Useful Libraries¶

Overview¶

While Julia lacks the massive scientific ecosystem of Python, it has successfully attracted a small army of enthusiastic and talented developers

As a result, its package system is moving towards a critical mass of useful, well written libraries

In addition, a major advantage of Julia libraries is that, because Julia itself is sufficiently fast, there is less need to mix in low level languages like C and Fortran

As a result, most Julia libraries are written exclusively in Julia

Not only does this make the libraries more portable, it makes them much easier to dive into, read, learn from and modify

In this lecture we introduce a few of the Julia libraries that we’ve found particularly useful for quantitative work in economics

Credits: Thanks to @cc7768, @vgregory757 and @spencerlyon2 for keeping us up to date with current best practice

Distributions¶

Functions for manipulating probability distributions and generating random variables are supplied by the excellent Distributions.jl package

We’ll restrict ourselves to a few simple examples (the package itself has detailed documentation)

• d = Normal(m, s) creates a normal distribution with mean \(m\) and standard deviation \(s\)

  • defaults are m = 0 and s = 1

• d = Uniform(a, b) creates a uniform distribution on interval \([a, b]\)

  • defaults are a = 0 and b = 1

• d = Binomial(n, p) creates a binomial over \(n\) trials with success probability \(p\)

• defaults are n = 1 and p = 0.5

Distributions.jl defines various methods for acting on these instances in order to obtain

• random draws
• evaluations of pdfs (densities), cdfs (distribution functions), quantiles, etc.
• mean, variance, kurtosis, etc.

For example,

• To generate k draws from the instance d use rand(d, k)
• To obtain the mean of the distribution use mean(d)
• To evaluate the probability density function of d at x use pdf(d, x)

Further details on the interface can be found here

Several multivariate distributions are also implemented

Working with Data¶

A useful package for working with data is DataFrames

The most important data type provided is a DataFrame, a two dimensional array for storing heterogeneous data

Although data can be heterogeneous within a DataFrame, the contents of the columns must be homogeneous

This is analogous to a data.frame in R, a DataFrame in Pandas (Python) or, more loosely, a spreadsheet in Excel

The DataFrames package also supplies a DataArray type, which is like a one dimensional DataFrame

In terms of working with data, the advantage of a DataArray over a standard numerical array is that it can handle missing values

Here’s an example

using DataFrames

commodities = ["crude", "gas", "gold", "silver"]

4-element Array{String,1}:
 "crude"
 "gas"
 "gold"
 "silver"

last_price = @data([4.2, 11.3, 12.1, NA])  # Create DataArray

4-element DataArrays.DataArray{Float64,1}:
  4.2
 11.3
 12.1
   NA

df = DataFrame(commod=commodities, price=last_price)

4×2 DataFrames.DataFrame
│ Row │ commod   │ price │
├─────┼──────────┼───────┤
│ 1   │ "crude"  │ 4.2   │
│ 2   │ "gas"    │ 11.3  │
│ 3   │ "gold"   │ 12.1  │
│ 4   │ "silver" │ NA    │

Columns of the DataFrame can be accessed by name

df[:price]

4-element DataArrays.DataArray{Float64,1}:
  4.2
 11.3
 12.1
   NA

df[:commod]

4-element DataArrays.DataArray{String,1}:
 "crude"
 "gas"
 "gold"
 "silver"

The DataFrames package provides a number of methods for acting on DataFrames

A simple one is describe()

describe(df)

commod
Length  4
Type    String
NAs     0
NA%     0.0%
Unique  4

price
Min      4.2
1st Qu.  7.75
Median   11.3
Mean     9.200000000000001
3rd Qu.  11.7
Max      12.1
NAs      1
NA%      25.0%

There are also functions for splitting, merging and other data munging operations

Data can be read from and written to CSV files using syntax df = readtable("data_file.csv") and writetable("data_file.csv", df) respectively

Other packages for working with data can be found at JuliaStats and JuliaQuant

Interpolation¶

In economics we often wish to interpolate discrete data (i.e., build continuous functions that join discrete sequences of points)

We also need such representations to be fast and efficient

The package we usually turn to for this purpose is Interpolations.jl

One downside of Interpolations.jl is that the code to set up simple interpolation objects is relatively verbose

The upside is that the routines have excellent performance

The package is also well written and well maintained

Another alternative, if using univariate linear interpolation, is LinInterp from QuantEcon.jl

As we show below, the syntax for this function is much simpler

Univariate Interpolation¶

Let’s start with the univariate case

We begin by creating some data points, using a sine function

using Interpolations
using Plots
plotlyjs()

x = -7:7    # x points, coase grid
y = sin.(x)  # corresponding y points

xf = -7:0.1:7  # fine grid
plot(xf, sin.(xf), label="sine function")
scatter!(x, y, label="sampled data", markersize=4)

Here’s the resulting figure

We can implement linear interpolation easily using QuantEcon’s LinInterp

using QuantEcon

li = LinInterp(x, y)        # create LinInterp object
li(0.3)                     # evaluate at a single point
y_linear_qe = li.(xf)       # evaluate at multiple points

plot(xf, y_linear_qe, label="linear")
scatter!(x, y, label="sampled data", markersize=4)

Here’s the result

The syntax is simple and the code is efficient, but for other forms of interpolation we need a more sophisticated set of routines

As an example, let’s employ Interpolations.jl to interpolate the sampled data points using piecewise constant, piecewise linear and cubic interpolation

itp_const = scale(interpolate(y, BSpline(Constant()), OnGrid()), x)
itp_linear = scale(interpolate(y, BSpline(Linear()), OnGrid()), x)
itp_cubic = scale(interpolate(y, BSpline(Cubic(Line())), OnGrid()), x)

When we want to evaluate them at points in their domain (i.e., between min(x) and max(x)) we can do so as follows

itp_cubic[0.3]

0.29400097760820687

Note the use of square brackets, rather than parentheses!

