trying to resolve merge conflicts

Author: NathanielF

docs/source/glossary.rst

@@ -78,89 +78,6 @@ Glossary
       A notation for describing statistical models :footcite:p:`wilkinson1973symbolic`.
 
 
-References
-----------
-.. footbibliography::
-Glossary
-========
-
-.. glossary::
-   :sorted:
-
-   ANCOVA
-      Analysis of covariance is a simple linear model, typically with one continuous predictor (the covariate) and a catgeorical variable (which may correspond to treatment or control group). In the context of this package, ANCOVA could be useful in pre-post treatment designs, either with or without random assignment. This is similar to the approach of difference in differences, but only applicable with a single pre and post treatment measure.
-
-   Average treatment effect
-   ATE
-      The average treatement effect across all units.
-
-   Average treatment effect on the treated
-   ATT
-      The average effect of the treatment on the units that recieved it. Also called Treatment on the treated.
-
-   Change score analysis
-      A statistical procedure where the outcome variable is the difference between the posttest and protest scores.
-
-   Comparative interrupted time-series
-   CITS
-      An interrupted time series design with added comparison time series observations.
-
-   Confound
-      Anything besides the treatment which varies across the treatment and control conditions.
-
-   Counterfactual
-      A hypothetical outcome that could or will occur under specific hypothetical circumstances.
-
-   Difference in differences
-   DiD
-      Analysis where the treatment effect is estimated as a difference between treatment conditions in the differences between pre-treatment to post treatment observations.
-
-   Interrupted time series design
-   ITS
-      A quasi-experimental design to estimate a treatment effect where a series of observations are collected before and after a treatment. No control group is present.
-
-   Non-equivalent group designs
-   NEGD
-      A quasi-experimental design where units are assigned to conditions non-randomly, and not according to a running variable (see Regression discontinuity design).
-
-   One-group posttest-only design
-      A design where a single group is exposed to a treatment and assessed on an outcome measure. There is no pretest measure or comparison group.
-
-   Panel data
-      Time series data collected on multiple units where the same units are observed at each time point.
-
-   Pretest-posttest design
-      A quasi-experimental design where the treatment effect is estimated by comparing an outcome measure before and after treatment.
-
-   Quasi-experiment
-      An empirical comparison used to estimate the effects of a treatment where units are not assigned to conditions at random.
-
-   Random assignment
-      Where units are assigned to conditions at random.
-
-   Randomized experiment
-      An emprical comparison used to estimate the effects of treatments where units are assigned to treatment conditions randomly.
-
-   Regression discontinuity design
-      A quasi–experimental comparison to estimate a treatment effect where units are assigned to treatment conditions based on a cut-off score on a quantitative assignment variable (aka running variable).
-
-   Sharp regression discontinuity design
-      A Regression discontinuity design where allocation to treatment or control is determined by a sharp threshold / step function.
-
-   Synthetic control
-      The synthetic control method is a statistical method used to evaluate the effect of an intervention in comparative case studies. It involves the construction of a weighted combination of groups used as controls, to which the treatment group is compared.
-
-   Treatment on the treated effect
-   TOT
-      The average effect of the treatment on the units that recieved it. Also called the average treatment effect on the treated (ATT).
-
-   Treatment effect
-      The difference in outcomes between what happened after a treatment is implemented and what would have happened (see Counterfactual) if the treatment had not been implemented, assuming everything else had been the same.
-
-   Wilkinson notation
-      A notation for describing statistical models :footcite:p:`wilkinson1973symbolic`.
-
-
 References
 ----------
 .. footbibliography::

docs/source/notebooks/did_pymc_banks.ipynb

@@ -7,11 +7,7 @@
    "source": [
     "# Banking dataset with a `pymc` model\n",
     "\n",
-    ":::{note}\n",
-    "This example is in-progress! Further elaboration and explanation will follow soon.\n",
-    ":::\n",
-    "\n",
-    "This notebook analyses historic data on banking closures from [Richardson & Troost (2009)](http://masteringmetrics.com/wp-content/uploads/2015/02/Richardson_Troost_2009_JPE.pdf) and used as a case study for a difference in differences analysis in the [Mastering Metrics](http://www.masteringmetrics.com) book. Here, we replicate this analysis, but using Bayesian inference."
+    "This notebook analyses historic data on banking closures from {cite:t}`richardson2009monetary` and used as a case study for a difference in differences analysis in the excellent book [Mastering Metrics](http://www.masteringmetrics.com) {cite:p}`angrist2014mastering`. Here, we replicate this analysis, but using Bayesian inference."
    ]
   },
   {
@@ -539,25 +535,6 @@
     "The only change here compared to the classic 2$\\times$2 DiD model above is the addition of a main effect of `year`. Because this is coded numerically (not categorically) this can capture any linear changes in the outcome variable over time."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will use the formula: `bib ~ 1 + year + district*post_treatment` which equates to the following model of expected values:\n",
-    "\n",
-    "$$\n",
-    "\\begin{aligned}\n",
-    "\\mu_i & = \\beta_0 \\\\\n",
-    " & \\quad + \\beta_{y} \\cdot year_i \\\\\n",
-    " & \\quad + \\beta_{d} \\cdot district_i \\\\\n",
-    " & \\quad + \\beta_{p} \\cdot post~treatment_i \\\\\n",
-    " & \\quad + \\beta_{\\Delta} \\cdot district_i \\cdot post~treatment_i\n",
-    "\\end{aligned}\n",
-    "$$\n",
-    "\n",
-    "The only change here compared to the classic 2$\\times$2 DiD model above is the addition of a main effect of `year`. Because this is coded numerically (not categorically) this can capture any linear changes in the outcome variable over time."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 10,

docs/source/references.bib

@@ -41,37 +41,6 @@ @article{carpenter2009effect
   publisher={American Economic Association}
 }
 
-@book{hansenEconometrics,
-  title={Econometrics},
-  author={Hansen, Bruce E},
-  year={2022},
-  publisher={Princeton}
-}
-@online{brexit2022policybrief,
-year={2022},
-title={What can we know about the cost of Brexit so far?},
-url={https://www.cer.eu/publications/archive/policy-brief/2022/cost-brexit-so-far},
-author={Springford, John}
-}
-
-@book{reichardt2019quasi,
-  title={Quasi-experimentation: A guide to design and analysis},
-  author={Reichardt, Charles S},
-  year={2019},
-  publisher={Guilford Publications}
-}
-
-@article{wilkinson1973symbolic,
-  title={Symbolic description of factorial models for analysis of variance},
-  author={Wilkinson, GN and Rogers, CE},
-  journal={Journal of the Royal Statistical Society Series C: Applied Statistics},
-  volume={22},
-  number={3},
-  pages={392--399},
-  year={1973},
-  publisher={Oxford University Press}
-}
-
 @article{wilkinson1973symbolic,
   title={Symbolic description of factorial models for analysis of variance},
   author={Wilkinson, GN and Rogers, CE},