.py
+++ b/code/run_all.py
@@ -1,6 +1,6 @@
 import dask
 from dask.distributed import Client
-from estimark.estimation import estimate
+from estimark.min import estimate_min
 from estimark.options import (
     all_replications,
     high_resource,
@@ -70,7 +70,7 @@ def run_replication():
                 replication_specs["subjective_labor"] = True
                 print("Adding subjective labor market beliefs...")
 
-            lazy_result = dask.delayed(estimate)(**replication_specs)
+            lazy_result = dask.delayed(estimate_min)(**replication_specs)
             lazy_results.append(lazy_result)
 
     dask.compute(*lazy_results)
diff --git a/code/tests.py b/code/tests.py
index c09474b..bdf5425 100644
--- a/code/tests.py
+++ b/code/tests.py
@@ -1,32 +1,32 @@
-from estimark.estimation import estimate
+from estimark.min import estimate_min
 from estimark.options import low_resource, medium_resource
 
 
 def test_low_resource():
     print("Running low-resource replication...")
-    estimate(**low_resource)
+    estimate_min(**low_resource)
 
 
 def test_medium_resource():
     print("Running medium-resource replication...")
-    estimate(**medium_resource)
+    estimate_min(**medium_resource)
 
 
 def test_portfolio_low_resource():
     print("Running medium-resource replication...")
-    estimate(**low_resource, init_agent_name="Portfolio")
+    estimate_min(**low_resource, init_agent_name="Portfolio")
 
 
 def test_warmglow_low_resource():
     print("Running medium-resource replication...")
-    estimate(**low_resource, init_agent_name="WarmGlow")
+    estimate_min(**low_resource, init_agent_name="WarmGlow")
 
 
 def test_warmglowportfolio_low_resource():
     print("Running medium-resource replication...")
-    estimate(**low_resource, init_agent_name="WarmGlowPortfolio")
+    estimate_min(**low_resource, init_agent_name="WarmGlowPortfolio")
 
 
 def test_wealthportfolio_low_resource():
     print("Running medium-resource replication...")
-    estimate(**low_resource, init_agent_name="WealthPortfolio")
+    estimate_min(**low_resource, init_agent_name="WealthPortfolio")
diff --git a/content/tables/IndShockSub(Labor)Market_estimate_results.csv b/content/tables/IndShockSub(Labor)Market_estimate_results.csv
index 0eaee39..72d0504 100644
--- a/content/tables/IndShockSub(Labor)Market_estimate_results.csv
+++ b/content/tables/IndShockSub(Labor)Market_estimate_results.csv
@@ -1,15 +1,15 @@
 CRRA,20.0
 DiscFac,1.0
-time_to_estimate,166.9264132976532
+time_to_estimate,124.87122821807861
 params,"{'CRRA': 20.0, 'DiscFac': 1.0}"
-criterion,562.694967726188
-start_criterion,1091.723914842073
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+criterion,544.5282150454914
+start_criterion,544.7352756884602
+start_params,"{'CRRA': 20.0, 'DiscFac': 1.0}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,24
+n_criterion_evaluations,13
 n_derivative_evaluations,
-n_iterations,13
+n_iterations,6
diff --git a/content/tables/IndShockSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/IndShockSub(Stock)(Labor)Market_estimate_results.csv
index 854948f..b8398a8 100644
--- a/content/tables/IndShockSub(Stock)(Labor)Market_estimate_results.csv
+++ b/content/tables/IndShockSub(Stock)(Labor)Market_estimate_results.csv
@@ -1,15 +1,15 @@
 CRRA,20.0
 DiscFac,1.0
-time_to_estimate,166.9813163280487
+time_to_estimate,126.26256227493286
 params,"{'CRRA': 20.0, 'DiscFac': 1.0}"
-criterion,575.865531975731
-start_criterion,1115.9263844617587
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+criterion,557.4368802957166
+start_criterion,557.6869633061931
+start_params,"{'CRRA': 20.0, 'DiscFac': 1.0}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,24
+n_criterion_evaluations,13
 n_derivative_evaluations,
-n_iterations,13
+n_iterations,6
diff --git a/content/tables/IndShockSub(Stock)Market_estimate_results.csv b/content/tables/IndShockSub(Stock)Market_estimate_results.csv
index 44e0440..85f2c64 100644
--- a/content/tables/IndShockSub(Stock)Market_estimate_results.csv
+++ b/content/tables/IndShockSub(Stock)Market_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,7.02580153071005
-DiscFac,0.9821557198347557
-time_to_estimate,453.12678694725037
-params,"{'CRRA': 7.02580153071005, 'DiscFac': 0.9821557198347557}"
-criterion,528.1077787460181
-start_criterion,636.2158488899107
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+CRRA,6.054570670364653
+DiscFac,0.9862525466473355
+time_to_estimate,407.98607778549194
+params,"{'CRRA': 6.054570670364653, 'DiscFac': 0.9862525466473355}"
+criterion,532.5629663834426
+start_criterion,534.9462229937646
+start_params,"{'CRRA': 7.02580153071005, 'DiscFac': 0.9821557198347557}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,151
+n_criterion_evaluations,155
 n_derivative_evaluations,
-n_iterations,75
+n_iterations,72
diff --git a/content/tables/IndShock_estimate_results.csv b/content/tables/IndShock_estimate_results.csv
index 31a366f..6f3c268 100644
--- a/content/tables/IndShock_estimate_results.csv
+++ b/content/tables/IndShock_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,7.0792573883887195

