Note: Extended Information (like abstracts, doi, url's etc.) can be found in quant-econ-extendedinfo.bib file in _static/

###

@book{Brunton_Kutz_2019,

place={Cambridge},

title={Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control},

publisher={Cambridge University Press},

author={Brunton, Steven L. and Kutz, J. Nathan},

year={2019}

}

@article{wallis1980statistical,

title={The statistical research group, 1942--1945},

author={Wallis, W Allen},

To conduct simulations, we bring in these imports, as in {doc}`A First Look at the Kalman filter <kalman>`.

```{code-cell} ipython3

%matplotlib inline

import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = (11, 5) #set default figure size

import numpy as np

from quantecon import Kalman, LinearStateSpace

from collections import namedtuple

from scipy.stats import multivariate_normal

import matplotlib as mpl

mpl.rcParams['text.usetex'] = True

mpl.rcParams['text.latex.preamble'] = r'\usepackage{{amsmath}}'

```

Dynamic mode decomposition was introduced by {cite}`schmid2010`,

You can read about Dynamic Mode Decomposition here {cite}`DMD_book` and here [[BK19](https://python.quantecon.org/zreferences.html#id25)] (section 7.2).

You can read about Dynamic Mode Decomposition {cite}`DMD_book` and {cite}`Brunton_Kutz_2019` (section 7.2).

**Dynamic Mode Decomposition** (DMD) computes a rank $ r < p $ approximation to the least squares regression coefficients $ \hat A $ described by formula {eq}`eq:AhatSVDformula`.