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Econometrics, Volume 9, Issue 1 (March 2021) – 14 articles

Cover Story (view full-size image): Time series of counts enjoy numerous applications in various fields as economics, engineering, and epidemiology. This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. An application on a real data set of claims counts is provided. View this paper
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Article
Estimating Endogenous Treatment Effects Using Latent Factor Models with and without Instrumental Variables
Econometrics 2021, 9(1), 14; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010014 - 17 Mar 2021
Viewed by 1192
Abstract
We provide evidence on the least biased ways to identify causal effects in situations where there are multiple outcomes that all depend on the same endogenous regressor and a reasonable but potentially contaminated instrumental variable that is available. Simulations provide suggestive evidence on [...] Read more.
We provide evidence on the least biased ways to identify causal effects in situations where there are multiple outcomes that all depend on the same endogenous regressor and a reasonable but potentially contaminated instrumental variable that is available. Simulations provide suggestive evidence on the complementarity of instrumental variable (IV) and latent factor methods and how this complementarity depends on the number of outcome variables and the degree of contamination in the IV. We apply the causal inference methods to assess the impact of mental illness on work absenteeism and disability, using the National Comorbidity Survey Replication. Full article
(This article belongs to the Special Issue Health Econometrics)
Article
Integration and Disintegration of EMU Government Bond Markets
Econometrics 2021, 9(1), 13; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010013 - 15 Mar 2021
Viewed by 733
Abstract
It is commonly found that the markets for long-term government bonds of Economic and Monetary Union (EMU) countries were integrated prior to the EMU debt crisis. Contrasting this, we show, based on the interrelation between market integration and fractional cointegration, that there were [...] Read more.
It is commonly found that the markets for long-term government bonds of Economic and Monetary Union (EMU) countries were integrated prior to the EMU debt crisis. Contrasting this, we show, based on the interrelation between market integration and fractional cointegration, that there were periods of integration and disintegration that coincide with bull and bear market periods in the stock market. An econometric argument about the spectral behavior of long-memory time series leads to the conclusion that there is a stronger differentiation between bonds with different default risks. This implied the possibility of macroeconomic and fiscal divergence between the EMU countries before the crisis periods. Full article
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Article
Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions
Econometrics 2021, 9(1), 12; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010012 - 13 Mar 2021
Viewed by 843
Abstract
This paper develops residual-based monitoring procedures for cointegrating polynomial regressions (CPRs), i.e., regression models including deterministic variables and integrated processes, as well as integer powers, of integrated processes as regressors. The regressors are allowed to be endogenous, and the stationary errors are allowed [...] Read more.
This paper develops residual-based monitoring procedures for cointegrating polynomial regressions (CPRs), i.e., regression models including deterministic variables and integrated processes, as well as integer powers, of integrated processes as regressors. The regressors are allowed to be endogenous, and the stationary errors are allowed to be serially correlated. We consider five variants of monitoring statistics and develop the results for three modified least squares estimators for the parameters of the CPRs. The simulations show that using the combination of self-normalization and a moving window leads to the best performance. We use the developed monitoring statistics to assess the structural stability of environmental Kuznets curves (EKCs) for both CO2 and SO2 emissions for twelve industrialized countries since the first oil price shock. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Article
New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?
Econometrics 2021, 9(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010011 - 06 Mar 2021
Viewed by 896
Abstract
We assess the forecasting performance of the nowcasting model developed at the New York FED. We show that the observation regarding a striking difference in the model’s predictive ability across business cycle phases made earlier in the literature also applies here. During expansions, [...] Read more.
We assess the forecasting performance of the nowcasting model developed at the New York FED. We show that the observation regarding a striking difference in the model’s predictive ability across business cycle phases made earlier in the literature also applies here. During expansions, the nowcasting model forecasts at best are at least as good as the historical mean model, whereas during the recessionary periods, there are very substantial gains corresponding in the reduction in MSFE of about 90% relative to the benchmark model. We show how the asymmetry in the relative forecasting performance can be verified by the use of such recursive measures of relative forecast accuracy as Cumulated Sum of Squared Forecast Error Difference (CSSFED) and Recursive Relative Mean Squared Forecast Error (based on Rearranged observations) (R2MSFE(+R)). Ignoring these asymmetries results in a biased judgement of the relative forecasting performance of the competing models over a sample as a whole, as well as during economic expansions, when the forecasting accuracy of a more sophisticated model relative to naive benchmark models tends to be overstated. Hence, care needs to be exercised when ranking several models by their forecasting performance without taking into consideration various states of the economy. Full article
(This article belongs to the Special Issue Special Issue on Economic Forecasting)
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Article
Goodness–of–Fit Tests for Bivariate Time Series of Counts
Econometrics 2021, 9(1), 10; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010010 - 04 Mar 2021
Viewed by 878
Abstract
This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric [...] Read more.
