scholarly journals Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach

2017 ◽  
Author(s):  
Ida Johnsson ◽  
Hyungsik Roger Moon
Keyword(s):  
Social Networks ◽  
Peer Effects ◽  
Control Function ◽  
Function Approach ◽  
Control Function Approach

scholarly journals Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach

2019 ◽  
pp. 1-51 ◽  
Author(s):  
Ida Johnsson ◽  
Hyungsik Roger Moon
Keyword(s):  
Peer Effects ◽  
Control Function ◽  
Peer Group ◽  
Individual Characteristics ◽  
Function Approach ◽  
Semiparametric Approach ◽  
The Social ◽  
Semiparametric Estimator ◽  
Control Function Approach ◽  
Link Formation

We propose methods of estimating the linear-in-means model of peer effects in which the peer group, defined by a social network, is endogenous in the outcome equation for peer effects. Endogeneity is due to unobservable individual characteristics that inuence both link formation in the network and the outcome of interest. We propose two estimators of the peer effect equation that control for the endogeneity of the social connections using a control function approach. We leave the functional form of the control function unspecified, estimate the model using a sieve semiparametric approach and establish asymptotics of the semiparametric estimator.

scholarly journals Local Government Cooperation at Work: A Control Function Approach

2014 ◽  
Author(s):  
Zineb Abid ◽  
Edoardo Di Porto ◽  
Angela Parenti ◽  
Sonia Paty
Keyword(s):  
Local Government ◽  
Control Function ◽  
Function Approach ◽  
Control Function Approach

Demand Estimation Using Managerial Responses to Automated Price Recommendations

2022 ◽  
Author(s):  
Daniel Garcia ◽  
Juha Tolvanen ◽  
Alexander K. Wagner
Keyword(s):  
Control Function ◽  
Price Change ◽  
Decision Makers ◽  
Function Approach ◽  
Difference In Differences ◽  
Price Variation ◽  
Data Set ◽  
Demand Elasticities ◽  
Price Adjustments ◽  
Control Function Approach

We provide a new framework to identify demand elasticities in markets where managers rely on algorithmic recommendations for price setting and apply it to a data set containing bookings for a sample of midsized hotels in Europe. Using nonbinding algorithmic price recommendations and observed delay in price adjustments by decision makers, we demonstrate that a control-function approach, combined with state-of-the-art model-selection techniques, can be used to isolate exogenous price variation and identify demand elasticities across hotel room types and over time. We confirm these elasticity estimates with a difference-in-differences approach that leverages the same delays in price adjustments by decision makers. However, the difference-in-differences estimates are more noisy and only yield consistent estimates if data are pooled across hotels. We then apply our control-function approach to two classic questions in the dynamic pricing literature: the evolution of price elasticity of demand over and the effects of a transitory price change on future demand due to the presence of strategic buyers. Finally, we discuss how our empirical framework can be applied directly to other decision-making situations in which recommendation systems are used. This paper was accepted by Omar Besbes, revenue management and market analytics.

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