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Econometrics
Special Issues
Recent Developments in Macro-Econometric Modeling: Theory and Applications
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Recent Developments in Macro-Econometric Modeling: Theory and Applications

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

Deadline for manuscript submissions: closed (30 November 2017) | Viewed by 85501

Special Issue Editors

Prof. Dr. Gilles Dufrénot

E-Mail Website
Guest Editor
AMU-AMSE 5-9, Boulevard Maurice Bourdet, CS 50498, 13205 Marseille, Cedex 1, France
Interests: economic policy; macroeconomics; time series; panel data
Dr. Fredj Jawadi

E-Mail Website
Guest Editor
University of Evry Val d’Essonne, 2 rue Facteur Cheval, Baîtment La Poste, Bureau 226 Evry 91025, France
Interests: Applied econometrics; Nonlinear Dynamics; financial econometrics and financial macroeconomics
Special Issues, Collections and Topics in MDPI journals
Dr. Alexander Mihailov

E-Mail Website
Guest Editor
Department of Economics, University of Reading, Whiteknights, Reading RG6 6AA, UK
Interests: international macroeconomics and finance; monetary theory and policy; political macroeconomics and socioeconomic dynamics; information and learning

Special Issue Information

Dear Colleagues,

Developments in macro-econometrics have been evolving since the aftermath of the Second World War. Essentially, macro-econometrics benefited from the development of mathematical, statistical, and econometric tools. Such a research program attained a meaningful success; as the methods of macro-econometrics have rapidly been used to check the implications of economic theories, forecast business cycles, and to provide advice to policymakers. More recently, as macro-econometrics has been at the center of the debate between economists and statisticians, several extensions to this research program have been proposed, with different interesting challenges. Accordingly, macro-econometricians have applied novel methods (panel data, instrumental variables, generalized method of moments (GMM), time series analysis, various simulations and computational algorithms, frequentist and Bayesian estimation of dynamic stochastic general equilibrium (DSGE) models, etc.) to appropriate versions of macro-economic models.

However, despite this progress, important methodological and interpretative questions in macro-econometrics remain. Additionally, the recent global financial crisis and the subsequent economic recession (2007–2008) that highlighted the end of the Great Moderation suggested at least two limitations. On the one hand, it seems that current macroeconomics models were not able to forecast this economic down-turn, perhaps because they did not take into account an indicator of systemic risk, nor a measure for the financial cycle. On the other hand, it seems that something was going wrong. Indeed, several new lines of research pointed to the importance of measurement errors in macroeconomic and financial variables (Barnett, 2012). Additionally, it appears that monetary rules and macroeconomic models were mis-specified, and the pre-crisis generation of DSGE models had ignored many features of the real world, which should have been incorporated in the structure (e.g., banks and financial sector, heterogeneities, imperfect information, etc.) and behavioral assumptions (e.g., expectation formation and learning, rational inattention, etc.) of these models. Consequently, a growing recent research agenda has been defined to tackle these questions.

This Special Issue aims to focus on the recent developments in macroeconomic modeling and macro-econometric techniques that have been introduced to extend the original framework of macro-econometrics. Accordingly, this Special Issue will present theoretical, methodological, as well as empirical, research in macro-econometrics. It will, thus, provide a concise but authoritative update on the recent developments of macro-econometric models.

Prof. Dr. Gilles Dufrénot
Dr. Fredj Jawadi
Dr. Alexander Mihailov
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 submissions that pass pre-check are 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 1400 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.

 

Keywords

  • Macroeconomics
  • Macro-econometrics
  • Monetary Policy
  • Macro-financial dynamics

 

References

Barnett, W., A. (2012), Getting It Wrong: How Faulty Monetary Statistics Undermine the Fed, the Financial System, and the Economy. Cambridge, MA: MIT Press, 322 pp. ISBN: 978-0-262-51688-4.

Del Negro, Marco and Frank Schorfheide (2011), Bayesian Macroeconometrics, in John Geweke, Gary Koop and Herman van Dijk (eds.), The Oxford Handbook of Bayesian Econometrics, Oxford: Oxford University Press (Ch. 7).

Schorfheide, Frank (2013), Estimation and Evaluation of DSGE Models: Progress and Challenges, Advances in Economics and Econometrics, Volume 3, Number 51, p. 184–230

Stock, J., H. (2001), Essays Macro-econometrics, Journal of Econometrics, 100, 29-32.