This article considers goodness-of-fit tests for bivariate INAR and bivariate Poisson autoregression models. The test statistics are based on an L2-type distance between two estimators of the probability generating function of the observations: one being entirely nonparametric and the second one being semiparametric computed under the corresponding null hypothesis. The asymptotic distribution of the proposed tests statistics both under the null hypotheses as well as under alternatives is derived and consistency is proved. The case of testing bivariate generalized Poisson autoregression and extension of the methods to dimension higher than two are also discussed. The finite-sample performance of a parametric bootstrap version of the tests is illustrated via a series of Monte Carlo experiments. The article concludes with applications on real data sets and discussion. Full article
(This article belongs to the Special Issue Discrete-Valued Time Series: Modelling, Estimation and Forecasting)
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Article
Temperature Anomalies, Long Memory, and Aggregation
Econometrics 2021, 9(1), 9; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010009 - 03 Mar 2021
Viewed by 916
Abstract
Econometric studies for global heating have typically used regional or global temperature averages to study its long memory properties. One typical explanation behind the long memory properties of temperature averages is cross-sectional aggregation. Nonetheless, formal analysis regarding the effect that aggregation has on [...] Read more.
Econometric studies for global heating have typically used regional or global temperature averages to study its long memory properties. One typical explanation behind the long memory properties of temperature averages is cross-sectional aggregation. Nonetheless, formal analysis regarding the effect that aggregation has on the long memory dynamics of temperature data has been missing. Thus, this paper studies the long memory properties of individual grid temperatures and compares them against the long memory dynamics of global and regional averages. Our results show that the long memory parameters in individual grid observations are smaller than those from regional averages. Global and regional long memory estimates are greatly affected by temperature measurements at the Tropics, where the data is less reliable. Thus, this paper supports the notion that aggregation may be exacerbating the long memory estimated in regional and global temperature data. The results are robust to the bandwidth parameter, limit for station radius of influence, and sampling frequency. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Article
Hospital Emergency Room Savings via Health Line S24 in Portugal
Econometrics 2021, 9(1), 8; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010008 - 20 Feb 2021
Viewed by 916
Abstract
Hospital emergency departments are often overused by patients that do not really need urgent care. These admissions are one of the major factors contributing to hospital costs, which should not be allowed to compromise the response and effectiveness of the National Health Services [...] Read more.
Hospital emergency departments are often overused by patients that do not really need urgent care. These admissions are one of the major factors contributing to hospital costs, which should not be allowed to compromise the response and effectiveness of the National Health Services (SNS). The aim of this study is to perform a detailed spatial health econometrics analysis of the non-urgent emergency situations (classified by Manchester triage) by area, linking them with the efficient use of the national health line, the Saude24 line (S24 line). This is evaluated through the S24 savings calls, using a savings index and its spatial effectiveness in solving the non-urgent emergency situations. A savings call is a call by a user whose initial intention was to go to an urgency department, but who. after calling the S24 line. changed his/her mind. Given the spatial nature of the data, and resorting to INLA in a Bayesian paradigm, the number of non-urgent cases in the Portuguese urgency hospital departments is modeled in an autoregressive way. The spatial structure is accounted for by a set of random effects. The model additionally includes regular covariates and a spatially lagged covariate savings index, related with the S24 savings calls. Therefore, the response in a given area depends not only on the (weighted) values of the response in its neighborhood and of the considered covariates, but also on the (weighted) values of the covariate savings index measured in each neighbor, by means of a Bayesian Poisson spatial Durbin model. Full article
(This article belongs to the Special Issue Health Econometrics)
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Erratum
Erratum: Hoover, K.D. 2020. The Discovery of Long-Run Causal Order: A Preliminary Investigation. Econometrics 8: 31
Econometrics 2021, 9(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010007 - 18 Feb 2021
Viewed by 992
Abstract
The author would like to make the following correction to the article by Kevin D [...] Full article
(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)
Article
Nonlinear Cointegrating Regression of the Earth’s Surface Mean Temperature Anomalies on Total Radiative Forcing
Econometrics 2021, 9(1), 6; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010006 - 08 Feb 2021
Viewed by 1099
Abstract
This study proposes a nonlinear cointegrating regression model based on the well-known energy balance climate model. Specifically, I investigate the nonlinear cointegrating regression of the mean of temperature anomaly distributions on total radiative forcing using estimated spatial distributions of temperature anomalies for the [...] Read more.
