Bayesian Threshold Autoregressive Models for Nonlinear Time Series

Geweke, John; Terui, Nobuhiko

doi:10.1111/j.1467-9892.1993.tb00156.x

Cited by 102 publications

(48 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another approach is Bayesian inference. For example, Ferreira (1975), Geweke and Terui (1993), and Chen and Lee (1995) provided Bayesian approaches to deal with discontinuous likelihoods in econometrics and nonlinear timeseries models. Our proposed method, using more updated tools, directly models price thresholds in choice models in a general way and conducts coherent statistical inference on them under a small-sample situation.…”

Section: Discontinuous Likelihoods For Thresholdsmentioning

confidence: 99%

Research Note—Estimating Heterogeneous Price Thresholds

Terui

Dahana

2006

Marketing Science

Self Cite

View full text Add to dashboard Cite

A brand choice model with heterogeneous price-threshold parameters is used to investigate a three-regime piecewise-linear stochastic utility function. The model is used to explore the relationships between aspects of consumer price sensitivity and price thresholds using hierarchical Bayes modeling with the Markov chain Monte Carlo (MCMC) method. This study contributes to the modeling literature on discontinuous likelihoods in choice models. The empirical application using our scanner panel data set shows that the reference effect and loss aversion are more marked after price thresholds are taken into heterogeneous price response models. Furthermore, loss aversion is attenuated by using price thresholds than by an aggregate (homogeneity) model without price thresholds.

show abstract

Section: Discontinuous Likelihoods For Thresholdsmentioning

confidence: 99%

Research Note—Estimating Heterogeneous Price Thresholds

Terui

Dahana

2006

Marketing Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Autoregression models have been modified by adding time-dependent variables to adapt with the nonstationarity of time series. Geweke and Terui (1993) developed the threshold autoregressive model for which its parameters depend on the value of a previous observation. Juang and Rabiner (1985), Wong and Li (2000), and Frühwirth-Schnatter (2006) introduced mixture models for time series.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Model for Stock Trading

Nguyen

2018

IJFS

View full text Add to dashboard Cite

Hidden Markov model (HMM) is a statistical signal prediction model, which has been widely used to predict economic regimes and stock prices. In this paper, we introduce the application of HMM in trading stocks (with S&P 500 index being an example) based on the stock price predictions. The procedure starts by using four criteria, including the Akaike information, the Bayesian information, the Hannan Quinn information, and the Bozdogan Consistent Akaike Information, in order to determine an optimal number of states for the HMM. The selected four-state HMM is then used to predict monthly closing prices of the S&P 500 index. For this work, the out-of-sample R 2 OS , and some other error estimators are used to test the HMM predictions against the historical average model. Finally, both the HMM and the historical average model are used to trade the S&P 500. The obtained results clearly prove that the HMM outperforms this traditional method in predicting and trading stocks.

show abstract

“…In the second, by considering that the outliers often exist in scanner data, we can extend the proposed model so as to accommodate the outliers. As a robust model for time series data, an autoregressive model with t distributed innovations is proposed and Bayesian solution is discussed in Geweke(1993). These extensions of the proposed model are left for future research.…”

Section: Discussionmentioning

confidence: 99%

Forecasting dynamic market share relationships

Terui

2000

Marketing Intelligence &Amp; Planning

Self Cite

View full text Add to dashboard Cite

In market share analysis, it is well recognized that we have often inadmissible predicted market share, which means that some of predictors take the values outside the range [0, 1] and the total sum of predicted shares is not always one, so called "logical inconsistency". In this article, based on Bayesian VAR model, I propose a dynamic market share model with logical consistency. The proposed method makes it possible to forecast not only the values of market share by themselves, but also various dynamic market share relations across dierent brands or companies. The daily scanner data are analyzed by the proposed method.

show abstract

Bayesian Threshold Autoregressive Models for Nonlinear Time Series

Cited by 102 publications

References 18 publications

Research Note—Estimating Heterogeneous Price Thresholds

Research Note—Estimating Heterogeneous Price Thresholds

Hidden Markov Model for Stock Trading

Forecasting dynamic market share relationships

Contact Info

Product

Resources

About