Published Papers (10 papers)

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Editorial

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5 pages, 191 KiB  
Editorial
Recent Developments in Macro-Econometric Modeling: Theory and Applications
by Gilles Dufrénot, Fredj Jawadi and Alexander Mihailov
Econometrics 2018, 6(2), 25; https://doi.org/10.3390/econometrics6020025 - 14 May 2018
Viewed by 7929
Abstract
Developments in macro-econometrics have been evolving since the aftermath of the Second World War.[...] Full article
(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)
5050 KiB  
Editorial
An Interview with William A. Barnett
by Apostolos Serletis
Econometrics 2017, 5(4), 45; https://doi.org/10.3390/econometrics5040045 - 17 Oct 2017
Cited by 1 | Viewed by 7987
Abstract
William(Bill) Barnett is an eminent econometrician andmacroeconomist.[...] Full article
(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)

Research

Jump to: Editorial

25 pages, 393 KiB  
Article
Forecasting Inflation Uncertainty in the G7 Countries
by Mawuli Segnon, Stelios Bekiros and Bernd Wilfling
Econometrics 2018, 6(2), 23; https://doi.org/10.3390/econometrics6020023 - 27 Apr 2018
Viewed by 7359
Abstract
There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth Transition AutoRegressive Fractionally Integrated Moving Average-Markov Switching Multifractal specification [ STARFIMA ( p , d , q ) - [...] Read more.
There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth Transition AutoRegressive Fractionally Integrated Moving Average-Markov Switching Multifractal specification [ STARFIMA ( p , d , q ) - MSM ( k ) ] for modeling and forecasting inflation uncertainty. We first provide the statistical properties of the process and investigate the finite sample properties of the maximum likelihood estimators through simulation. Second, we evaluate the out-of-sample forecast performance of the model in forecasting inflation uncertainty in the G7 countries. Our empirical analysis demonstrates the superiority of the new model over the alternative STARFIMA ( p , d , q ) - GARCH -type models in forecasting inflation uncertainty. Full article
(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)
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20 pages, 360 KiB  
Article
Estimating Unobservable Inflation Expectations in the New Keynesian Phillips Curve
by Francesca Rondina
Econometrics 2018, 6(1), 6; https://doi.org/10.3390/econometrics6010006 - 05 Feb 2018
Cited by 3 | Viewed by 8313
Abstract
This paper uses an econometric model and Bayesian estimation to reverse engineer the path of inflation expectations implied by the New Keynesian Phillips Curve and the data. The estimated expectations roughly track the patterns of a number of common measures of expected inflation [...] Read more.
This paper uses an econometric model and Bayesian estimation to reverse engineer the path of inflation expectations implied by the New Keynesian Phillips Curve and the data. The estimated expectations roughly track the patterns of a number of common measures of expected inflation available from surveys or computed from financial data. In particular, they exhibit the strongest correlation with the inflation forecasts of the respondents in the University of Michigan Survey of Consumers. The estimated model also shows evidence of the anchoring of long run inflation expectations to a value that is in the range of the target inflation rate. Full article
(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)
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884 KiB  
Article
Do Seasonal Adjustments Induce Noncausal Dynamics in Inflation Rates?
by Alain Hecq, Sean Telg and Lenard Lieb
Econometrics 2017, 5(4), 48; https://doi.org/10.3390/econometrics5040048 - 31 Oct 2017
Cited by 9 | Viewed by 7737
Abstract
This paper investigates the effect of seasonal adjustment filters on the identification of mixed causal-noncausal autoregressive models. By means of Monte Carlo simulations, we find that standard seasonal filters induce spurious autoregressive dynamics on white noise series, a phenomenon already documented in the [...] Read more.
This paper investigates the effect of seasonal adjustment filters on the identification of mixed causal-noncausal autoregressive models. By means of Monte Carlo simulations, we find that standard seasonal filters induce spurious autoregressive dynamics on white noise series, a phenomenon already documented in the literature. Using a symmetric argument, we show that those filters also generate a spurious noncausal component in the seasonally adjusted series, but preserve (although amplify) the existence of causal and noncausal relationships. This result has has important implications for modelling economic time series driven by expectation relationships. We consider inflation data on the G7 countries to illustrate these results. Full article
(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)
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2254 KiB  
Article
Twenty-Two Years of Inflation Assessment and Forecasting Experience at the Bulletin of EU & US Inflation and Macroeconomic Analysis
by Antoni Espasa and Eva Senra
Econometrics 2017, 5(4), 44; https://doi.org/10.3390/econometrics5040044 - 06 Oct 2017
Cited by 1 | Viewed by 7786
Abstract
The Bulletin of EU & US Inflation and Macroeconomic Analysis (BIAM) is a monthly publication that has been reporting real time analysis and forecasts for inflation and other macroeconomic aggregates for the Euro Area, the US and Spain since 1994. The BIAM inflation [...] Read more.
The Bulletin of EU & US Inflation and Macroeconomic Analysis (BIAM) is a monthly publication that has been reporting real time analysis and forecasts for inflation and other macroeconomic aggregates for the Euro Area, the US and Spain since 1994. The BIAM inflation forecasting methodology stands on working with useful disaggregation schemes, using leading indicators when possible and applying outlier correction. The paper relates this methodology to corresponding topics in the literature and discusses the design of disaggregation schemes. It concludes that those schemes would be useful if they were formulated according to economic, institutional and statistical criteria aiming to end up with a set of components with very different statistical properties for which valid single-equation models could be built. The BIAM assessment, which derives from a new observation, is based on (a) an evaluation of the forecasting errors (innovations) at the components’ level. It provides information on which sectors they come from and allows, when required, for the appropriate correction in the specific models. (b) In updating the path forecast with its corresponding fan chart. Finally, we show that BIAM real time Euro Area inflation forecasts compare successfully with the consensus from the ECB Survey of Professional Forecasters, one and two years ahead. Full article