This study proposes a nonlinear cointegrating regression model based on the well-known energy balance climate model. Specifically, I investigate the nonlinear cointegrating regression of the mean of temperature anomaly distributions on total radiative forcing using estimated spatial distributions of temperature anomalies for the Globe, Northern Hemisphere, and Southern Hemisphere. Further, I provide two types of nonlinear response functions that map the total radiative forcing level to mean temperature anomalies. The proposed statistical model provides a climatological implication that spatially heterogenous warming effects play a significant role in identifying nonlinear climate sensitivity. Cointegration and specification tests are provided that support the existence of nonlinear effects of total radiative forcing. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Article
Searching for a Theory That Fits the Data: A Personal Research Odyssey
Econometrics 2021, 9(1), 5; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010005 - 01 Feb 2021
Cited by 3 | Viewed by 1195
Abstract
This survey paper discusses the Cointegrated Vector AutoRegressive (CVAR) methodology and how it has evolved over the past 30 years. It describes major steps in the econometric development, discusses problems to be solved when confronting theory with the data, and, as a solution, [...] Read more.
This survey paper discusses the Cointegrated Vector AutoRegressive (CVAR) methodology and how it has evolved over the past 30 years. It describes major steps in the econometric development, discusses problems to be solved when confronting theory with the data, and, as a solution, proposes a so-called theory-consistent CVAR scenario. A number of early CVAR applications are motivated by the urge to find out why the empirical results did not support Milton Friedman’s concept of monetary inflation. The paper also proposes a method for combining partial CVAR analyses into a large-scale macroeconomic model. It argues that an empirically-based approach to macroeconomics preferably should be based on Keynesian disequilibrium economics, where imperfect knowledge expectations replace so called rational expectations and where the financial sector plays a key role for understanding the long persistent movements in the data. Finally, the paper argues that the CVAR is potentially a candidate for Haavelmo’s “design of experiment for passive observations” and provides several illustrations. Full article
(This article belongs to the Special Issue Celebrated Econometricians: Katarina Juselius and Søren Johansen)
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Editorial
Acknowledgment to Reviewers of Econometrics in 2020
Econometrics 2021, 9(1), 4; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010004 - 21 Jan 2021
Viewed by 763
Abstract
Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Econometrics maintains its standards for the high quality of its published papers [...] Full article
Article
Enhanced Methods of Seasonal Adjustment
Econometrics 2021, 9(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010003 - 05 Jan 2021
Cited by 1 | Viewed by 1188
Abstract
The effect of the conventional model-based methods of seasonal adjustment is to nullify the elements of the data that reside at the seasonal frequencies and to attenuate the elements at the adjacent frequencies. It may be desirable to nullify some of the adjacent [...] Read more.
The effect of the conventional model-based methods of seasonal adjustment is to nullify the elements of the data that reside at the seasonal frequencies and to attenuate the elements at the adjacent frequencies. It may be desirable to nullify some of the adjacent elements instead of merely attenuating them. For this purpose, two alternative sets of procedures are presented that have been implemented in a computer program named SEASCAPE. In the first set of procedures, a basic seasonal adjustment filter is augmented by additional filters that are targeted at the adjacent frequencies. In the second set of procedures, a Fourier transform of the data is exploited to allow the elements in the vicinities of the seasonal frequencies to be eliminated or attenuated at will. The question is raised of whether an estimated trend-cycle trajectory that is devoid of high-frequency noise can serve in place of the seasonally adjusted data. Full article
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Editorial
Towards a New Paradigm for Statistical Evidence in the Use of p-Value
Econometrics 2021, 9(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010002 - 31 Dec 2020
Viewed by 1195
Abstract
As the guest editors of this Special Issue, we feel proud and grateful to write the editorial note of this issue, which consists of seven high-quality research papers [...] Full article
(This article belongs to the Special Issue Towards a New Paradigm for Statistical Evidence)
Article
Regularized Maximum Diversification Investment Strategy
Econometrics 2021, 9(1), 1; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9010001 - 29 Dec 2020
Cited by 1 | Viewed by 897
Abstract
The maximum diversification has been shown in the literature to depend on the vector of asset volatilities and the inverse of the covariance matrix of the asset return. In practice, these two quantities need to be replaced by their sample statistics. The estimation [...] Read more.
The maximum diversification has been shown in the literature to depend on the vector of asset volatilities and the inverse of the covariance matrix of the asset return. In practice, these two quantities need to be replaced by their sample statistics. The estimation error associated with the use of these sample statistics may be amplified due to (near) singularity of the covariance matrix, in financial markets with many assets. This, in turn, may lead to the selection of portfolios that are far from the optimal regarding standard portfolio performance measures of the financial market. To address this problem, we investigate three regularization techniques, including the ridge, the spectral cut-off, and the Landweber–Fridman approaches in order to stabilize the inverse of the covariance matrix. These regularization schemes involve a tuning parameter that needs to be chosen. In light of this fact, we propose a data-driven method for selecting the tuning parameter. We show that the selected portfolio by regularization is asymptotically efficient with respect to the diversification ratio. In empirical and Monte Carlo experiments, the resulting regularized rules are compared to several strategies, such as the most diversified portfolio, the target portfolio, the global minimum variance portfolio, and the naive 1/N strategy in terms of in-sample and out-of-sample Sharpe ratio performance, and it is shown that our method yields significant Sharpe ratio improvements. Full article
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