Pub Date : 2022-11-27DOI: 10.3390/econometrics11020015
Jarosław Gruszka , Janusz Szwabi'nski
The parametric estimation of stochastic differential equations (SDEs) has been the subject of intense studies already for several decades. The Heston model, for instance, is based on two coupled SDEs and is often used in financial mathematics for the dynamics of asset prices and their volatility. Calibrating it to real data would be very useful in many practical scenarios. It is very challenging, however, since the volatility is not directly observable. In this paper, a complete estimation procedure of the Heston model without and with jumps in the asset prices is presented. Bayesian regression combined with the particle filtering method is used as the estimation framework. Within the framework, we propose a novel approach to handle jumps in order to neutralise their negative impact on the estimates of the key parameters of the model. An improvement in the sampling in the particle filtering method is discussed as well. Our analysis is supported by numerical simulations of the Heston model to investigate the performance of the estimators. In addition, a practical follow-along recipe is given to allow finding adequate estimates from any given data.
{"title":"Parameter Estimation of the Heston Volatility Model with Jumps in the Asset Prices","authors":"Jarosław Gruszka , Janusz Szwabi'nski","doi":"10.3390/econometrics11020015","DOIUrl":"https://doi.org/10.3390/econometrics11020015","url":null,"abstract":"The parametric estimation of stochastic differential equations (SDEs) has been the subject of intense studies already for several decades. The Heston model, for instance, is based on two coupled SDEs and is often used in financial mathematics for the dynamics of asset prices and their volatility. Calibrating it to real data would be very useful in many practical scenarios. It is very challenging, however, since the volatility is not directly observable. In this paper, a complete estimation procedure of the Heston model without and with jumps in the asset prices is presented. Bayesian regression combined with the particle filtering method is used as the estimation framework. Within the framework, we propose a novel approach to handle jumps in order to neutralise their negative impact on the estimates of the key parameters of the model. An improvement in the sampling in the particle filtering method is discussed as well. Our analysis is supported by numerical simulations of the Heston model to investigate the performance of the estimators. In addition, a practical follow-along recipe is given to allow finding adequate estimates from any given data.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44861140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-25DOI: 10.3390/econometrics10040033
Neil R. Ericsson, Mohammed H. I. Dore, Hassan A. Butt
Structural breaks have attracted considerable attention recently, especially in light of the financial crisis, Great Recession, the COVID-19 pandemic, and war. While structural breaks pose significant econometric challenges, machine learning provides an incisive tool for detecting and quantifying breaks. The current paper presents a unified framework for analyzing breaks; and it implements that framework to test for and quantify changes in precipitation in Mauritania over 1919–1997. These tests detect a decline of one third in mean rainfall, starting around 1970. Because water is a scarce resource in Mauritania, this decline—with adverse consequences on food production—has potential economic and policy consequences.
{"title":"Detecting and Quantifying Structural Breaks in Climate","authors":"Neil R. Ericsson, Mohammed H. I. Dore, Hassan A. Butt","doi":"10.3390/econometrics10040033","DOIUrl":"https://doi.org/10.3390/econometrics10040033","url":null,"abstract":"Structural breaks have attracted considerable attention recently, especially in light of the financial crisis, Great Recession, the COVID-19 pandemic, and war. While structural breaks pose significant econometric challenges, machine learning provides an incisive tool for detecting and quantifying breaks. The current paper presents a unified framework for analyzing breaks; and it implements that framework to test for and quantify changes in precipitation in Mauritania over 1919–1997. These tests detect a decline of one third in mean rainfall, starting around 1970. Because water is a scarce resource in Mauritania, this decline—with adverse consequences on food production—has potential economic and policy consequences.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48101146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-25DOI: 10.3390/econometrics11010007
M. Bolla, Dongze Ye, Haoyu Wang, Renyuan Ma, Valentin Frappier, William Thompson, Catherine Donner, Máté Baranyi, Fatma Abdelkhalek
A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations and estimate the path coefficients along a directed acyclic graph (DAG). If the DAG is decomposable, i.e., the zeros form a reducible zero pattern (RZP) in its adjacency matrix, then covariance selection is applied that assigns zeros to the corresponding path coefficients. Real-life applications are also considered, where for the optimal order p≥1 of the fitted CVAR(p) model, order selection is performed with various information criteria.
{"title":"Causal Vector Autoregression Enhanced with Covariance and Order Selection","authors":"M. Bolla, Dongze Ye, Haoyu Wang, Renyuan Ma, Valentin Frappier, William Thompson, Catherine Donner, Máté Baranyi, Fatma Abdelkhalek","doi":"10.3390/econometrics11010007","DOIUrl":"https://doi.org/10.3390/econometrics11010007","url":null,"abstract":"A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations and estimate the path coefficients along a directed acyclic graph (DAG). If the DAG is decomposable, i.e., the zeros form a reducible zero pattern (RZP) in its adjacency matrix, then covariance selection is applied that assigns zeros to the corresponding path coefficients. Real-life applications are also considered, where for the optimal order p≥1 of the fitted CVAR(p) model, order selection is performed with various information criteria.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43201688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-04DOI: 10.3390/econometrics10040032
Irwan Susanto, Nur Iriawan, H. Kuswanto
This paper proposes enhanced studies on a model consisting of a finite mixture framework of generalized linear models (GLMs) with gamma-distributed responses estimated using the Bayesian approach coupled with the Markov Chain Monte Carlo (MCMC) method. The log-link function, which relates the mean and linear predictors of the model, is implemented to ensure non-negative values of the predicted gamma-distributed responses. The simulation-based inferential processes related to the Bayesian-MCMC method is carried out using the Gibbs sampler algorithm. The performance of proposed model is conducted through two real data applications on the gross domestic product per capita at purchasing power parity and the annual household income per capita. Graphical posterior predictive checks are carried out to verify the adequacy of the fitted model for the observed data. The predictive accuracy of this model is compared with other Bayesian models using the widely applicable information criterion (WAIC). We find that the Bayesian mixture of GLMs with gamma-distributed responses performs properly when the appropriate prior distributions are applied and has better predictive accuracy than the Bayesian mixture of linear regression model and the Bayesian gamma regression model.