(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)
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1204 KiB  
Article
Evaluating Ingenious Instruments for Fundamental Determinants of Long-Run Economic Growth and Development
by P. Dorian Owen
Econometrics 2017, 5(3), 38; https://doi.org/10.3390/econometrics5030038 - 05 Sep 2017
Cited by 10 | Viewed by 10233
Abstract
Empirical studies of the determinants of cross-country differences in long-run development are characterized by the ingenious nature of the instruments used. However, scepticism remains about their ability to provide a valid basis for causal inference. This paper examines whether explicit consideration of the [...] Read more.
Empirical studies of the determinants of cross-country differences in long-run development are characterized by the ingenious nature of the instruments used. However, scepticism remains about their ability to provide a valid basis for causal inference. This paper examines whether explicit consideration of the statistical adequacy of the underlying reduced form, which provides an embedding framework for the structural equations, can usefully complement economic theory as a basis for assessing instrument choice in the fundamental determinants literature. Diagnostic testing of the reduced forms in influential studies reveals evidence of model misspecification, with parameter non-constancy and spatial dependence of the residuals being almost ubiquitous. This feature, surprisingly not previously identified, potentially undermines the inferences drawn about the structural parameters, such as the quantitative and statistical significance of different fundamental determinants. Full article
(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)
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292 KiB  
Article
Endogeneity, Time-Varying Coefficients, and Incorrect vs. Correct Ways of Specifying the Error Terms of Econometric Models
by P.A.V.B. Swamy, Jatinder S. Mehta and I-Lok Chang
Econometrics 2017, 5(1), 8; https://doi.org/10.3390/econometrics5010008 - 03 Feb 2017
Cited by 3 | Viewed by 7913
Abstract
Using the net effect of all relevant regressors omitted from a model to form its error term is incorrect because the coefficients and error term of such a model are non-unique. Non-unique coefficients cannot possess consistent estimators. Uniqueness can be achieved if; instead; [...] Read more.
Using the net effect of all relevant regressors omitted from a model to form its error term is incorrect because the coefficients and error term of such a model are non-unique. Non-unique coefficients cannot possess consistent estimators. Uniqueness can be achieved if; instead; one uses certain “sufficient sets” of (relevant) regressors omitted from each model to represent the error term. In this case; the unique coefficient on any non-constant regressor takes the form of the sum of a bias-free component and omitted-regressor biases. Measurement-error bias can also be incorporated into this sum. We show that if our procedures are followed; accurate estimation of bias-free components is possible. Full article
(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)
663 KiB  
Article
Between Institutions and Global Forces: Norwegian Wage Formation Since Industrialisation
by Ragnar Nymoen
Econometrics 2017, 5(1), 6; https://doi.org/10.3390/econometrics5010006 - 12 Jan 2017
Cited by 6 | Viewed by 10044
Abstract
This paper reviews the development of labour market institutions in Norway, shows how labour market regulation has been related to the macroeconomic development, and presents dynamic econometric models of nominal and real wages. Single equation and multi-equation models are reported. The econometric modelling [...] Read more.
This paper reviews the development of labour market institutions in Norway, shows how labour market regulation has been related to the macroeconomic development, and presents dynamic econometric models of nominal and real wages. Single equation and multi-equation models are reported. The econometric modelling uses a new data set with historical time series of wages and prices, unemployment and labour productivity. Impulse indicator saturation is used to achieve robust estimation of focus parameters, and the breaks are interpreted in the light of the historical overview. A relatively high degree of constancy of the key parameters of the wage setting equation is documented, over a considerably longer historical time period than earlier studies have done. The evidence is consistent with the view that the evolving system of collective labour market regulation over long periods has delivered a certain necessary level of coordination of wage and price setting. Nevertheless, there is also evidence that global forces have been at work for a long time, in a way that links real wages to productivity trends in the same way as in countries with very different institutions and macroeconomic development. Full article
(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)
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1425 KiB  
Article
Business Cycle Estimation with High-Pass and Band-Pass Local Polynomial Regression
by Luis J. Álvarez
Econometrics 2017, 5(1), 1; https://doi.org/10.3390/econometrics5010001 - 05 Jan 2017
Cited by 5 | Viewed by 8081
Abstract
Filters constructed on the basis of standard local polynomial regression (LPR) methods have been used in the literature to estimate the business cycle. We provide a frequency domain interpretation of the contrast filter obtained by the difference of a series and its long-run [...] Read more.
Filters constructed on the basis of standard local polynomial regression (LPR) methods have been used in the literature to estimate the business cycle. We provide a frequency domain interpretation of the contrast filter obtained by the difference of a series and its long-run LPR component and show that it operates as a kind of high-pass filter, so that it provides a noisy estimate of the cycle. We alternatively propose band-pass local polynomial regression methods aimed at isolating the cyclical component. Results are compared to standard high-pass and band-pass filters. Procedures are illustrated using the US GDP series. Full article
(This article belongs to the Special Issue Recent Developments in Macro-Econometric Modeling: Theory and Applications)
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