Free online course
==================

Users of the pyStoNED package may benefit of the free online course "Productivity and Efficiency Analysis"(30E00300) 
organized at Aalto University of Business, Spring 2020, including video lessons, 
empirical examples and supporting materials. The course materials are available on the `YouTube channel <https://youtube.com/playlist?list=PL1V6V9R5sTmkYINZ-fNGJPrhN4806rje_>`_.

Table of contents:

- `Lesson 1a <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_1a_motivation.pdf>`_: What is productivity and why it matters?
- `Lesson 1b <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_1b_taxonomy.pdf>`_: Taxonomy of frontier estimation methods
- `Lesson 1c <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_1c_application.pdf>`_: Productivity analysis in action: Incentive regulation of electricity distribution networks

- `Lesson 2a <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_2a_Application.pdf>`_: Application of DEA
- `Lesson 2b <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_2b_DEAformulations.pdf>`_: DEA formulations
- `Lesson 2c <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_2c_Production_theory.pdf>`_: Production theory
- `Lesson 2d <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_2d_DEAstatistics.pdf>`_: Statistical approach to DEA
- `Lesson 2e* <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_2e_prehistory.pdf>`_: Pre-history of DEA in economics

- `Lesson 3a <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_3a_parametric_approach.pdf>`_: Parametric approach
- `Lesson 3b <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_3b_SFA_basics.pdf>`_: Basics of SFA
- `Lesson 3c <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_3c_SFA_Application.pdf>`_: Application of SFA

- `Lesson 4a <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_4a_StoNED_Application.pdf>`_: Application of StoNED
- `Lesson 4b <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_4b_StoNED_procedure.pdf>`_: StoNED estimation
- `Lesson 4c <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_4c_CNLS.pdf>`_: Convex regression
- `Lesson 4d <https://www.youtube.com/watch?v=NSSdUuFO5EU&list=PL1V6V9R5sTmkYINZ-fNGJPrhN4806rje_&index=16&ab_channel=TimoKuosmanen>`_: Decomposing the residual
- `Lesson 4e* <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_4e_Quantiles.pdf>`_: Quantiles and expectiles

- `Lesson 5a <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_5a_WhatZ.pdf>`_: What are z-variables and where to put them?
- `Lesson 5b <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_5b_SFAZ.pdf>`_: SFA modeling of z-variables
- `Lesson 5c <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_5c_DEAZ.pdf>`_: Contextual variables in DEA
- `Lesson 5d <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_5d_StoNEZD.pdf>`_: Semi-nonparametric approach

- `Lesson 6a <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_6a_synergies.pdf>`_: Synergies of joint production
- `Lesson 6b <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_6b_distancefunctions.pdf>`_: Distance functions and DEA
- `Lesson 6c <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_6c_badoutputs.pdf>`_: Bad outputs
- `Lesson 6d <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_6d_ParametricD.pdf>`_: Parametric distance functions
- `Lesson 6e <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_6e_StoNEDDDF.pdf>`_: StoNED with multiple outputs

- `Lesson 7a <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_7a_prod_growth.pdf>`_: Growth accounting and index numbers
- `Lesson 7b <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_7b_Malmquist.pdf>`_: Malmquist index
- `Lesson 7c <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_7c_Computation.pdf>`_: Application of Malmquist index
- `Lesson 7d <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_7d_GreenGrowth.pdf>`_: Case: Green TFP growth

- `Lesson 8a <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_8a_StructuralChange.pdf>`_: Entry & exit
- `Lesson 8b <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_8b_Shareweights.pdf>`_: Share weights
- `Lesson 8c <https://github.com/ds2010/pyStoNED/raw/master/notebooks/slides/PROD_8c_Application.pdf>`_: Application to Finnish agriculture