Statistical Backwards Induction: A Simple Method for Estimating Recursive Strategic Models

2007 ◽  
Vol 16 (1) ◽  
pp. 21-40 ◽  
Author(s):  
Muhammet Ali Bas ◽  
Curtis S. Signorino ◽  
Robert W. Walker
Keyword(s):  
Financial Markets ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Extensive Form ◽  
Speculative Attacks ◽  
Simple Method ◽  
Extensive Form Games ◽  
Backwards Induction ◽  
Consistent Parameter

We present a simple method for estimating regressions based on recursive extensive-form games. Our procedure, which can be implemented in most standard statistical packages, involves sequentially estimating standard logits (or probits) in a manner analogous to backwards induction. We demonstrate that the technique produces consistent parameter estimates and show how to calculate consistent standard errors. To illustrate the method, we replicate Leblang's (2003) study of speculative attacks by financial markets and government responses to these attacks.

Valuing Policy Alternatives: A Generalization

2003 ◽  
Vol 11 (4) ◽  
pp. 397-418 ◽  
Author(s):  
Nathan Dietz ◽  
Lawrence S. Rothenberg
Keyword(s):  
Political Science ◽  
Predictive Accuracy ◽  
Ideal Point ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Policy Alternatives ◽  
Point Estimates ◽  
Utility Models ◽  
The Impact ◽  
Consistent Parameter

Those interested in political phenomena such as voting have found random utility models, originally developed for decisions such as transportation choice, especially attractive, as the underlying model can yield a statistical model with a few simple, realistic assumptions. Unfortunately, such models have proven difficult to apply to situations with more than two votes and three alternatives or an unknown cutpoint. Additionally, as we show, standard applications of such models to voting, while producing consistent parameter estimates, yield standard errors that are too small and, due to a failure to employ all relevant theoretical information, biased ideal point estimates. We specify a general model applicable to any number of votes and alternatives, with correct standard errors and unbiased ideal point estimates. We apply this model to a number of cases studied by previous scholars involving legislative voting over the minimum wage: (1) when there are two votes and two known cutpoints (K. Krehbiel and D. Rivers, American Journal of Political Science, 1988, 32, 1151–1174); (2) when there are three votes and three known cutpoints (J. Wilkerson, American Journal of Political Science, 1991, 35, 613–623); and (3) when there are three votes but where one cutpoint is unknown given a lack of knowledge about the impact of a policy (J. Wilkerson, American Journal of Political Science, 1991, 35, 613–623) or the possibility of sophisticated voting (C. Volden, Journal of Politics, 1998, 60, 149–173). We show that in various contexts our analysis improves on existing methods, yielding consistent and efficient ideal point estimates and a better-fitting model with improved predictive accuracy.

The Four-States Model of Memory Retrieval Experiences

2007 ◽  
Vol 215 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Edgar Erdfelder ◽  
Lutz Cüpper ◽  
Tina-Sarah Auer ◽  
Monika Undorf
Keyword(s):  
Signal Detection ◽  
Memory Retrieval ◽  
Measurement Model ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Unequal Variances ◽  
One Dimensional ◽  
Detection Model ◽  
Memory States ◽  
The One

Abstract. A memory measurement model is presented that accounts for judgments of remembering, knowing, and guessing in old-new recognition tasks by assuming four disjoint latent memory states: recollection, familiarity, uncertainty, and rejection. This four-states model can be applied to both Tulving's (1985) remember-know procedure (RK version) and Gardiner and coworker's ( Gardiner, Java, & Richardson-Klavehn, 1996 ; Gardiner, Richardson-Klavehn, & Ramponi, 1997 ) remember-know-guess procedure (RKG version). It is shown that the RK version of the model fits remember-know data approximately as well as the one-dimensional signal detection model does. In contrast, the RKG version of the four-states model outperforms the corresponding detection model even if unequal variances for old and new items are allowed for.We show empirically that the two versions of the four-statesmodelmeasure the same state probabilities. However, the RKG version, requiring remember-know-guess judgments, provides parameter estimates with smaller standard errors and is therefore recommended for routine use.

scholarly journals Prudent Rationalizability in Generalized Extensive-Form Games

2011 ◽  
Author(s):  
Aviad Heifetz ◽  
Martin Meier ◽  
Burkhard C. Schipper
Keyword(s):  
Extensive Form ◽  
Extensive Form Games

Estimating a model of sportfishing trip expenditures using a quasi-maximum likelihood approach

2016 ◽  
Vol 23 (2) ◽  
pp. 448-459 ◽  
Author(s):  
Richard T. Melstrom
Keyword(s):  
Maximum Likelihood ◽  
Exponential Model ◽  
Parameter Estimates ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach ◽  
Quasi Maximum Likelihood ◽  
Species Preferences ◽  
The Impact ◽  
Consistent Parameter

This article presents an exponential model of tourist expenditures estimated by a quasi-maximum likelihood (QML) technique. The advantage of this approach is that, unlike conventional OLS and Tobit estimators, it produces consistent parameter estimates under conditions of a corner solution at zero and heteroscedasticity. An application to sportfishing evaluates the role of socioeconomic demographics and species preferences on trip spending. The bias from an inappropriate estimator is illustrated by comparing the results from QML and OLS estimation, which shows that OLS significantly overstates the impact of trip duration on trip expenditures compared with the QML estimator. Both sets of estimates imply that trout and bass anglers spend significantly more on their fishing trips compared with other anglers.

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