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A critical error that I see many grad students make: they try to estimate Frankenstein's model. Rather than viewing a model as answering a research question, they view a model as an arbitrary hodgepodge of models they learned about in their classes.
Models answer questions. There is no such thing as a "good model of a hospital" or a "good model of small firms". There are models which concisely and quantitatively capture the trade-off that you are intuitively trying to capture.
PhD students will often tell me about the project they have been working on for years where they estimate a model combining X, Y and Z, and when I ask what they want to do with the model they say, "That's what I was hoping you could help me out with."
But this is the wrong way of writing a paper! You don't estimate a complicated model and then tack on a motivation at the end. You start with a motivation that people care about and you estimate a model to answer it.
The research question disciplines the content of the model. Without the research question, you're not doing research, you're just pasting together ideas you've seen before which may or may not be suitable for answering any question of interest.
So takeaways:
1) Don't be like Frankenstein when you write down models
2) It's okay to be like Frankenstein in striving for a godlike mastery of nature. This pursuit gets an unfairly bad reputation in literature.
1) Don't be like Frankenstein when you write down models
2) It's okay to be like Frankenstein in striving for a godlike mastery of nature. This pursuit gets an unfairly bad reputation in literature.
