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Econometrics, Volume 9, Issue 4 (December 2021) – 11 articles

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Article
Does the Choice of Realized Covariance Measures Empirically Matter? A Bayesian Density Prediction Approach
by , and
Econometrics 2021, 9(4), 45; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040045 (registering DOI) - 06 Dec 2021
Abstract
This paper suggests a new approach to evaluate realized covariance (RCOV) estimators via their predictive power on return density. By jointly modeling returns and RCOV measures under a Bayesian framework, the predictive density of returns and ex-post covariance measures are bridged. The forecast [...] Read more.
This paper suggests a new approach to evaluate realized covariance (RCOV) estimators via their predictive power on return density. By jointly modeling returns and RCOV measures under a Bayesian framework, the predictive density of returns and ex-post covariance measures are bridged. The forecast performance of a covariance estimator can be assessed according to its improvement in return density forecasting. Empirical applications to equity data show that several RCOV estimators consistently perform better than others and emphasize the importance of RCOV selection in covariance modeling and forecasting. Full article
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Article
Interdependency Pattern Recognition in Econometrics: A Penalized Regularization Antidote
by , and
Econometrics 2021, 9(4), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040044 (registering DOI) - 06 Dec 2021
Abstract
When it comes to variable interpretation, multicollinearity is among the biggest issues that must be surmounted, especially in this new era of Big Data Analytics. Since even moderate size multicollinearity can prevent proper interpretation, special diagnostics must be recommended and implemented for identification [...] Read more.
When it comes to variable interpretation, multicollinearity is among the biggest issues that must be surmounted, especially in this new era of Big Data Analytics. Since even moderate size multicollinearity can prevent proper interpretation, special diagnostics must be recommended and implemented for identification purposes. Nonetheless, in the areas of econometrics and statistics, among other fields, these diagnostics are controversial concerning their “successfulness”. It has been remarked that they frequently fail to do proper model assessment due to information complexity, resulting in model misspecification. This work proposes and investigates a robust and easily interpretable methodology, termed Elastic Information Criterion, capable of capturing multicollinearity rather accurately and effectively and thus providing a proper model assessment. The performance is investigated via simulated and real data. Full article
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Article
Climate Finance: Mapping Air Pollution and Finance Market in Time Series
Econometrics 2021, 9(4), 43; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040043 (registering DOI) - 04 Dec 2021
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Abstract
Climate finance is growing popular in addressing challenges of climate change because it controls the funding and resources to emission entities and promotes green manufacturing. In this study, we determined that PM2.5, PM10, SO2, NO2, [...] Read more.
Climate finance is growing popular in addressing challenges of climate change because it controls the funding and resources to emission entities and promotes green manufacturing. In this study, we determined that PM2.5, PM10, SO2, NO2, CO, and O3 are the target pollutant in the atmosphere and we use a deep neural network to enhance the regression analysis in order to investigate the relationship between air pollution and stock prices of the targeted manufacturer. We also conduct time series analysis based on air pollution and heavy industry manufacturing in China, as the country is facing serious air pollution problems. Our study uses Convolutional-Long Short Term Memory in 2 Dimension (ConvLSTM2D) to extract the features from air pollution and enhance the time series regression in the financial market. The main contribution in our paper is discovering a feature term that impacts the stock price in the financial market, particularly for the companies that are highly impacted by the local environment. We offer a higher accurate model than the traditional time series in the stock price prediction by considering the environmental factor. The experimental results suggest that there is a negative linear relationship between air pollution and the stock market, which demonstrates that air pollution has a negative effect on the financial market. It promotes the manufacturer’s improving their emission recycling and encourages them to invest in green manufacture—otherwise, the drop in stock price will impact the company funding process. Full article
(This article belongs to the Special Issue Econometric Analysis of Climate Change)
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Article
Children’s Health Capital Investment: Effects of U.S. Infant Breastfeeding on Teenage Obesity
Econometrics 2021, 9(4), 42; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040042 - 29 Nov 2021
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Abstract
Obesity, as a health and social problem with rising prevalence and soaring economic cost, is increasingly drawing scholarly and public policy attention. While many studies have suggested that infant breastfeeding protects against childhood obesity, empirical evidence on this causal relationship is fragile. Using [...] Read more.
