Search results

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

We examine the Stein-rule shrinkage estimator for possible improvements in estimation and forecasting when there are many predictors in a linear time series model. We consider the Stein-rule estimator of Hill and Judge (1987) that shrinks the unrestricted unbiased ordinary least squares (OLS) estimator toward a restricted biased principal component (PC) estimator. Since the Stein-rule estimator combines the OLS and PC estimators, it is a model-averaging estimator and produces a combined forecast. The conditions under which the improvement can be achieved depend on several unknown parameters that determine the degree of the Stein-rule shrinkage. We conduct Monte Carlo simulations to examine these parameter regions. The overall picture that emerges is that the Stein-rule shrinkage estimator can dominate both OLS and principal components estimators within an intermediate range of the signal-to-noise ratio. If the signal-to-noise ratio is low, the PC estimator is superior. If the signal-to-noise ratio is high, the OLS estimator is superior. In out-of-sample forecasting with AR(1) predictors, the Stein-rule shrinkage estimator can dominate both OLS and PC estimators when the predictors exhibit low persistence.

This chapter examines the asymptotic properties of the Stein-type shrinkage combined (averaging) estimation of panel data models. We introduce a combined estimation when the fixed effects (FE) estimator is inconsistent due to endogeneity arising from the correlated common effects in the regression error and regressors. In this case, the FE estimator and the CCEP estimator of Pesaran (2006) are combined. This can be viewed as the panel data model version of the shrinkage to combine the OLS and 2SLS estimators as the CCEP estimator is a 2SLS or control function estimator that controls for the endogeneity arising from the correlated common effects. The asymptotic theory, Monte Carlo simulation, and empirical applications are presented. According to our calculation of the asymptotic risk, the Stein-like shrinkage estimator is more efficient estimation than the CCEP estimator.

Monte Carlo simulation (MCS) has been widely used in probabilistic analysis of the input‐output relationship of a complex function in various applications where probability distributions of the inputs are known. However, MCS is time‐consuming for complex analysis. In addition, it is not straightforward in revealing the relationship between the output variables and the input variables. This paper proposes an alternative analytical approach of statistical estimation, namely the integration of piecewise approach and cell technique that will significantly reduce the number of observations to be taken in a simulation process. The development of the algorithm has included estimation of two statistical parameters, which are the mean and standard deviation. The algorithm has been tested with an engineering example, which is the volumetric shrinkage analysis of a plastic injection molded part.

Hashem Pesaran has made many seminal contributions, among others, in the time series econometrics estimation and forecasting under structural break, see Pesaran and Timmermann (2005, 2007), Pesaran, Pettenuzzo, and Timmermann (2006), and Pesaran, Pick, and Pranovich (2013). In this chapter, the authors focus on the estimation of regression parameters under multiple structural breaks with heteroskedasticity across regimes. The authors propose a combined estimator of regression parameters based on combining restricted estimator under the situation that there is no break in the parameters, with unrestricted estimator under the break. The operational optimal combination weight is between zero and one. The analytical finite sample risk is derived, and it is shown that the risk of the proposed combined estimator is lower than that of the unrestricted estimator under any break size and break points. Further, the authors show that the combined estimator outperforms over the unrestricted estimator in terms of the mean squared forecast errors. Properties of the estimator are also demonstrated in simulations. Finally, empirical illustrations for parameter estimators and forecasts are presented through macroeconomic and financial data sets.

In the context of competing theoretical economic–econometric models and corresponding estimators, we demonstrate a semiparametric combining estimator that, under quadratic loss, has superior risk performance. The method eliminates the need for pretesting to decide between members of the relevant family of econometric models and demonstrates, under quadratic loss, the nonoptimality of the conventional pretest estimator. First-order asymptotic properties of the combined estimator are demonstrated. A sampling study is used to illustrate finite sample performance over a range of econometric model sampling designs that includes performance relative to a Hausman-type model selection pretest estimator. An important empirical problem from the causal effects literature is analyzed to indicate the applicability and econometric implications of the methodology. This combining estimation and inference framework can be extended to a range of models and corresponding estimators. The combining estimator is novel in that it provides directly minimum quadratic loss solutions.

The purpose of this paper is to propose a framework of the potential benefits of asset visibility in the context of returnable transport items (RTI), and uses the framework to examine the effect of asset visibility on the management of RTI systems.

A combined case study and simulation approach was used. A case study was performed to identify and understand how an existing RTI system is managed, while discrete‐event simulation was the method chosen to explore the potential effect of asset visibility.

The paper identifies cost aspects of implementing and operating RTI systems which may be influenced by asset visibility. The study implies that significant cost savings can be achieved through increased asset visibility, and highlights the importance of shrinkage and its impact on the operating cost of an RTI system. However, asset visibility alone is not enough; it requires proper actions and continuous management attention in order to attain the savings.

The results are derived from a single, combined case and simulation study.

The combined methods proved to be an efficient way of assessing and quantifying the potential effect of asset visibility along with the associated uncertainty in the results.

The paper provides an improved understanding of the effect of asset visibility on the management of RTI systems and complements existing visibility literature.

Market‐share analysis focuses on the competitive interrelations between products or brands. Marketing activity may affect the performance of a company's own product and that of its competitors not only within a single time horizon but also over several extended periods. Starting from a static market‐share analysis model, the dynamic relationships of market shares between competitive brands are described by multiplicative competitive interaction (MCI) time‐series models, in which the problem of logical consistency for estimated shares is resolved. A Bayesian shrinkage estimator solution is applied to the further problem of model‐induced collinearity in cross‐differential MCI models. Dynamic elasticity is defined and used to measure the delayed and long‐term effects of marketing mix variables on market shares. The dynamic relationships of future market shares are predicted by means of predictive density. Strategic simulations are conducted under several scenarios for marketing planning. It is argued that the new dynamic model proposed here, applied to daily national or store tracking data, provides useful insights into dynamic competitive relationships in the marketplace, to the benefit of corporate planners, marketing directors, brand managers and retail strategists.

Structural models of demand founded on the classic work of Berry, Levinsohn, and Pakes (1995) link variation in aggregate market shares for a product to the influence of product attributes on heterogeneous consumer tastes. We consider implementing these models in settings with complicated products where consumer preferences for product attributes are sparse, that is, where a small proportion of a high-dimensional product characteristics influence consumer tastes. We propose a multistep estimator to efficiently perform uniform inference. Our estimator employs a penalized pre-estimation model specification stage to consistently estimate nonlinear features of the BLP model. We then perform selection via a Triple-LASSO for explanatory controls, treatment selection controls, and instrument selection. After selecting variables, we use an unpenalized GMM estimator for inference. Monte Carlo simulations verify the performance of these estimators.

The authors introduce a new approach to estimate high-dimensional factor-augmented vector autoregressive models (FAVAR) where the loadings are subject to idiosyncratic regime-switching dynamics. Our Bayesian estimation method alleviates computational challenges and makes the estimation of high-dimensional FAVAR with heterogeneous regime-switching straightforward to implement. The authors perform extensive simulation experiments to study the finite sample performance of our estimation method, demonstrating its relevance in high-dimensional settings. Next, the authors illustrate the performance of the proposed framework for studying the impact of credit market disruptions on a large set of macroeconomic variables. The results of this study underline the importance of accounting for non-linearities in factor loadings when evaluating the propagation of aggregate shocks.