Merge pull request #9 from QuantEcon/author-year

MAINT: update bibtex to author-year

Author: mmcky

lectures/_config.yml

@@ -34,6 +34,7 @@ latex:
 sphinx:
   extra_extensions: [sphinx_multitoc_numbering, sphinxext.rediraffe, sphinx_tojupyter, sphinxcontrib.youtube, sphinx.ext.todo, sphinx_exercise, sphinx_togglebutton, sphinx.ext.intersphinx, sphinx_reredirects]
   config:
+    bibtex_reference_style: author_year
     nb_mime_priority_overrides: [
        # HTML
        ['html', 'application/vnd.jupyter.widget-view+json', 10],

lectures/_static/quant-econ.bib

@@ -2717,3 +2717,11 @@ @article{stachurski2019impossibility
     year = "2019",
     publisher = "Elsevier"
 }
+
+@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}
+}

lectures/var_dmd.md

@@ -260,23 +260,18 @@ $$ (eq:AhatSVDformula)
 
 ## Dynamic Mode Decomposition (DMD)
 
-
-
 We turn to the $ m >>n $ **tall and skinny** case  associated with **Dynamic Mode Decomposition**.
 
 Here an $ m \times n+1 $  data matrix $ \tilde X $ contains many more attributes (or variables) $ m $ than time periods  $ n+1 $.
 
-
 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 here {cite}`DMD_book` and here {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`.
 
-  
 We'll  build up gradually  to a formulation that is useful  in applications.
 
-
 We'll do this by describing three  alternative representations of our first-order linear dynamic system, i.e., our vector autoregression. 
 
 **Guide to three representations:** In practice, we'll mainly be interested in Representation 3. 
@@ -289,7 +284,6 @@ We use  such a small subset of DMD modes to  construct a reduced-rank approximat
 
 To do that, we'll want to use the  **reduced**  SVD's affiliated with representation 3, not the **full** SVD's affiliated with representations 1 and 2. 
 
-
 **Guide to impatient reader:** In our applications, we'll be using Representation 3. 
 
 You might want to skip the stage-setting representations 1 and 2 on first reading.