Let’s plot these objects created above

xf = -7:0.1:7
y_const = [itp_const[x] for x in xf]
y_linear = [itp_linear[x] for x in xf]
y_cubic = [itp_cubic[x] for x in xf]

plot(xf, [y_const y_linear y_cubic], label=["constant" "linear" "cubic"])
scatter!(x, y, label="sampled data", markersize=4)

Here’s the figure we obtain

Univariate with Irregular Grid¶

The LinInterp from QuantEcon.jl works the same whether or not the grid is regular (i.e., evenly spaced)

The Interpolations.jl code is a bit more picky

Here’s an example of the latter with an irregular grid

using Interpolations
using Plots
plotlyjs()

x = log.(linspace(1, exp(4), 10)) + 1  # Uneven grid
y = log.(x)                            # Corresponding y points

itp_const = interpolate((x, ), y, Gridded(Constant()))
itp_linear = interpolate((x, ), y, Gridded(Linear()))

xf = log.(linspace(1, exp(4), 100)) + 1
y_const = [itp_const[x] for x in xf]
y_linear = [itp_linear[x] for x in xf]
y_true = [log(x) for x in xf]

labels = ["piecewise constant" "linear" "true function"]
plot(xf, [y_const y_linear y_true], label=labels)
scatter!(x, y, label="sampled data", markersize=4, size=(800, 400))

The figure looks as follows

Multivariate Interpolation¶

We can also interpolate in higher dimensions

The following example gives one illustration

n = 5
x = linspace(-3, 3, n)
y = copy(x)

z = Array{Float64}(n, n)
f(x, y) = cos(x^2 + y^2) / (1 + x^2 + y^2)
for i in 1:n
    for j in 1:n
        z[j, i] = f(x[i], y[j])
    end
end

itp = interpolate((x, y), z, Gridded(Linear()));

nf = 50
xf = linspace(-3, 3, nf)
yf = copy(xf)

zf = Array{Float64}(nf, nf)
ztrue = Array{Float64}(nf, nf)
for i in 1:nf
    for j in 1:nf
        zf[j, i] = itp[xf[i], yf[j]]
        ztrue[j, i] = f(xf[i], yf[j])
    end
end

grid = gridmake(x, y)
z = reshape(z, n*n, 1)

pyplot()
surface(xf, yf, zf, color=:greens, falpha=0.7, cbar=false)
surface!(xf, yf, ztrue, fcolor=:blues, falpha=0.25, cbar=false)
scatter!(grid[:, 1], grid[:, 2], vec(z), legend=:none, color=:black, markersize=4)

This code produces the following figure

The original function is in blue, while the linear interpolant is shown in green

Optimization, Roots and Fixed Points¶

Let’s look briefly at the optimization and root finding algorithms

Roots¶

A root of a real function \(f\) on \([a,b]\) is an \(x \in [a, b]\) such that \(f(x)=0\)

For example, if we plot the function

(1)¶\[f(x) = \sin(4 (x - 1/4)) + x + x^{20} - 1\]

with \(x \in [0,1]\) we get

The unique root is approximately 0.408

One common root-finding algorithm is the Newton-Raphson method

This is implemented as newton() in the Roots package and is called with the function and an initial guess

using Roots

f(x) = sin(4 * (x - 1/4)) + x + x^20 - 1

newton(f, 0.2)

0.40829350427936706

The Newton-Raphson method uses local slope information, which can lead to failure of convergence for some initial conditions

newton(f, 0.7)

-1.0022469256696986

For this reason most modern solvers use more robust “hybrid methods”, as does Roots’s fzero() function

fzero(f, 0, 1)

0.40829350427936706

using Optim

optimize(x -> x^2, -1.0, 1.0)

Results of Optimization Algorithm
 * Algorithm: Brent's Method
 * Search Interval: [-1.000000, 1.000000]
 * Minimizer: -2.775558e-17
 * Minimum: 7.703720e-34
 * Iterations: 5
 * Convergence: max(|x - x_upper|, |x - x_lower|) <= 2*(1.5e-08*|x|+2.2e-16): true
 * Objective Function Calls: 6

Others Topics¶

import QuadGK.quadgk
quadgk(x -> cos(x), -2pi, 2pi)

(-1.5474478810961125e-14,5.7846097329025695e-24)

using QuantEcon
nodes, weights = qnwlege(65, -2pi, 2pi);
integral = do_quad(x -> cos.(x), nodes, weights)

-3.0062757838678067e-15

@time quadgk(x -> cos.(x), -2pi, 2pi)
@time do_quad(x -> cos.(x), nodes, weights)

0.800730 seconds (591.95 k allocations: 16.933 MB)
0.008481 seconds (573 allocations: 33.076 KB)

Further Reading¶

The full set of libraries available under the Julia packaging system can be browsed at pkg.julialang.org