Econometrics

21 pages, 673 KiB

Open AccessArticle

by S. Yanki Kalfa and Jaime Marquez

Econometrics 2021, 9(3), 34; https://doi.org/10.3390/econometrics9030034 - 14 Sep 2021

Cited by 2 | Viewed by 4132

(Hendry 1980, p. 403) The three golden rules of econometrics are “test, test, and test”. The current paper applies that approach to model the forecasts of the Federal Open Market Committee over 1992–2019 and to forecast those forecasts themselves. Monetary policy is forward-looking, [...] Read more.

(Hendry 1980, p. 403) The three golden rules of econometrics are “test, test, and test”. The current paper applies that approach to model the forecasts of the Federal Open Market Committee over 1992–2019 and to forecast those forecasts themselves. Monetary policy is forward-looking, and as part of the FOMC’s effort toward transparency, the FOMC publishes its (forward-looking) economic projections. The overall views on the economy of the FOMC participants–as characterized by the median of their projections for inflation, unemployment, and the Fed’s policy rate–are themselves predictable by information publicly available at the time of the FOMC’s meeting. Their projections also communicate systematic behavior on the part of the FOMC’s participants. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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18 pages, 6811 KiB

Open AccessArticle

On Spurious Causality, CO₂, and Global Temperature

by Philippe Goulet Coulombe and Maximilian Göbel

Econometrics 2021, 9(3), 33; https://doi.org/10.3390/econometrics9030033 - 7 Sep 2021

Cited by 4 | Viewed by 5954

Abstract

Stips et al. (2016) use information flows (Liang (2008, 2014)) to establish causality from various forcings to global temperature. We show that the formulas being used hinge on a simplifying assumption that is nearly always rejected by the data. We propose the well-known [...] Read more.

Stips et al. (2016) use information flows (Liang (2008, 2014)) to establish causality from various forcings to global temperature. We show that the formulas being used hinge on a simplifying assumption that is nearly always rejected by the data. We propose the well-known forecast error variance decomposition based on a Vector Autoregression as an adequate measure of information flow, and find that most results in Stips et al. (2016) cannot be corroborated. Then, we discuss which modeling choices (e.g., the choice of CO

_{2}

series and assumptions about simultaneous relationships) may help in extracting credible estimates of causal flows and the transient climate response simply by looking at the joint dynamics of two climatic time series. Full article

(This article belongs to the Collection Econometric Analysis of Climate Change)

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6 pages, 227 KiB

Open AccessCommunication

Prais–Winsten Algorithm for Regression with Second or Higher Order Autoregressive Errors

by Dimitrios V. Vougas

Econometrics 2021, 9(3), 32; https://doi.org/10.3390/econometrics9030032 - 27 Aug 2021

Cited by 3 | Viewed by 4722

Abstract

There is no available Prais–Winsten algorithm for regression with AR(2) or higher order errors, and the one with AR(1) errors is not fully justified or is implemented incorrectly (thus being inefficient). This paper addresses both issues, providing an accurate, computationally fast, and inexpensive [...] Read more.

There is no available Prais–Winsten algorithm for regression with AR(2) or higher order errors, and the one with AR(1) errors is not fully justified or is implemented incorrectly (thus being inefficient). This paper addresses both issues, providing an accurate, computationally fast, and inexpensive generic zig-zag algorithm. Full article

27 pages, 469 KiB

Open AccessEditor’s ChoiceArticle

Cointegration, Root Functions and Minimal Bases

by Massimo Franchi and Paolo Paruolo

Econometrics 2021, 9(3), 31; https://doi.org/10.3390/econometrics9030031 - 17 Aug 2021

Cited by 2 | Viewed by 3127

Abstract

This paper discusses the notion of cointegrating space for linear processes integrated of any order. It first shows that the notions of (polynomial) cointegrating vectors and of root functions coincide. Second, it discusses how the cointegrating space can be defined (i) as a [...] Read more.

This paper discusses the notion of cointegrating space for linear processes integrated of any order. It first shows that the notions of (polynomial) cointegrating vectors and of root functions coincide. Second, it discusses how the cointegrating space can be defined (i) as a vector space of polynomial vectors over complex scalars, (ii) as a free module of polynomial vectors over scalar polynomials, or finally (iii) as a vector space of rational vectors over rational scalars. Third, it shows that a canonical set of root functions can be used as a basis of the various notions of cointegrating space. Fourth, it reviews results on how to reduce polynomial bases to minimal order—i.e., minimal bases. The application of these results to Vector AutoRegressive processes integrated of order 2 is found to imply the separation of polynomial cointegrating vectors from non-polynomial ones. Full article

(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)

28 pages, 979 KiB

Open AccessArticle

Søren Johansen and Katarina Juselius: A Bibliometric Analysis of Citations through Multivariate Bass Models

by Fragiskos Archontakis and Rocco Mosconi

Econometrics 2021, 9(3), 30; https://doi.org/10.3390/econometrics9030030 - 12 Aug 2021

Cited by 5 | Viewed by 3734

Abstract

We showcase the impact of Katarina Juselius and Søren Johansen’s contribution to econometrics using bibliometric data on citations from 1989 to 2017, extracted from the Web of Science (WoS) database. Our purpose is to analyze the impact of KJ and SJ’s ideas on [...] Read more.

