scholarly journals The Macroeconomic Controversy Over Price Rigidity — How to Resolve it and How Bayesian Estimation has Led us Astray

2022 ◽  
Author(s):  
David Meenagh ◽  
Patrick Minford ◽  
Michael R. Wickens
Keyword(s):  
Monte Carlo ◽  
Bayesian Estimation ◽  
Estimation Methods ◽  
Monte Carlo Experiment ◽  
Price Rigidity ◽  
Estimation Bias ◽  
Indirect Estimation ◽  
Macroeconomic Models ◽  
Estimation Techniques ◽  
Price Flexibility

AbstractPrice rigidity plays a central role in macroeconomic models but remains controversial. Those espousing it look to Bayesian estimated models in support, while those assuming price flexibility largely impose it on their models. So controversy continues unresolved by testing on the data. In a Monte Carlo experiment we ask how different estimation methods could help to resolve this controversy. We find Bayesian estimation creates a large potential estimation bias compared with standard estimation techniques. Indirect estimation where the bias is found to be low appears to do best, and offers the best way forward for settling the price rigidity controversy.

Can Indirect Estimation Methods and the Medical Officer of Health Reports 'Correct' Distorted Infant Mortality Rates Reported by the Registrar General? The Case of London, 1896–1911

2021 ◽  
pp. 57-81
Author(s):  
Sarah L Rafferty
Keyword(s):  
Infant Mortality ◽  
Medical Officer ◽  
Mortality Rates ◽  
Alternative Methods ◽  
Estimation Methods ◽  
Place Of Residence ◽  
Indirect Estimation ◽  
Registrar General ◽  
Estimation Techniques ◽  
The Rich

The Registrar General's Returns are an integral source for historical demographers. Concerns have been raised, however, over the geographical accuracy of their pre-1911 mortality figures when institutional deaths were not redistributed to place of residence. This paper determines the extent of the distortions caused by institutional mortality in the context of aggregate infant mortality rates for London's registration sub-districts. The potential of two alternative methods to 'correct' these distortions is then assessed. The first method uses indirect estimation techniques based on data from the 1911 Fertility Census, and the second exploits the rich detail available from the Medical Officer of Health reports. Through narrowing the focus to seven London registration sub-districts over the years 1896–1911, it is shown that both suggested alternative methods remove the institutional mortality biases found in the Registrar General's figures, yet they come with their own limitations.

scholarly journals A Bayesian model for binary Markov chains

2004 ◽  
Vol 2004 (8) ◽  
pp. 421-429 ◽  
Author(s):  
Souad Assoudou ◽  
Belkheir Essebbar
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chains ◽  
Bayesian Estimation ◽  
Bayesian Model ◽  
Transition Probabilities ◽  
Simulated Data ◽  
Bayesian Estimator ◽  
Jeffreys Prior ◽  
Data Set

This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC) techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.

scholarly journals On the existence of an optimal estimation window for risk measures

2015 ◽  
Vol 4 (1and2) ◽  
pp. 28
Author(s):  
Marcelo Brutti Righi ◽  
Paulo Sergio Ceretta
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Optimal Estimation ◽  
Expected Shortfall ◽  
Estimation Bias ◽  
Optimal Length

We investigate whether there can exist an optimal estimation window for financial risk measures. Accordingly, we propose a procedure that achieves optimal estimation window by minimizing estimation bias. Using results from a Monte Carlo simulation for Value at Risk and Expected Shortfall in distinct scenarios, we conclude that the optimal length for the estimation window is not random but has very clear patterns. Our findings can contribute to the literature, as studies have typically neglected the estimation window choice or relied on arbitrary choices.

scholarly journals Bayesian Uncertainty Estimation for Gaussian Graphical Models and Centrality Indices

2021 ◽  
Author(s):  
Joran Jongerling ◽  
Sacha Epskamp ◽  
Donald Ray Williams
Keyword(s):  
Bayesian Estimation ◽  
Graphical Models ◽  
Estimation Methods ◽  
Centrality Measures ◽  
Gaussian Graphical Models ◽  
Graphical Lasso ◽  
Study Results ◽  
Partial Correlations ◽  
Good Coverage ◽  
Better Than

Gaussian Graphical Models (GGMs) are often estimated using regularized estimation and the graphical LASSO (GLASSO). However, the GLASSO has difficulty estimating(uncertainty in) centrality indices of nodes. Regularized Bayesian estimation might provide a solution, as it is better suited to deal with bias in the sampling distribution ofcentrality indices. This study therefore compares estimation of GGMs with a Bayesian GLASSO- and a Horseshoe prior to estimation using the frequentist GLASSO in an extensive simulation study. Results showed that out of the two Bayesian estimation methods, the Bayesian GLASSO performed best. In addition, the Bayesian GLASSOperformed better than the frequentist GLASSO with respect to bias in edge weights, centrality measures, correlation between estimated and true partial correlations, andspecificity. With respect to sensitivity the frequentist GLASSO performs better.However, sensitivity of the Bayesian GLASSO is close to that of the frequentist GLASSO (except for the smallest N used in the simulations) and tends to be favored over the frequentist GLASSO in terms of F1. With respect to uncertainty in the centrality measures, the Bayesian GLASSO shows good coverage for strength andcloseness centrality. Uncertainty in betweenness centrality is estimated less well, and typically overestimated by the Bayesian GLASSO.

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