-DiscFac,0.9710907218282636

-time_to_estimate,265.2772467136383

-params,"{'CRRA': 7.0792573883887195, 'DiscFac': 0.9710907218282636}"

-criterion,497.887755198283

-start_criterion,497.887755198283

-start_params,"{'CRRA': 7.0792573883887195, 'DiscFac': 0.9710907218282636}"

-algorithm,multistart_scipy_neldermead

-direction,minimize

-n_free,2

-message,Optimization terminated successfully.

-success,True

-n_criterion_evaluations,126

-n_derivative_evaluations,

-n_iterations,61

+CRRA,6.526991832288527
+DiscFac,0.9739934841042822
+time_to_estimate,399.25848603248596
+params,"{'CRRA': 6.526991832288527, 'DiscFac': 0.9739934841042822}"
+criterion,501.12312447594826
+start_criterion,502.30240594034615
+start_params,"{'CRRA': 7.0792573883887195, 'DiscFac': 0.9710907218282636}"
+algorithm,multistart_scipy_neldermead
+direction,minimize
+n_free,2
+message,Optimization terminated successfully.
+success,True
+n_criterion_evaluations,149
+n_derivative_evaluations,
+n_iterations,69
diff --git a/content/tables/PortfolioSub(Labor)Market_estimate_results.csv b/content/tables/PortfolioSub(Labor)Market_estimate_results.csv
new file mode 100644
index 0000000..60faaf5
--- /dev/null
+++ b/content/tables/PortfolioSub(Labor)Market_estimate_results.csv
@@ -0,0 +1,15 @@
+CRRA,5.865453132577567
+DiscFac,0.5
+time_to_estimate,751.97678399086
+params,"{'CRRA': 5.865453132577567, 'DiscFac': 0.5}"
+criterion,0.923070680889991
+start_criterion,5.27642528130259
+start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+algorithm,multistart_scipy_neldermead
+direction,minimize
+n_free,2
+message,Maximum number of iterations has been exceeded.
+success,False
+n_criterion_evaluations,268
+n_derivative_evaluations,
+n_iterations,100
diff --git a/content/tables/PortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/PortfolioSub(Stock)(Labor)Market_estimate_results.csv
index 454324b..8a37dd7 100644
--- a/content/tables/PortfolioSub(Stock)(Labor)Market_estimate_results.csv
+++ b/content/tables/PortfolioSub(Stock)(Labor)Market_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,5.278040599215181
-DiscFac,0.6460704140717104
-time_to_estimate,466.3470206260681
-params,"{'CRRA': 5.278040599215181, 'DiscFac': 0.6460704140717104}"
-criterion,0.9586251907598755
-start_criterion,3.236297547891923
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+CRRA,4.724872715799437
+DiscFac,0.5827256679465178
+time_to_estimate,404.2306773662567
+params,"{'CRRA': 4.724872715799437, 'DiscFac': 0.5827256679465178}"
+criterion,0.953237438372669
+start_criterion,3.7130193420488324
+start_params,"{'CRRA': 5.278040599215181, 'DiscFac': 0.6460704140717104}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,113
+n_criterion_evaluations,103
 n_derivative_evaluations,
-n_iterations,58
+n_iterations,50
diff --git a/content/tables/PortfolioSub(Stock)Market_estimate_results.csv b/content/tables/PortfolioSub(Stock)Market_estimate_results.csv
index 9d08990..74a770f 100644
--- a/content/tables/PortfolioSub(Stock)Market_estimate_results.csv
+++ b/content/tables/PortfolioSub(Stock)Market_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,4.6378134894615615
+CRRA,3.9886633995822067
 DiscFac,0.5
-time_to_estimate,296.98850679397583
-params,"{'CRRA': 4.6378134894615615, 'DiscFac': 0.5}"
-criterion,1.177996454996367
-start_criterion,1.7122028387950428
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+time_to_estimate,285.0257930755615
+params,"{'CRRA': 3.9886633995822067, 'DiscFac': 0.5}"
+criterion,1.0425570066466112
+start_criterion,3.7728432111104273
+start_params,"{'CRRA': 4.6378134894615615, 'DiscFac': 0.5}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,86
+n_criterion_evaluations,91
 n_derivative_evaluations,
-n_iterations,42
+n_iterations,47
diff --git a/content/tables/Portfolio_estimate_results.csv b/content/tables/Portfolio_estimate_results.csv
new file mode 100644
