Special Issue "Celebrated Econometricians: David Hendry"

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (15 November 2018).

Special Issue Editor

Neil Ericsson
Website
Guest Editor
Federal Reserve Board, Washington, DC, US
Interests: Econometrics and Statistics; Monetary Economics; Macroeconomics

Special Issue Information

Dear Colleagues,

Contributions for the Special Issue, in honor of David Hendry, should relate to an area of research in which David has made recent important contributions. Potential areas include the following: Exploring alternative modeling strategies and empirical methodologies for macro-econometrics; analyzing concepts and criteria for viable empirical modeling of time series; diagnostic testing and model specification techniques; computer automated procedures for model selection, especially when facing structural breaks; developing software for econometric analysis; empirical investigations of money demand, wage and price inflation, and climate change; empirical and theoretical analyses of forecasting, especially forecast failure and co-breaking; and the history of econometric thought.

Inquiries about this Special Issue should be addressed to: [email protected]

Neil Ericsson
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Econometrics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

Open AccessFeature PaperArticle
Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?
Econometrics 2020, 8(2), 16; https://doi.org/10.3390/econometrics8020016 - 06 May 2020
Abstract
We apply a bootstrap test to determine whether some forecasters are able to make superior probability assessments to others. In contrast to some findings in the literature for point predictions, there is evidence that some individuals really are better than others. The testing [...] Read more.
We apply a bootstrap test to determine whether some forecasters are able to make superior probability assessments to others. In contrast to some findings in the literature for point predictions, there is evidence that some individuals really are better than others. The testing procedure controls for the different economic conditions the forecasters may face, given that each individual responds to only a subset of the surveys. One possible explanation for the different findings for point predictions and histograms is explored: that newcomers may make less accurate histogram forecasts than experienced respondents given the greater complexity of the task. Full article
(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)
Open AccessArticle
Balanced Growth Approach to Tracking Recessions
Econometrics 2020, 8(2), 14; https://doi.org/10.3390/econometrics8020014 - 23 Apr 2020
Abstract
In this paper, we propose a hybrid version of Dynamic Stochastic General Equilibrium models with an emphasis on parameter invariance and tracking performance at times of rapid changes (recessions). We interpret hypothetical balanced growth ratios as moving targets for economic agents that rely [...] Read more.
In this paper, we propose a hybrid version of Dynamic Stochastic General Equilibrium models with an emphasis on parameter invariance and tracking performance at times of rapid changes (recessions). We interpret hypothetical balanced growth ratios as moving targets for economic agents that rely upon an Error Correction Mechanism to adjust to changes in target ratios driven by an underlying state Vector AutoRegressive process. Our proposal is illustrated by an application to a pilot Real Business Cycle model for the US economy from 1948 to 2019. An extensive recursive validation exercise over the last 35 years, covering 3 recessions, is used to highlight its parameters invariance, tracking and 1- to 3-step ahead forecasting performance, outperforming those of an unconstrained benchmark Vector AutoRegressive model. Full article
(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)
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Open AccessArticle
HAR Testing for Spurious Regression in Trend
Econometrics 2019, 7(4), 50; https://doi.org/10.3390/econometrics7040050 - 16 Dec 2019
Abstract
The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests in [...] Read more.
The usual t test, the t test based on heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators, and the heteroskedasticity and autocorrelation robust (HAR) test are three statistics that are widely used in applied econometric work. The use of these significance tests in trend regression is of particular interest given the potential for spurious relationships in trend formulations. Following a longstanding tradition in the spurious regression literature, this paper investigates the asymptotic and finite sample properties of these test statistics in several spurious regression contexts, including regression of stochastic trends on time polynomials and regressions among independent random walks. Concordant with existing theory (Phillips 1986, 1998; Sun 2004, 2014b) the usual t test and HAC standardized test fail to control size as the sample size n in these spurious formulations, whereas HAR tests converge to well-defined limit distributions in each case and therefore have the capacity to be consistent and control size. However, it is shown that when the number of trend regressors K , all three statistics, including the HAR test, diverge and fail to control size as n . These findings are relevant to high-dimensional nonstationary time series regressions where machine learning methods may be employed. Full article
(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)
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