Lecture 12: Starting with CLT for Extremum Estimators from Lecture 11 Flashcards
State the CLT for extrememum estimators along with the 6 conditions
Which condition is particularly important for the CLT for extremum estimators? Illustrate why.
State and prove the CLT for extrememum estimators along with the 6 conditions.
State again, why is condition 1 particularly important?
is the condition regarding twice differentiability particularly important?
Set up the classic LSE example for the case where the true value of the parameter Beta lies in the boundary.
Without relying on the usual LSE, state the condition which we need to show to prove consistency.
Set up the condition needed to prove consistency in this example, and prove it. What can you say about pointwise vs uniform consistency here?
Formally set up the problem of what we want to show to establish rate of convergence.
Verbally described the motivation and methodology for the peeling technique.
Formally set up the problem of what we want to show to establish rate of convergence - and show it.
Also aslinda ilki elemani degildir
Can you establish a stricter bound for the rate of convergence? Explain verbally.
Formally summarize, by transforming the parameter space, how we can write the beta s.
Set up the expression we end up considering, and defend why we use this expression. Then derive gamma based on this expression.
What do we consider when trying to understand where gamma converges to? Show in general and for q=1.
Describe in words the concept we use to move from finite sample distributions to whole or joint distribution, and state a sufficient condition for this concept.
Re-state the gamma function in our context, and show that it converges.
Conclude the proof of the extremum estimator starting from the last point we have: that gamma n converges.