index 0000000..3b39113
--- /dev/null
+++ b/content/tables/Portfolio_estimate_results.csv
@@ -0,0 +1,15 @@
+CRRA,3.0761208392259816
+DiscFac,0.5757132378363539
+time_to_estimate,580.4633679389954
+params,"{'CRRA': 3.0761208392259816, 'DiscFac': 0.5757132378363539}"
+criterion,2.3565297732828996
+start_criterion,5.266598881640827
+start_params,"{'CRRA': 3.1517962160236057, 'DiscFac': 0.5742414027038963}"
+algorithm,multistart_scipy_neldermead
+direction,minimize
+n_free,2
+message,Maximum number of iterations has been exceeded.
+success,False
+n_criterion_evaluations,270
+n_derivative_evaluations,
+n_iterations,100
diff --git a/content/tables/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv
new file mode 100644
index 0000000..96a4200
--- /dev/null
+++ b/content/tables/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv
@@ -0,0 +1,15 @@
+CRRA,5.865453132577567
+DiscFac,0.5
+time_to_estimate,755.4963464736938
+params,"{'CRRA': 5.865453132577567, 'DiscFac': 0.5}"
+criterion,0.923070680889991
+start_criterion,5.276425253120833
+start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+algorithm,multistart_scipy_neldermead
+direction,minimize
+n_free,2
+message,Maximum number of iterations has been exceeded.
+success,False
+n_criterion_evaluations,268
+n_derivative_evaluations,
+n_iterations,100
diff --git a/content/tables/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv
index 0a6536d..3dea548 100644
--- a/content/tables/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv
+++ b/content/tables/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,5.278040599215181
-DiscFac,0.6460704140717104
-time_to_estimate,463.825003862381
-params,"{'CRRA': 5.278040599215181, 'DiscFac': 0.6460704140717104}"
-criterion,0.9586251907598755
-start_criterion,3.236297547641285
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+CRRA,4.724872715799437
+DiscFac,0.5827256679465178
+time_to_estimate,403.1223566532135
+params,"{'CRRA': 4.724872715799437, 'DiscFac': 0.5827256679465178}"
+criterion,0.9532374383726688
+start_criterion,3.7130193420488258
+start_params,"{'CRRA': 5.278040599215181, 'DiscFac': 0.6460704140717104}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,113
+n_criterion_evaluations,103
 n_derivative_evaluations,
-n_iterations,58
+n_iterations,50
diff --git a/content/tables/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv
index 11ca5b3..16bab14 100644
--- a/content/tables/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv
+++ b/content/tables/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,4.6378134894615615
+CRRA,3.9886633995822067
 DiscFac,0.5
-time_to_estimate,294.00997853279114
-params,"{'CRRA': 4.6378134894615615, 'DiscFac': 0.5}"
-criterion,1.1779964549963657
-start_criterion,1.712202832710532
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+time_to_estimate,284.14713644981384
+params,"{'CRRA': 3.9886633995822067, 'DiscFac': 0.5}"
+criterion,1.0425570066465277
+start_criterion,3.7728432111103603
+start_params,"{'CRRA': 4.6378134894615615, 'DiscFac': 0.5}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,86
+n_criterion_evaluations,91
 n_derivative_evaluations,
-n_iterations,42
+n_iterations,47
diff --git a/content/tables/WarmGlowPortfolio_estimate_results.csv b/content/tables/WarmGlowPortfolio_estimate_results.csv
new file mode 100644
index 0000000..d1b2e02
--- /dev/null
+++ b/content/tables/WarmGlowPortfolio_estimate_results.csv
@@ -0,0 +1,15 @@
+CRRA,4.824889380065167
+DiscFac,0.5
+time_to_estimate,553.7847819328308
+params,"{'CRRA': 4.824889380065167, 'DiscFac': 0.5}"
+criterion,1.7700699649737257
+start_criterion,3.1144499795897445
+start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+algorithm,multistart_scipy_neldermead
+direction,minimize
+n_free,2
+message,Maximum number of iterations has been exceeded.
+success,False
+n_criterion_evaluations,254
+n_derivative_evaluations,