We showcase the impact of Katarina Juselius and Søren Johansen’s contribution to econometrics using bibliometric data on citations from 1989 to 2017, extracted from the Web of Science (WoS) database. Our purpose is to analyze the impact of KJ and SJ’s ideas on applied and methodological research in econometrics. To this aim, starting from WoS data, we derived two composite indices whose purpose is to disentangle the authors’ impact on applied research from their impact on methodological research. As of 2017, the number of applied citing papers per quarter had not yet reached the peak; conversely, the peak in the methodological literature seem to have been reached around 2000, although the shape of the trajectory is very flat after the peak. We analyzed the data using a multivariate dynamic version of the well known Bass model. Our estimates suggest that the methodological literature is mainly driven by “innovators”, whereas “imitators” are relatively more important in the applied literature: this might explain the different location of the peaks. We also find that, in the literature referring to KJ and SJ, the “cross-fertilization” between methodological and applied research is statistically significant and bi-directional. Full article

(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)

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3 pages, 177 KiB

Open AccessEditorial

Special Issue “Celebrated Econometricians: Peter Phillips”

by Federico Bandi, Alex Maynard, Hyungsik Roger Moon and Benoit Perron

Econometrics 2021, 9(3), 29; https://doi.org/10.3390/econometrics9030029 - 27 Jul 2021

Viewed by 2764

Abstract

Peter Phillips has had a tremendous impact on econometric theory and practice [...] Full article

(This article belongs to the Special Issue Celebrated Econometricians: Peter Phillips)

17 pages, 1424 KiB

Open AccessArticle

Multivariate Analysis of Cryptocurrencies

by Vincenzo Candila

Econometrics 2021, 9(3), 28; https://doi.org/10.3390/econometrics9030028 - 1 Jul 2021

Cited by 8 | Viewed by 7525

Abstract

Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are [...] Read more.

Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework. Full article

(This article belongs to the Special Issue Topics in Computational Econometrics and Finance: Theory and Applications)

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35 pages, 4140 KiB

Open AccessArticle

Fisher’s z Distribution-Based Mixture Autoregressive Model

by Arifatus Solikhah, Heri Kuswanto, Nur Iriawan and Kartika Fithriasari

Econometrics 2021, 9(3), 27; https://doi.org/10.3390/econometrics9030027 - 29 Jun 2021

Cited by 6 | Viewed by 4136

Abstract

We generalize the Gaussian Mixture Autoregressive (GMAR) model to the Fisher’s z Mixture Autoregressive (ZMAR) model for modeling nonlinear time series. The model consists of a mixture of K-component Fisher’s z autoregressive models with the mixing proportions changing over time. This model [...] Read more.

We generalize the Gaussian Mixture Autoregressive (GMAR) model to the Fisher’s z Mixture Autoregressive (ZMAR) model for modeling nonlinear time series. The model consists of a mixture of K-component Fisher’s z autoregressive models with the mixing proportions changing over time. This model can capture time series with both heteroskedasticity and multimodal conditional distribution, using Fisher’s z distribution as an innovation in the MAR model. The ZMAR model is classified as nonlinearity in the level (or mode) model because the mode of the Fisher’s z distribution is stable in its location parameter, whether symmetric or asymmetric. Using the Markov Chain Monte Carlo (MCMC) algorithm, e.g., the No-U-Turn Sampler (NUTS), we conducted a simulation study to investigate the model performance compared to the GMAR model and Student t Mixture Autoregressive (TMAR) model. The models are applied to the daily IBM stock prices and the monthly Brent crude oil prices. The results show that the proposed model outperforms the existing ones, as indicated by the Pareto-Smoothed Important Sampling Leave-One-Out cross-validation (PSIS-LOO) minimum criterion. Full article

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35 pages, 516 KiB

Open AccessFeature PaperArticle

Selecting a Model for Forecasting

by Jennifer L. Castle, Jurgen A. Doornik and David F. Hendry

Econometrics 2021, 9(3), 26; https://doi.org/10.3390/econometrics9030026 - 25 Jun 2021

Cited by 9 | Viewed by 5669

Abstract

We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is extended by allowing for a [...] Read more.

We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is extended by allowing for a structural break, either in the first forecast period or just before. Theoretical results are derived for a three-variable static model, but generalized to include dynamics and many more variables in the simulation experiment. The results show that the trade-off for selecting variables in forecasting models in a stationary world, namely that variables should be retained if their noncentralities exceed unity, still applies in settings with structural breaks. This provides support for model selection at looser than conventional settings, albeit with many additional features explaining the forecast performance, and with the caveat that retaining irrelevant variables that are subject to location shifts can worsen forecast performance. Full article

(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)

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Econometrics, Volume 9, Issue 3 (September 2021) – 9 articles

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