Obesity, as a health and social problem with rising prevalence and soaring economic cost, is increasingly drawing scholarly and public policy attention. While many studies have suggested that infant breastfeeding protects against childhood obesity, empirical evidence on this causal relationship is fragile. Using the health capital development theory, this study exploited multiple data sources from the U.S. and a three-way error components model (ECM) with a jackknife resampling plan to estimate the effect of in-hospital breastfeeding initiation and breastfeeding for durations of 3, 6, and 12 months on the prevalence of obesity during teenage years. The main finding was that a 1% rise in the in-hospital breastfeeding initiation rate reduces the teenage obesity prevalence rate by 1.7% (9.6% of a standard deviation). The magnitude of this effect declines as the infant breastfeeding duration lengthens—e.g., the 12-month infant breastfeeding duration rate is associated with a 0.53% (3.7% of a standard deviation) reduction in obesity prevalence in the teenage years (9th to 12th grades). The study findings agree with both the behavioral and physiological theories on the long-term effects of breastfeeding, and have timely implications for public policies promoting infant breastfeeding to reduce the economic burden of teenage and later adult-stage obesity prevalence rates. Full article
Article
Second-Order Least Squares Estimation in Nonlinear Time Series Models with ARCH Errors
Econometrics 2021, 9(4), 41; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040041 - 27 Nov 2021
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Abstract
Many financial and economic time series exhibit nonlinear patterns or relationships. However, most statistical methods for time series analysis are developed for mean-stationary processes that require transformation, such as differencing of the data. In this paper, we study a dynamic regression model with [...] Read more.
Many financial and economic time series exhibit nonlinear patterns or relationships. However, most statistical methods for time series analysis are developed for mean-stationary processes that require transformation, such as differencing of the data. In this paper, we study a dynamic regression model with nonlinear, time-varying mean function, and autoregressive conditionally heteroscedastic errors. We propose an estimation approach based on the first two conditional moments of the response variable, which does not require specification of error distribution. Strong consistency and asymptotic normality of the proposed estimator is established under strong-mixing condition, so that the results apply to both stationary and mean-nonstationary processes. Moreover, the proposed approach is shown to be superior to the commonly used quasi-likelihood approach and the efficiency gain is significant when the (conditional) error distribution is asymmetric. We demonstrate through a real data example that the proposed method can identify a more accurate model than the quasi-likelihood method. Full article
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Article
Estimating the Competitive Storage Model with Stochastic Trends in Commodity Prices
Econometrics 2021, 9(4), 40; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040040 - 05 Nov 2021
Viewed by 320
Abstract
We propose a State-Space Model (SSM) for commodity prices that combines the competitive storage model with a stochastic trend. This approach fits into the economic rationality of storage decisions and adds to previous deterministic trend specifications of the storage model. For a Bayesian [...] Read more.
We propose a State-Space Model (SSM) for commodity prices that combines the competitive storage model with a stochastic trend. This approach fits into the economic rationality of storage decisions and adds to previous deterministic trend specifications of the storage model. For a Bayesian posterior analysis of the SSM, which is nonlinear in the latent states, we used a Markov chain Monte Carlo algorithm based on the particle marginal Metropolis–Hastings approach. An empirical application to four commodity markets showed that the stochastic trend SSM is favored over deterministic trend specifications. The stochastic trend SSM identifies structural parameters that differ from those for deterministic trend specifications. In particular, the estimated price elasticities of demand are typically larger under the stochastic trend SSM. Full article
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Article
Nonfractional Long-Range Dependence: Long Memory, Antipersistence, and Aggregation
Econometrics 2021, 9(4), 39; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040039 (registering DOI) - 19 Oct 2021
Viewed by 377
Abstract
This paper used cross-sectional aggregation as the inspiration for a model with long-range dependence that arises in actual data. One of the advantages of our model is that it is less brittle than fractionally integrated processes. In particular, we showed that the antipersistent [...] Read more.
This paper used cross-sectional aggregation as the inspiration for a model with long-range dependence that arises in actual data. One of the advantages of our model is that it is less brittle than fractionally integrated processes. In particular, we showed that the antipersistent phenomenon is not present for the cross-sectionally aggregated process. We proved that this has implications for estimators of long-range dependence in the frequency domain, which will be misspecified for nonfractional long-range-dependent processes with negative degrees of persistence. As an application, we showed how we can approximate a fractionally differenced process using theoretically-motivated cross-sectional aggregated long-range-dependent processes. An example with temperature data showed that our framework provides a better fit to the data than the fractional difference operator. Full article
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Article
Modeling Hospital Resource Management during the COVID-19 Pandemic: An Experimental Validation
Econometrics 2021, 9(4), 38; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040038 - 14 Oct 2021
Viewed by 500
Abstract
One of the main challenges posed by the healthcare crisis generated by COVID-19 is to avoid hospital collapse. The occupation of hospital beds by patients diagnosed by COVID-19 implies the diversion or suspension of their use for other specialities. Therefore, it is useful [...] Read more.