+n_iterations,100
diff --git a/content/tables/WarmGlowSub(Labor)Market_estimate_results.csv b/content/tables/WarmGlowSub(Labor)Market_estimate_results.csv
index 20196c6..f6a6992 100644
--- a/content/tables/WarmGlowSub(Labor)Market_estimate_results.csv
+++ b/content/tables/WarmGlowSub(Labor)Market_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,5.4407958984375
+CRRA,1.1
 DiscFac,1.0
-time_to_estimate,427.5443205833435
-params,"{'CRRA': 5.4407958984375, 'DiscFac': 1.0}"
-criterion,716.0373514721741
-start_criterion,1041.1181541285023
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+time_to_estimate,200.14172959327698
+params,"{'CRRA': 1.1, 'DiscFac': 1.0}"
+criterion,616.3227769557907
+start_criterion,704.2852785327993
+start_params,"{'CRRA': 5.4407958984375, 'DiscFac': 1.0}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,103
+n_criterion_evaluations,33
 n_derivative_evaluations,
-n_iterations,55
+n_iterations,20
diff --git a/content/tables/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv
index e231e8d..7d814d6 100644
--- a/content/tables/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv
+++ b/content/tables/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,6.590661406517029
+CRRA,6.385896487706444
 DiscFac,1.0
-time_to_estimate,607.3060421943665
-params,"{'CRRA': 6.590661406517029, 'DiscFac': 1.0}"
-criterion,781.5156069337606
-start_criterion,1077.2295229251981
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+time_to_estimate,614.8144376277924
+params,"{'CRRA': 6.385896487706444, 'DiscFac': 1.0}"
+criterion,774.1714126356081
+start_criterion,769.1547323613637
+start_params,"{'CRRA': 6.590661406517029, 'DiscFac': 1.0}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
-message,Optimization terminated successfully.
-success,True
-n_criterion_evaluations,180
+message,Maximum number of iterations has been exceeded.
+success,False
+n_criterion_evaluations,208
 n_derivative_evaluations,
-n_iterations,85
+n_iterations,100
diff --git a/content/tables/WarmGlowSub(Stock)Market_estimate_results.csv b/content/tables/WarmGlowSub(Stock)Market_estimate_results.csv
index 8ee9843..ebe13cb 100644
--- a/content/tables/WarmGlowSub(Stock)Market_estimate_results.csv
+++ b/content/tables/WarmGlowSub(Stock)Market_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,4.985237150336616
-DiscFac,0.9934927501861235
-time_to_estimate,433.9389910697937
-params,"{'CRRA': 4.985237150336616, 'DiscFac': 0.9934927501861235}"
-criterion,491.03160837625785
-start_criterion,577.8965828401128
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+CRRA,3.6766995632322557
+DiscFac,1.0
+time_to_estimate,382.6571476459503
+params,"{'CRRA': 3.6766995632322557, 'DiscFac': 1.0}"
+criterion,508.4490874190184
+start_criterion,nan
+start_params,"{'CRRA': 4.985237150336616, 'DiscFac': 0.9934927501861235}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,143
+n_criterion_evaluations,135
 n_derivative_evaluations,
-n_iterations,68
+n_iterations,67
diff --git a/content/tables/WarmGlow_estimate_results.csv b/content/tables/WarmGlow_estimate_results.csv
index 5422471..4f94c93 100644
--- a/content/tables/WarmGlow_estimate_results.csv
+++ b/content/tables/WarmGlow_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,4.297116224347462
-DiscFac,1.0
-time_to_estimate,445.9531614780426
-params,"{'CRRA': 4.297116224347462, 'DiscFac': 1.0}"
-criterion,470.48292496760513
+CRRA,3.2410996113130945
+DiscFac,0.996449988698152
+time_to_estimate,413.6879527568817
+params,"{'CRRA': 3.2410996113130945, 'DiscFac': 0.996449988698152}"
+criterion,482.5715668635871
 start_criterion,nan
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+start_params,"{'CRRA': 4.297116224347462, 'DiscFac': 1.0}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,149
+n_criterion_evaluations,156
 n_derivative_evaluations,
-n_iterations,70
+n_iterations,79
