Special Issue "Celebrated Econometricians: Katarina Juselius and Søren Johansen"

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

Deadline for manuscript submissions: closed (31 March 2018)

Special Issue Editors

Guest Editor
Dr. Paolo Paruolo

European Commission, Joint Research Centre, IT
Website | E-Mail
Interests: econometrics (theory and applications), counterfactual methods
Guest Editor
Prof. Rocco Mosconi

Politecnico di Milano, IT
Website | E-Mail
Interests: Time series, financial econometrics

Special Issue Information

Dear Colleagues,

Contributions for this Special Issue in honour of Katarina Juselius and Søren Johansen should relate to an area of research to which they have made significant contributions. These include, but are certainly not limited to, econometric theory and applications related to the following questions:

  1. How many common trends are there in a given set of time series?
  2. What is the set of equilibria relations, or, dually what characterizes the attractor set for the common trends?
  3. How is one variable adjusting to the (dis-)equilibrium or going back to the attractor?
  4. Is the VAR compatible with agents being learning or rational?
  5. What would have happened if a different policy intervention had been implemented?

Katarina Juselius and Søren Johansen contributed to these fundamental issues by developing new methodology, and by providing inspiring paradigmatic applications to several empirical economics problems; the latter range from the study of international parity relationships, to the analysis of monetary policy, unemployment, climatology, and optimal hedging, to name a few. The former include, but are not restricted to: representation, inference and testing in I(1) and I(2) and fractional systems, nonlinear time series, structural breaks, (mis)-specification testing, identification of linear system of equations.

This Special Issue aims to collect state of the art applications and theory developments in these areas, as well as in all other areas to which Katarina Juselius and Søren Johansen have contributed.

Informal enquiries regarding the scope and suitability of a potential submission should first be made to the guest editors (emails on the top of this page).

Rocco Mosconi
Paolo Paruolo
Guest Editors

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 Charges (APCs) of 350 CHF (Swiss Francs) per published paper are fully funded by institutions through the Knowledge Unlatched initiative, resulting in no direct charge to authors. 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.

Keywords

  • Cointegration
  • Common trends
  • Error-correcting adjustment
  • Estimation and hypothesis testing in cointegrated models
  • Fractional integration
  • Imperfect knowledge and expectation formation
  • Macroeconomic fluctuations and transmission mechanisms
  • Mis-specification testing
  • Representation theory of I(1), I(2) systems
  • Short-run and long-run impact
  • Vector Autoregressive Processes

Published Papers (4 papers)

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Research

Open AccessArticle The Stochastic Stationary Root Model
Econometrics 2018, 6(3), 39; https://doi.org/10.3390/econometrics6030039
Received: 31 March 2018 / Revised: 27 July 2018 / Accepted: 13 August 2018 / Published: 21 August 2018
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Abstract
We propose and study the stochastic stationary root model. The model resembles the cointegrated VAR model but is novel in that: (i) the stationary relations follow a random coefficient autoregressive process, i.e., exhibhits heavy-tailed dynamics, and (ii) the system is observed with measurement
[...] Read more.
We propose and study the stochastic stationary root model. The model resembles the cointegrated VAR model but is novel in that: (i) the stationary relations follow a random coefficient autoregressive process, i.e., exhibhits heavy-tailed dynamics, and (ii) the system is observed with measurement error. Unlike the cointegrated VAR model, estimation and inference for the SSR model is complicated by a lack of closed-form expressions for the likelihood function and its derivatives. To overcome this, we introduce particle filter-based approximations of the log-likelihood function, sample score, and observed Information matrix. These enable us to approximate the ML estimator via stochastic approximation and to conduct inference via the approximated observed Information matrix. We conjecture the asymptotic properties of the ML estimator and conduct a simulation study to investigate the validity of the conjecture. Model diagnostics to assess model fit are considered. Finally, we present an empirical application to the 10-year government bond rates in Germany and Greece during the period from January 1999 to February 2018. Full article
(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)
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Open AccessArticle The Relation between Monetary Policy and the Stock Market in Europe
Econometrics 2018, 6(3), 36; https://doi.org/10.3390/econometrics6030036
Received: 21 March 2018 / Revised: 25 July 2018 / Accepted: 31 July 2018 / Published: 5 August 2018
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Abstract
We use a cointegrated structural vector autoregressive model to investigate the relation between monetary policy in the euro area and the stock market. Since there may be an instantaneous causal relation, we consider long-run identifying restrictions for the structural shocks and also used
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We use a cointegrated structural vector autoregressive model to investigate the relation between monetary policy in the euro area and the stock market. Since there may be an instantaneous causal relation, we consider long-run identifying restrictions for the structural shocks and also used (conditional) heteroscedasticity in the residuals for identification purposes. Heteroscedasticity is modelled by a Markov-switching mechanism. We find a plausible identification scheme for stock market and monetary policy shocks which is consistent with the second-order moment structure of the variables. The model indicates that contractionary monetary policy shocks lead to a long-lasting downturn of real stock prices. Full article
(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)
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Open AccessArticle Johansen’s Reduced Rank Estimator Is GMM
Econometrics 2018, 6(2), 26; https://doi.org/10.3390/econometrics6020026
Received: 30 January 2018 / Revised: 9 March 2018 / Accepted: 16 May 2018 / Published: 18 May 2018
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Abstract
The generalized method of moments (GMM) estimator of the reduced-rank regression model is derived under the assumption of conditional homoscedasticity. It is shown that this GMM estimator is algebraically identical to the maximum likelihood estimator under normality developed by Johansen (1988). This includes
[...] Read more.
The generalized method of moments (GMM) estimator of the reduced-rank regression model is derived under the assumption of conditional homoscedasticity. It is shown that this GMM estimator is algebraically identical to the maximum likelihood estimator under normality developed by Johansen (1988). This includes the vector error correction model (VECM) of Engle and Granger. It is also shown that GMM tests for reduced rank (cointegration) are algebraically similar to the Gaussian likelihood ratio tests. This shows that normality is not necessary to motivate these estimators and tests. Full article
(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)
Open AccessFeature PaperArticle Evaluating Forecasts, Narratives and Policy Using a Test of Invariance
Econometrics 2017, 5(3), 39; https://doi.org/10.3390/econometrics5030039
Received: 17 December 2016 / Revised: 14 August 2017 / Accepted: 23 August 2017 / Published: 1 September 2017
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Abstract
Economic policy agencies produce forecasts with accompanying narratives, and base policy changes on the resulting anticipated developments in the target variables. Systematic forecast failure, defined as large, persistent deviations of the outturns from the numerical forecasts, can make the associated narrative false, which
[...] Read more.
Economic policy agencies produce forecasts with accompanying narratives, and base policy changes on the resulting anticipated developments in the target variables. Systematic forecast failure, defined as large, persistent deviations of the outturns from the numerical forecasts, can make the associated narrative false, which would in turn question the validity of the entailed policy implementation. We establish when systematic forecast failure entails failure of the accompanying narrative, which we call forediction failure, and when that in turn implies policy invalidity. Most policy regime changes involve location shifts, which can induce forediction failure unless the policy variable is super exogenous in the policy model. We propose a step-indicator saturation test to check in advance for invariance to policy changes. Systematic forecast failure, or a lack of invariance, previously justified by narratives reveals such stories to be economic fiction. Full article
(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)
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