State-Dependent Demand Estimation with Initial Conditions Correction

2020 ◽  
Vol 57 (5) ◽  
pp. 789-809
Author(s):  
Andrey Simonov ◽  
Jean-Pierre Dubé ◽  
Günter Hitsch ◽  
Peter Rossi
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Initial Conditions ◽  
Brand Choice ◽  
State Dependence ◽  
The State ◽  
Data Sets ◽  
True Parameter ◽  
State Dependent ◽  
Initial Conditions Problem

The authors analyze the initial conditions bias in the estimation of brand choice models with structural state dependence. Using a combination of Monte Carlo simulations and empirical case studies of shopping panels, they show that popular, simple solutions that misspecify the initial conditions are likely to lead to bias even in relatively long panel data sets. The magnitude of the bias in the state dependence parameter can be as large as a factor of 2–2.5. The authors propose a solution to the initial conditions problem that samples the initial states as auxiliary variables in a Markov chain Monte Carlo procedure. The approach assumes that the joint distribution of prices and consumer choices is in equilibrium, which is plausible for the mature consumer packaged goods products commonly used in empirical applications. In Monte Carlo simulations, the approach recovers the true parameter values even in relatively short panels. Finally, the authors propose a diagnostic tool that uses common, biased approaches to bound the values of the state dependence and construct a computationally light test for state dependence.

On the Estimation of the Kurtosis in Directional Sea States for Freak Wave Forecasting

2011 ◽  
Vol 41 (8) ◽  
pp. 1484-1497 ◽  
Author(s):  
Nobuhito Mori ◽  
Miguel Onorato ◽  
Peter A. E. M. Janssen
Keyword(s):  
Monte Carlo ◽  
Energy Distribution ◽  
Monte Carlo Simulations ◽  
Initial Conditions ◽  
Laboratory Data ◽  
Dispersion Effect ◽  
Freak Wave ◽  
Wave Forecasting ◽  
Directional Sea States ◽  
Good Agreement

Abstract Based on Monte Carlo simulations of the nonlinear Schrödinger equation in two horizontal dimensions, the dependence of the kurtosis on the directional energy distribution of the initial conditions is examined. The parametric survey is carried out to obtain the behavior of the kurtosis as function of the Benjamin–Feir index and directional spread in directional sea states. As directional dispersion effect becomes significant, the kurtosis monotonically decreases in comparison with the unidirectional waves. A parameterization of the kurtosis estimated from directional spectra is proposed here; the error of the parameterization is at most 10%. The parameterization is verified against laboratory data, and good agreement is obtained.

scholarly journals Loss Aversion and Consumption Plans with Stochastic Reference Points

2016 ◽  
Vol 16 (1) ◽  
pp. 303-336 ◽  
Author(s):  
Hyeon Park
Keyword(s):  
Decision Maker ◽  
Loss Aversion ◽  
Intertemporal Choice ◽  
Choice Model ◽  
State Dependence ◽  
Analytic Solutions ◽  
The State ◽  
Reference Points ◽  
State Dependent ◽  
The Individual

AbstractThis paper studies the making of risky choices following loss aversion with endogenous reference expectations under the two schemes of state-independent and state-dependent stochastic reference points. Using a tractable, intertemporal choice model, this paper derives analytic solutions to show that, when loss aversion is high, the reference-dependent decision maker saves a markedly larger amount than is predicted by the standard model. When the loss aversion is low (i.e. the individual is loss-tolerant), the overall result is ambiguous, although the decision maker may deviate into consuming more; if he faces a small level of uncertainty relative to the intensity of his loss aversion, he may even do this by borrowing. Given the same loss aversion level, this study determines that, in the presence of positive state-dependence, the state-independent model generates greater deviation than the state-dependent one. Finally, this paper derives a two-period general equilibrium result with two agents who have different attitudes toward loss.

scholarly journals Monte Carlo simulations of two-component drop growth by stochastic coalescence

2009 ◽  
Vol 9 (4) ◽  
pp. 1241-1251 ◽  
Author(s):  
L. Alfonso ◽  
G. B. Raga ◽  
D. Baumgardner
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Numerical Solutions ◽  
Initial Conditions ◽  
Stochastic Algorithm ◽  
Aerosol Composition ◽  
Aerosol Mass ◽  
Two Component ◽  
Framework Species ◽  
Stochastic Coalescence

