Econometrics

385 KiB

Open AccessArticle

Credible Granger-Causality Inference with Modest Sample Lengths: A Cross-Sample Validation Approach

by Richard A. Ashley and Kwok Ping Tsang

Econometrics 2014, 2(1), 72-91; https://doi.org/10.3390/econometrics2010072 - 25 Mar 2014

Cited by 15 | Viewed by 6314

Credible Granger-causality analysis appears to require post-sample inference, as it is well-known that in-sample fit can be a poor guide to actual forecasting effectiveness. However, post-sample model testing requires an often-consequential a priori partitioning of the data into an “in-sample” period – purportedly utilized only for model specification/estimation – and a “post-sample” period, purportedly utilized (only at the end of the analysis) for model validation/testing purposes. This partitioning is usually infeasible, however, with samples of modest length – e.g., T ≤ 150 – as is common in both quarterly data sets and/or in monthly data sets where institutional arrangements vary over time, simply because there is in such cases insufficient data available to credibly accomplish both purposes separately. A cross-sample validation (CSV) testing procedure is proposed below which both eliminates the aforementioned a priori partitioning and which also substantially ameliorates this power versus credibility predicament – preserving most of the power of in-sample testing (by utilizing all of the sample data in the test), while also retaining most of the credibility of post-sample testing (by always basing model forecasts on data not utilized in estimating that particular model’s coefficients). Simulations show that the price paid, in terms of power relative to the in-sample Granger-causality F test, is manageable. An illustrative application is given, to a re-analysis of the Engel andWest [1] study of the causal relationship between macroeconomic fundamentals and the exchange rate; several of their conclusions are changed by our analysis. Full article

482 KiB

Open AccessArticle

Bias-Correction in Vector Autoregressive Models: A Simulation Study

by Tom Engsted and Thomas Q. Pedersen

Econometrics 2014, 2(1), 45-71; https://doi.org/10.3390/econometrics2010045 - 13 Mar 2014

Cited by 18 | Viewed by 7888

Abstract

We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models. Full article

► Show Figures

Figure 1

312 KiB

Open AccessArticle

Incorporating Responsiveness to Marketing Efforts in Brand Choice Modeling

by Dennis Fok, Richard Paap and Philip Hans Franses

Econometrics 2014, 2(1), 20-44; https://doi.org/10.3390/econometrics2010020 - 21 Feb 2014

Cited by 2 | Viewed by 6207

Abstract

We put forward a brand choice model with unobserved heterogeneity that concerns responsiveness to marketing efforts. We introduce two latent segments of households. The first segment is assumed to respond to marketing efforts, while households in the second segment do not do so. Whether a specific household is a member of the first or the second segment at a specific purchase occasion is described by household-specific characteristics and characteristics concerning buying behavior. Households may switch between the two responsiveness states over time. When comparing the performance of our model with alternative choice models that account for various forms of heterogeneity for three different datasets, we find better face validity for our parameters. Our model also forecasts better. Full article

► Show Figures

Figure 1

482 KiB

Open AccessArticle

Referee Bias and Stoppage Time in Major League Soccer: A Partially Adaptive Approach

by Katherine G. Yewell, Steven B. Caudill and Franklin G. Mixon, Jr.

Econometrics 2014, 2(1), 1-19; https://doi.org/10.3390/econometrics2010001 - 17 Feb 2014

Cited by 5 | Viewed by 7959

Abstract

This study extends prior research on referee bias and close bias in professional soccer by examining whether Major League Soccer (MLS) referees’ discretion over stoppage time (i.e., extra play beyond regulation) is influenced by end-of-regulation match scores and/or home field advantage. To do so, we employ a grouped-data regression model and a partially adaptive model. Both account for the imprecise measurement in reported stoppage time. For the 2011 season we find no home field advantage. In fact, stoppage time is the same with a one or two goal deficit at the end of regulation, regardless of which team is ahead. However, the 2011 results do point to an increase in stoppage time of 12 to 20 seconds for nationally televised matches. For the 2012 season, the nationally televised effect disappears due to an increase in stoppage time for those matches not nationally televised. However, a home field advantage is present. Facing a one-goal deficit at the end of regulation, the home team receives about 33 seconds more stoppage time than a visiting team facing the same deficit. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

Econometrics, Volume 2, Issue 1 (March 2014) – 4 articles , Pages 1-91

Further Information

Guidelines

MDPI Initiatives

Follow MDPI