One of the main challenges posed by the healthcare crisis generated by COVID-19 is to avoid hospital collapse. The occupation of hospital beds by patients diagnosed by COVID-19 implies the diversion or suspension of their use for other specialities. Therefore, it is useful to have information that allows efficient management of future hospital occupancy. This article presents a robust and simple model to show certain characteristics of the evolution of the dynamic process of bed occupancy by patients with COVID-19 in a hospital by means of an adaptation of Kaplan-Meier survival curves. To check this model, the evolution of the COVID-19 hospitalization process of two hospitals between 11 March and 15 June 2020 is analyzed. The information provided by the Kaplan-Meier curves allows forecasts of hospital occupancy in subsequent periods. The results shows an average deviation of 2.45 patients between predictions and actual occupancy in the period analyzed. Full article
(This article belongs to the Special Issue Health Econometrics)
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Article
Air Pollution and Mobility, What Carries COVID-19?
Econometrics 2021, 9(4), 37; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040037 - 11 Oct 2021
Viewed by 477
Abstract
This paper tests if air pollution serves as a carrier for SARS-CoV-2 by measuring the effect of daily exposure to air pollution on its spread by panel data models that incorporates a possible commonality between municipalities. We show that the contemporary exposure to [...] Read more.
This paper tests if air pollution serves as a carrier for SARS-CoV-2 by measuring the effect of daily exposure to air pollution on its spread by panel data models that incorporates a possible commonality between municipalities. We show that the contemporary exposure to particle matter is not the main driver behind the increasing number of cases and deaths in the Mexico City Metropolitan Area. Remarkably, we also find that the cross-dependence between municipalities in the Mexican region is highly correlated to public mobility, which plays the leading role behind the rhythm of contagion. Our findings are particularly revealing given that the Mexico City Metropolitan Area did not experience a decrease in air pollution during COVID-19 induced lockdowns. Full article
(This article belongs to the Special Issue Health Econometrics)
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Article
Forecasting US Inflation in Real Time
Econometrics 2021, 9(4), 36; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040036 - 09 Oct 2021
Viewed by 394
Abstract
We analyze real-time forecasts of US inflation over 1999Q3–2019Q4 and subsamples, investigating whether and how forecast accuracy and robustness can be improved with additional information such as expert judgment, additional macroeconomic variables, and forecast combination. The forecasts include those from the Federal Reserve [...] Read more.
We analyze real-time forecasts of US inflation over 1999Q3–2019Q4 and subsamples, investigating whether and how forecast accuracy and robustness can be improved with additional information such as expert judgment, additional macroeconomic variables, and forecast combination. The forecasts include those from the Federal Reserve Board’s Tealbook, the Survey of Professional Forecasters, dynamic models, and combinations thereof. While simple models remain hard to beat, additional information does improve forecasts, especially after 2009. Notably, forecast combination improves forecast accuracy over simpler models and robustifies against bad forecasts; aggregating forecasts of inflation’s components can improve performance compared to forecasting the aggregate directly; and judgmental forecasts, which may incorporate larger and more timely datasets in conjunction with model-based forecasts, improve forecasts at short horizons. Full article
(This article belongs to the Special Issue Celebrated Econometricians: David Hendry)
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Article
Inference Using Simulated Neural Moments
Econometrics 2021, 9(4), 35; https://0-doi-org.brum.beds.ac.uk/10.3390/econometrics9040035 - 24 Sep 2021
Viewed by 534
Abstract
This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals using these [...] Read more.
This paper studies method of simulated moments (MSM) estimators that are implemented using Bayesian methods, specifically Markov chain Monte Carlo (MCMC). Motivation and theory for the methods is provided by Chernozhukov and Hong (2003). The paper shows, experimentally, that confidence intervals using these methods may have coverage which is far from the nominal level, a result which has parallels in the literature that studies overidentified GMM estimators. A neural network may be used to reduce the dimension of an initial set of moments to the minimum number that maintains identification, as in Creel (2017). When MSM-MCMC estimation and inference is based on such moments, and using a continuously updating criteria function, confidence intervals have statistically correct coverage in all cases studied. The methods are illustrated by application to several test models, including a small DSGE model, and to a jump-diffusion model for returns of the S&P 500 index. Full article
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