Abstract. The evolution of two-dimensional drop distributions is simulated in this study using a Monte Carlo method. The stochastic algorithm of Gillespie (1976) for chemical reactions in the formulation proposed by Laurenzi et al. (2002) was used to simulate the kinetic behavior of the drop population. Within this framework, species are defined as droplets of specific size and aerosol composition. The performance of the algorithm was checked by a comparison with the analytical solutions found by Lushnikov (1975) and Golovin (1963) and with finite difference solutions of the two-component kinetic collection equation obtained for the Golovin (sum) and hydrodynamic kernels. Very good agreement was observed between the Monte Carlo simulations and the analytical and numerical solutions. A simulation for realistic initial conditions is presented for the hydrodynamic kernel. As expected, the aerosol mass is shifted from small to large particles due to collection process. This algorithm could be extended to incorporate various properties of clouds such several crystals habits, different types of soluble CCN, particle charging and drop breakup.

Multivariate Normal Data: A Tool for Instruction

1992 ◽  
Vol 19 (3) ◽  
pp. 188-189 ◽  
Author(s):  
John F. Walsh
Keyword(s):  
Monte Carlo ◽  
Experimental Design ◽  
Monte Carlo Simulations ◽  
Covariance Structure ◽  
Data Sets ◽  
Multivariate Normal ◽  
International Mathematical ◽  
Statistical Library ◽  
Scale Transformations

Courses in statistics and experimental design can be enhanced through use of crafted data sets. The use of examples highlights the interface between data and statistical routine. FORTRAN programs utilizing the International Mathematical and Statistical Library subroutines permit the user to control the variance—covariance structure of multivariate normal variables and build data sets that have instructional value. Scale transformations and Monte Carlo simulations of the data can be performed as well.

scholarly journals Lessons from Pinocchio: Cues to deception may be highly exaggerated

2018 ◽  
Author(s):  
Timothy John Luke
Keyword(s):  
Monte Carlo ◽  
Low Power ◽  
Monte Carlo Simulations ◽  
Publication Bias ◽  
False Positive ◽  
The State ◽  
Effect Sizes ◽  
Cues To Deception ◽  
State Of The Science ◽  
Informational Value

Deception researchers widely acknowledge that cues to deception - observable behaviors that may differ between truthful and deceptive messages - tend to be weak. Nevertheless, several deception cues have been reported with unusually large effect sizes, and some researchers have advocated the use of such cues as tools for detecting deceit and assessing credibility in practical contexts. Examining data from empirical deception cue research and using a series of Monte Carlo simulations, I demonstrate that many estimated effect sizes of deception cues may be greatly inflated by publication bias, small numbers of estimates, and low power. Indeed, simulations indicate the informational value of the present deception literature is quite low, such it is not possible to determine whether any given effect is real or a false positive. I warn against the hazards of relying on potentially illusory cues to deception and offer some recommendations for improving the state of the science of deception.

scholarly journals Selection Bias in Comparative Research: The Case of Incomplete Data Sets

2003 ◽  
Vol 11 (3) ◽  
pp. 255-274 ◽  
Author(s):  
Simon Hug
Keyword(s):  
Monte Carlo ◽  
Political Parties ◽  
Monte Carlo Simulations ◽  
Recent Work ◽  
Selection Bias ◽  
Incomplete Data ◽  
Comparative Research ◽  
Data Sets ◽  
Comparative Case Studies ◽  
Cross National

Selection bias is an important but often neglected problem in comparative research. While comparative case studies pay some attention to this problem, this is less the case in broader cross-national studies, where this problem may appear through the way the data used are generated. The article discusses three examples: studies of the success of newly formed political parties, research on protest events, and recent work on ethnic conflict. In all cases the data at hand are likely to be afflicted by selection bias. Failing to take into consideration this problem leads to serious biases in the estimation of simple relationships. Empirical examples illustrate a possible solution (a variation of a Tobit model) to the problems in these cases. The article also discusses results of Monte Carlo simulations, illustrating under what conditions the proposed estimation procedures lead to improved results.

