Merge pull request econ-ark#891 from sbenthall/i637

adds constructor for LogNormal distribution using mean and std

Documentation/CHANGELOG.md

@@ -14,8 +14,11 @@ Release Data: TBD
 
 #### Major Changes
 
+* Adds a constructor for LogNormal distributions from mean and standard deviation (#891)[https://github.com/econ-ark/HARK/pull/891/]
+* Uses new LogNormal constructor in ConsPortfolioModel (#891)[https://github.com/econ-ark/HARK/pull/891/]
 * calcExpectations method for taking the expectation of a distribution over a function (#884)[https://github.com/econ-ark/HARK/pull/884/]
 
+
 #### Minor Changes
 
 * Remove uses of deprecated time.clock [#887](https://github.com/econ-ark/HARK/pull/887)

HARK/ConsumptionSaving/ConsPortfolioModel.py

@@ -241,24 +241,23 @@ def updateRiskyDstn(self):
         # Generate a discrete approximation to the risky return distribution if the
         # agent has age-varying beliefs about the risky asset
         if "RiskyAvg" in self.time_vary:
-            RiskyDstn = []
+            self.RiskyDstn = []
             for t in range(self.T_cycle):
-                RiskyAvgSqrd = self.RiskyAvg[t] ** 2
-                RiskyVar = self.RiskyStd[t] ** 2
-                mu = np.log(self.RiskyAvg[t] / (np.sqrt(1.0 + RiskyVar / RiskyAvgSqrd)))
-                sigma = np.sqrt(np.log(1.0 + RiskyVar / RiskyAvgSqrd))
-                RiskyDstn.append(Lognormal(mu=mu, sigma=sigma).approx(self.RiskyCount))
-            self.RiskyDstn = RiskyDstn
+                self.RiskyDstn.append(
+                    Lognormal.from_mean_std(
+                        self.RiskyAvg[t],
+                        self.RiskyStd[t]
+                    ).approx(self.RiskyCount)
+                )
             self.addToTimeVary("RiskyDstn")
 
         # Generate a discrete approximation to the risky return distribution if the
         # agent does *not* have age-varying beliefs about the risky asset (base case)
         else:
-            RiskyAvgSqrd = self.RiskyAvg ** 2
-            RiskyVar = self.RiskyStd ** 2
-            mu = np.log(self.RiskyAvg / (np.sqrt(1.0 + RiskyVar / RiskyAvgSqrd)))
-            sigma = np.sqrt(np.log(1.0 + RiskyVar / RiskyAvgSqrd))
-            self.RiskyDstn = Lognormal(mu=mu, sigma=sigma).approx(self.RiskyCount)
+            self.RiskyDstn = Lognormal.from_mean_std(
+                self.RiskyAvg,
+                self.RiskyStd,
+            ).approx(self.RiskyCount)
             self.addToTimeInv("RiskyDstn")
 
     def updateShockDstn(self):

HARK/distribution.py

@@ -213,6 +213,39 @@ def approx(self, N, tail_N=0, tail_bound=None, tail_order=np.e):
             pmf, X, seed=self.RNG.randint(0, 2 ** 31 - 1, dtype="int32")
         )
 
+    @classmethod
+    def from_mean_std(cls, mean, std, seed = 0):
+        """
+        Construct a LogNormal distribution from its
+        mean and standard deviation.
+
+        This is unlike the normal constructor for the distribution,
+        which takes the mu and sigma for the normal distribution
+        that is the logarithm of the Log Normal distribution.
+
+        Parameters
+        ----------
+        mean : float or [float]
+            One or more means.  Number of elements T in mu determines number
+            of rows of output.
+        std : float or [float]
+            One or more standard deviations. Number of elements T in sigma
+            determines number of rows of output.
+        seed : int
+            Seed for random number generator.
+
+        Returns
+        ---------
+        LogNormal
+ 
+        """
+        mean_squared = mean ** 2
+        variance = std ** 2
+        mu = np.log(mean / (np.sqrt(1.0 + variance / mean_squared)))
+        sigma = np.sqrt(np.log(1.0 + variance / mean_squared))
+
+        return cls(mu = mu, sigma = sigma, seed = seed)
+
 
 class MeanOneLogNormal(Lognormal):
     def __init__(self, sigma=1.0, seed=0):
@@ -440,7 +473,6 @@ def approx(self, N):
             pmf, X, seed=self.RNG.randint(0, 2 ** 31 - 1, dtype="int32")
         )
 
-
 ### DISCRETE DISTRIBUTIONS