Simulation based Bayesian econometric inference: principles and some recent computational advances

In this paper we discuss several aspects of simulation based Bayesian econometric inference. We start at an elementary level on basic concepts of Bayesian analysis; evaluating integrals by simulation methods is a crucial ingredient in Bayesian inference. Next, the most popular and well-known simulation techniques are discussed, the MetropolisHastings algorithm and Gibbs sampling (being the most popular Markov chain Monte Carlo methods) and importance sampling. After that, we discuss two recently developed sampling methods: adaptive radial based direction sampling [ARDS], which makes use of a transformation to radial coordinates, and neural network sampling, which makes use of a neural network approximation to the posterior distribution of interest. Both methods are especially useful in cases where the posterior distribution is not well-behaved, in the sense of having highly non-elliptical shapes. The simulation techniques are illustrated in several example models, such as a model for the real US GNP and models for binary data of a US recession indicator.

Simulation based bayesian econometric inference: principles and some recent computational advances.

In this paper we discuss several aspects of simulation based Bayesian econometric inference. We start at an elementary level on basic concepts of Bayesian analysis; evaluating integrals by simulation methods is a crucial ingredient in Bayesian inference. Next, the most popular and well-known simulation techniques are discussed, the Metropolis-Hastings algorithm and Gibbs sampling (being the most popular Markov chain Monte Carlo methods) and importance sampling. After that, we discuss two recently developed sampling methods: adaptive radial based direction sampling [ARDS], which makes use of a transformation to radial coordinates, and neural network sampling, which makes use of a neural network approximation to the posterior distribution of interest. Both methods are especially useful in cases where the posterior distribution is not well-behaved, in the sense of having highly non-elliptical shapes. The simulation techniques are illustrated in several example models, such as a model for the real US GNP and models for binary data of a US recession indicator

Why are consumers less loss averse in internal than external reference prices?

The literature has produced mixed support for loss aversion in a reference price context and the outcome may depend on the type of reference price. One extant study has reported empirical evidence that consumers are less loss averse in internal than external reference prices, but without discussing causes or implications. In the current study, we reconcile relevant literature and propose this asymmetric loss aversion result as an empirical generalization. Next, we provide and test an explanation: two empirical regularities in pricing cause that consumers tend to observe few losses for external reference price and many losses for internal reference price, making them less sensitive to internal than external losses. We use two scanner panel data sets to show that the two empirical regularities contribute to asymmetric loss aversion, while accounting for alternative explanations. We explore the implications of loss aversion asymmetry for the effectiveness of price promotions by simulation

Å estimere handelsområder uten å følge kundene hjem

Det er svært viktig å forstå den geografiske utstrekningen for varehandelsområder i detaljhandel. Gjeldende metoder for å tegne opp varehandelsområder er basert på undersøkelser og data fra lojalitetskort. Disse tilnærmingene bruker kundedata på husholdningsnivå til å koble postnummer sammen med butikker, og dette har flere ulemper: unøyaktighet i undersøkelser, ikke-representative data, kostbar datainnsamling og/eller data som kun gjelder butikker innenfor egne kjeder uten at andre kjeder tas med i betraktningen. Vi har utviklet en ny metode for å skissere opp handelsområder. Datakravene er at man må kjenne til de samlede salgsinntektene for butikker, butikkenes egenskaper, populasjonens egenskaper på postnummer-nivå, og avstander mellom butikker og postnummer. Denne typen data er enten offentlig tilgjengelig eller samles vanligvis inn av dataanalyseselskaper som AC Nielsen og Experian, og de er dermed lette å få tak i. Vår tilnærming kan gi handelsområdet til enhver butikk som tilhører enhver kjede, er basert på objektive data og krever ikke kundedata på husholdningsnivå for å koble postnummer til butikker. Isteden forsøker vi å hente ut kundekrets basert på postnummer ved å bryte ned butikkenes samlede salgsnivåer til komponenter som kan tilskrives postnumrene der husholdningene holder til. Vi illustrerer potensialet til den nye modellen med et datasett fra Experian som inneholder alle dagligvareforretninger i Oslo, samt populasjonens egenskaper og reiseavstander på grunnkrets-nivå

Simulation-Based Bayesian Econometric Inference:Principles and Some Recent Computational Advances

In this paper we discuss several aspects of simulation based Bayesian econometric inference. We start at an elementary level on basic concepts of Bayesian analysis; evaluating integrals by simulation methods is a crucial ingredient in Bayesian inference. Next, the most popular and well-known simulation techniques are discussed, the Metropolis-Hastings algorithm and Gibbs sampling (being the most popular Markov chain Monte Carlo methods) and importance sampling. After that, we discuss two recently developed sampling methods: adaptive radial based direction sampling [ARDS], which makes use of a transformation to radial coordinates, and neural network sampling, which makes use of a neural network approximation to the posterior distribution of interest. Both methods are especially useful in cases where the posterior distribution is not well-behaved, in the sense of having highly non-elliptical shapes. The simulation techniques are illustrated in several example models, such as a model for the real US GNP and models for binary data of a US recession indicator