ENSO Complexity Induced by State Dependence of Westerly Wind Events

2017 ◽  
Vol 30 (9) ◽  
pp. 3401-3420 ◽  
Author(s):  
Michiya Hayashi ◽  
Masahiro Watanabe
Keyword(s):  
El Niño ◽  
El Nino ◽  
State Dependence ◽  
The State ◽  
Stochastic Noise ◽  
Westerly Wind ◽  
Central Pacific ◽  
State Dependent ◽  
Wind Events ◽  
Westerly Wind Events

Coupled dynamics between westerly wind events (WWEs) and the El Niño–Southern Oscillation (ENSO) is examined using an atmosphere–ocean coupled model with intermediate complexity. The model incorporates state-dependent stochastic noise that mimics observed WWEs, which occur at the edge of the Pacific warm pool when the Niño-4 sea surface temperature (SST) anomaly increases positively. The model parameter that controls the efficiency of the thermocline feedback, γ, is perturbed to elaborate the sensitivity of the results to the system’s stability. Without the noise (experiment NO), the model produces an ENSO-like regular oscillation with a 6-yr period, the variance of which increases with γ. When additive noise is introduced over the western Pacific (experiment AD), the oscillations become irregular with a dominant period of 4–6 years and the increase in the variance relative to the NO experiment depends on γ. When state-dependent noise is included (experiment SD), the oscillatory solution is also irregular, and its variance and asymmetry are increased irrespective of the value of γ. Both the additive and state-dependent noise contribute to the occurrence of two types of variability, corresponding to the eastern Pacific (EP) and central Pacific (CP) El Niños. In SD, the state dependence of the stochastic noise guarantees the existence of CP El Niño regardless of γ since the increased likelihood of WWE occurrence with Niño-4 SSTs results in a positive feedback in the central Pacific. The above results suggest that the state dependence of WWEs plays a crucial role in the asymmetry and diversity of ENSO.

scholarly journals The Generalized Transmuted Poisson-G Family of Distributions: Theory, Characterizations and Applications

2018 ◽  
pp. 759-779 ◽  
Author(s):  
Haitham Yousof ◽  
Ahmed Z Afify ◽  
Morad Alizadeh ◽  
G. G. Hamedani ◽  
S. Jahanshahi ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Order Statistics ◽  
Real Data ◽  
Model Parameters ◽  
Data Sets ◽  
Continuous Distributions ◽  
New Class ◽  
Mathematical Properties ◽  
Family Of Distributions

In this work, we introduce a new class of continuous distributions called the generalized poissonfamily which extends the quadratic rank transmutation map. We provide some special models for thenew family. Some of its mathematical properties including Rényi and q-entropies, order statistics andcharacterizations are derived. The estimations of the model parameters is performed by maximumlikelihood method. The Monte Carlo simulations is used for assessing the performance of the maximumlikelihood estimators. The ‡exibility of the proposed family is illustrated by means of two applicationsto real data sets.

Regression discontinuity designs with unknown state-dependent discontinuity points: estimation and testing

2018 ◽  
Vol 23 (2) ◽  
Author(s):  
Lixiong Yang
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Treatment Effect ◽  
Regression Discontinuity ◽  
Test Statistics ◽  
State Dependency ◽  
Monte Carlo Experiments ◽  
State Dependent ◽  
Regression Discontinuity Designs ◽  
Biased Estimates

Abstract This paper extends Regression discontinuity designs with unknown discontinuity points developed by (Porter, J., and P. Yu. 2015. “Regression Discontinuity Designs with Unknown Discontinuity Points: Testing and Estimation.” Journal of Econometrics 189: 132–147.) to allow for state-dependent discontinuity points. We discuss the estimation of the model, and propose test statistics for treatment effect and state dependency in the discontinuity points. We conduct Monte Carlo simulations to compare the proposed estimator with these based on the constant discontinuity RDD and the classic fuzzy RDD, and find that overlooking the state dependency can lead to biased estimates of treatment effects, while the proposed estimator works well and is robust when applied to constant discontinuity RDDs. Monte Carlo experiments also point out that the sizes and powers of the proposed test statistics are generally satisfactory. The model is illustrated with an empirical application.

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