diff --git a/content/tables/WealthPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/WealthPortfolioSub(Labor)Market_estimate_results.csv
index e3db0f8..ea92083 100644
--- a/content/tables/WealthPortfolioSub(Labor)Market_estimate_results.csv
+++ b/content/tables/WealthPortfolioSub(Labor)Market_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,1.2322318491428117
+CRRA,1.1
 DiscFac,0.5
-time_to_estimate,403.5017123222351
-params,"{'CRRA': 1.2322318491428117, 'DiscFac': 0.5}"
-criterion,6.103508613447084
-start_criterion,1.9056233601020711
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+time_to_estimate,222.81176948547363
+params,"{'CRRA': 1.1, 'DiscFac': 0.5}"
+criterion,6.229663982456266
+start_criterion,4.013578484230534
+start_params,"{'CRRA': 1.1, 'DiscFac': 0.5}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,69
+n_criterion_evaluations,27
 n_derivative_evaluations,
-n_iterations,38
+n_iterations,14
diff --git a/content/tables/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv
index c1136b2..622092d 100644
--- a/content/tables/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv
+++ b/content/tables/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv
@@ -1,15 +1,15 @@
-CRRA,1.4163626423462197
+CRRA,1.2462065213787068
 DiscFac,0.5
-time_to_estimate,396.4375936985016
-params,"{'CRRA': 1.4163626423462197, 'DiscFac': 0.5}"
-criterion,5.627150500685743
-start_criterion,2.1017597844874185
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+time_to_estimate,384.6623764038086
+params,"{'CRRA': 1.2462065213787068, 'DiscFac': 0.5}"
+criterion,5.833521811732498
+start_criterion,4.020725417438446
+start_params,"{'CRRA': 1.4163626423462197, 'DiscFac': 0.5}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,66
+n_criterion_evaluations,73
 n_derivative_evaluations,
-n_iterations,34
+n_iterations,36
diff --git a/content/tables/WealthPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/WealthPortfolioSub(Stock)Market_estimate_results.csv
index 927461c..e81c536 100644
--- a/content/tables/WealthPortfolioSub(Stock)Market_estimate_results.csv
+++ b/content/tables/WealthPortfolioSub(Stock)Market_estimate_results.csv
@@ -1,15 +1,15 @@
 CRRA,1.1
 DiscFac,0.5
-time_to_estimate,230.61838388442993
+time_to_estimate,184.4830780029297
 params,"{'CRRA': 1.1, 'DiscFac': 0.5}"
-criterion,6.067050120794328
-start_criterion,1.6900017447749627
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+criterion,6.244341789694212
+start_criterion,4.01918953565245
+start_params,"{'CRRA': 1.1, 'DiscFac': 0.5}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,41
+n_criterion_evaluations,28
 n_derivative_evaluations,
-n_iterations,22
+n_iterations,14
diff --git a/content/tables/WealthPortfolio_estimate_results.csv b/content/tables/WealthPortfolio_estimate_results.csv
index 910bc05..a8fc9c6 100644
--- a/content/tables/WealthPortfolio_estimate_results.csv
+++ b/content/tables/WealthPortfolio_estimate_results.csv
@@ -1,15 +1,15 @@
 CRRA,1.1
 DiscFac,0.5
-time_to_estimate,239.4185471534729
+time_to_estimate,183.13626527786255
 params,"{'CRRA': 1.1, 'DiscFac': 0.5}"
-criterion,6.5458132852367
-start_criterion,1.618676051720708
-start_params,"{'CRRA': 5.0, 'DiscFac': 0.95}"
+criterion,6.668610211284288
+start_criterion,4.00846573530044
+start_params,"{'CRRA': 1.1, 'DiscFac': 0.5}"
 algorithm,multistart_scipy_neldermead
 direction,minimize
 n_free,2
 message,Optimization terminated successfully.
 success,True
-n_criterion_evaluations,42
+n_criterion_evaluations,28
 n_derivative_evaluations,
-n_iterations,22
+n_iterations,14
diff --git a/content/tables/msm/Portfolio_estimate_results.csv b/content/tables/msm/Portfolio_estimate_results.csv
new file mode 100644
index 0000000..10bd388
--- /dev/null
+++ b/content/tables/msm/Portfolio_estimate_results.csv
@@ -0,0 +1,3 @@
+CRRA,7.988151380338656
+DiscFac,0.7012415354852454
+time_to_estimate,247.5582902431488