Comments (3)
jeffgortmaker
commented on August 10, 2024
1
I think this should be possible with a Simulation class initialized with
- your changed product characteristics,
- the same agent data and formulations as your original problem,
- your estimated parameters, and
- your estimated structural error terms. If you only estimated a demand side, I think this should still work if you set
omegaequal to estimated marginal costs andgammaequal to zero.
You could then solve for equilibrium prices with Simulation.solve, which uses the same contraction as ProblemResults.compute_prices.
Can you let me know if this makes sense/works? I should describe how to do this in the post-estimation tutorial and think about how to make it more straightforward.
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amandeep0
commented on August 10, 2024
1
Thanks a lot for your prompt reply. I was working on adding support for
this in your library, but this sounds way better. Btw thanks a lot for
writing this library. It is going to help a lot of researchers. I’ll let
you know if I run into any issues.
On Wed, May 1, 2019 at 6:40 PM Jeff Gortmaker ***@***.***> wrote:
I think this should be possible with a Simulation
<https://pyblp.readthedocs.io/en/stable/_api/pyblp.Simulation.html> class
initialized with
- your changed product characteristics,
- the same agent data and formulations as your original problem,
- your estimated parameters, and
- your estimated structural error terms. If you only estimated a
demand side, I think this should still work if you set omega equal to
estimated marginal costs and gamma equal to zero.
You could then solve for equilibrium prices with Simulation.solve, which
uses the same contraction as ProblemResults.compute_prices.
Can you let me know if this makes sense/works? I should describe how to do
this in the post-estimation tutorial and think about how to make it more
straightforward.
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jeffgortmaker
commented on August 10, 2024
I've improved the flexibility of Simulation with the goal of making more complicated counterfactuals like this easier in the next stable version. I've also added some notes in the docs explaining this, so I'm closing this issue for now.
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Related Issues (20)
- How to construct a separate pair of micro moments for each market HOT 4
- X1 formulation with only fixed effects HOT 2
- Memory issues with pyblp.differentiation_instruments HOT 4
- Demand Jacobian, markup term and first order conditions HOT 2
- Micromoments for choices within individuals HOT 10
- Boostrap the change in consumer surplus under a counterfactual scenario HOT 3
- Question: When using pyblp.parallel() to run problem.solve(), How can I make sure that each partition has enough RAM? HOT 9
- questions on price elasticity and convergence HOT 2
- Computing counterfactual shares HOT 2
- questions about different markets have different numbers of products HOT 1
- Questions on how to run simulation with different product attributes to different agents HOT 10
- Question - Is there a way to incorporate FEs in Simulation? HOT 4
- Supply-side moments for subset of markets HOT 3
- Trouble with importing PyBLP HOT 2
- ProblemResults question HOT 6
- Coding base level in categorical variables in pyblp HOT 2
- divide by zero error in "ProblemResults.compute_consumer_surpluses(eliminate_product_ids)" HOT 7
- ValueError ("The detected shape was (3,) + inhomogeneous part.") when running simulation.ipynb from the tutorial HOT 2
- Problem with including log-normally distributed random coefficients on price HOT 4
- Question: Most Efficient Way of Producing LaTeX Tables of PyBLP Results and Estimates HOT 4
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