2,528 research outputs found
Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection
We consider two recent suggestions for how to perform an empirically
motivated Monte Carlo study to help select a treatment effect estimator under
unconfoundedness. We show theoretically that neither is likely to be
informative except under restrictive conditions that are unlikely to be
satisfied in many contexts. To test empirical relevance, we also apply the
approaches to a real-world setting where estimator performance is known. Both
approaches are worse than random at selecting estimators which minimise
absolute bias. They are better when selecting estimators that minimise mean
squared error. However, using a simple bootstrap is at least as good and often
better. For now researchers would be best advised to use a range of estimators
and compare estimates for robustness
Econometrics for Learning Agents
The main goal of this paper is to develop a theory of inference of player
valuations from observed data in the generalized second price auction without
relying on the Nash equilibrium assumption. Existing work in Economics on
inferring agent values from data relies on the assumption that all participant
strategies are best responses of the observed play of other players, i.e. they
constitute a Nash equilibrium. In this paper, we show how to perform inference
relying on a weaker assumption instead: assuming that players are using some
form of no-regret learning. Learning outcomes emerged in recent years as an
attractive alternative to Nash equilibrium in analyzing game outcomes, modeling
players who haven't reached a stable equilibrium, but rather use algorithmic
learning, aiming to learn the best way to play from previous observations. In
this paper we show how to infer values of players who use algorithmic learning
strategies. Such inference is an important first step before we move to testing
any learning theoretic behavioral model on auction data. We apply our
techniques to a dataset from Microsoft's sponsored search ad auction system
Simple versus optimal rules as guides to policy
This paper contributes to the policy evaluation literature by developing new strategies to study alternative policy rules. We compare optimal rules to simple rules within canonical monetary policy models. In our context, an optimal rule represents the solution to an intertemporal optimization problem in which a loss function for the policymaker and an explicit model of the macroeconomy are specified. We define a simple rule to be a summary of the intuition policymakers and economists have about how a central bank should react to aggregate disturbances. The policy rules are evaluated under minimax and minimax regret criteria. These criteria force the policymaker to guard against a worst-case scenario, but in different ways. Minimax makes the worst possible model the benchmark for the policymaker, while minimax regret confronts the policymaker with uncertainty about the true model. Our results indicate that the case for a model-specific optimal rule can break down when uncertainty exists about which of several models is true. Further, we show that the assumption that the policymaker’s loss function is known can obscure policy trade-offs that exist in the short, medium, and long run. Thus, policy evaluation is more difficult once it is recognized that model and preference uncertainty can interact.
Measuring Precision of Statistical Inference on Partially Identified Parameters
Planners of surveys and experiments that partially identify parameters of interest face trade offs between using limited resources to reduce sampling error and using them to reduce the extent of partial identification. I evaluate these trade offs in a simple statistical problem with normally distributed sample data and interval partial identification using different frequentist measures of inference precision (length of confidence intervals, minimax mean sqaured error and mean absolute deviation, minimax regret for treatment choice) and analogous Bayes measures with a flat prior. The relative value of collecting data with better identification properties (e.g., increasing response rates in surveys) depends crucially on the choice of the measure of precision. When the extent of partial identification is significant in comparison to sampling error, the length of confidence intervals, which has been used most often, assigns the lowest value to improving identification among the measures considered.statistical treatment choice; survey planning; nonresponse; mean squared error; mean absolute deviation; minimax regret
A "Quantal Regret" Method for Structural Econometrics in Repeated Games
We suggest a general method for inferring players' values from their actions
in repeated games. The method extends and improves upon the recent suggestion
of (Nekipelov et al., EC 2015) and is based on the assumption that players are
more likely to exhibit sequences of actions that have lower regret.
We evaluate this "quantal regret" method on two different datasets from
experiments of repeated games with controlled player values: those of (Selten
and Chmura, AER 2008) on a variety of two-player 2x2 games and our own
experiment on ad-auctions (Noti et al., WWW 2014). We find that the quantal
regret method is consistently and significantly more precise than either
"classic" econometric methods that are based on Nash equilibria, or the
"min-regret" method of (Nekipelov et al., EC 2015)
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