{"title":"On the Bayesian Mixture of Generalized Linear Models with Gamma-Distributed Responses","authors":"Irwan Susanto, Nur Iriawan, H. Kuswanto","doi":"10.3390/econometrics10040032","DOIUrl":"https://doi.org/10.3390/econometrics10040032","url":null,"abstract":"This paper proposes enhanced studies on a model consisting of a finite mixture framework of generalized linear models (GLMs) with gamma-distributed responses estimated using the Bayesian approach coupled with the Markov Chain Monte Carlo (MCMC) method. The log-link function, which relates the mean and linear predictors of the model, is implemented to ensure non-negative values of the predicted gamma-distributed responses. The simulation-based inferential processes related to the Bayesian-MCMC method is carried out using the Gibbs sampler algorithm. The performance of proposed model is conducted through two real data applications on the gross domestic product per capita at purchasing power parity and the annual household income per capita. Graphical posterior predictive checks are carried out to verify the adequacy of the fitted model for the observed data. The predictive accuracy of this model is compared with other Bayesian models using the widely applicable information criterion (WAIC). We find that the Bayesian mixture of GLMs with gamma-distributed responses performs properly when the appropriate prior distributions are applied and has better predictive accuracy than the Bayesian mixture of linear regression model and the Bayesian gamma regression model.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48294247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-13DOI: 10.3390/econometrics10030031
Robert C. Jung, S. Glaser
This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based on different distributional assumptions using standard numerical procedures. In addition, we advocate the use of data-coherent diagnostic tools in spatial count regression models. The application revisits a data set on the location choice of single unit start-up firms in the manufacturing industry in the US.
{"title":"Modelling and Diagnostics of Spatially Autocorrelated Counts","authors":"Robert C. Jung, S. Glaser","doi":"10.3390/econometrics10030031","DOIUrl":"https://doi.org/10.3390/econometrics10030031","url":null,"abstract":"This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring observations on the conditional expectation of the counts. It allows for flexible likelihood-based inference based on different distributional assumptions using standard numerical procedures. In addition, we advocate the use of data-coherent diagnostic tools in spatial count regression models. The application revisits a data set on the location choice of single unit start-up firms in the manufacturing industry in the US.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48007302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The article presents an analysis of the results of a survey conducted in 2022 among students and young graduates of three vocational education courses, studying in EU countries (N = 428). The area of research concerns the awareness of competencies sought-after by employers and the self-assessed level of these competencies. The authors used tests of the equality of two means in order to check for differences in assessments according to the respondents’ metric characteristics, and also factor analysis to check for similarities in attitudes towards different types of competencies in the respondents’ assessments. Finding such similarities would allow to use a summative scale and reduce the dimensions.
{"title":"The Vocational Education Sector in Relation to Labour Market Expectations. The Analysis of the Results of an International Student Survey","authors":"Dorota Kwiatkowska-Ciotucha, Urszula Załuska","doi":"10.15611/eada.2022.3.03","DOIUrl":"https://doi.org/10.15611/eada.2022.3.03","url":null,"abstract":"Abstract The article presents an analysis of the results of a survey conducted in 2022 among students and young graduates of three vocational education courses, studying in EU countries (N = 428). The area of research concerns the awareness of competencies sought-after by employers and the self-assessed level of these competencies. The authors used tests of the equality of two means in order to check for differences in assessments according to the respondents’ metric characteristics, and also factor analysis to check for similarities in attitudes towards different types of competencies in the respondents’ assessments. Finding such similarities would allow to use a summative scale and reduce the dimensions.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":"26 1","pages":"35 - 53"},"PeriodicalIF":1.5,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43259897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The purpose of the paper was to investigate by the kernel method a nonparametric estimate of the conditional density function of a scalar response variable given a random variable taking values in a separable real Hilbert space when the observations are quasi-associated dependent. Under some general conditions, the authors established the pointwise almost complete consistencies with rates of this estimator. The principal aim is the investigate the convergence rate of the proposed estimator.
{"title":"Asymptotic Properties of the Estimator of the Conditional Distribution for Associated Functional Data","authors":"Mohamed Mehdi Hamri, Abdassamad Dib, A. Rabhi","doi":"10.15611/eada.2022.3.02","DOIUrl":"https://doi.org/10.15611/eada.2022.3.02","url":null,"abstract":"Abstract The purpose of the paper was to investigate by the kernel method a nonparametric estimate of the conditional density function of a scalar response variable given a random variable taking values in a separable real Hilbert space when the observations are quasi-associated dependent. Under some general conditions, the authors established the pointwise almost complete consistencies with rates of this estimator. The principal aim is the investigate the convergence rate of the proposed estimator.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":"26 1","pages":"21 - 34"},"PeriodicalIF":1.5,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42804758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper expands the considerations of Becker’s and Leibenstein’s family theories with a focus on the additional member of the household (pet/animal) in the analysis of consumption. It is the first analytical approach regarding pet goods consumption with references to microeconomic theories based on Polish data. The study analyses the households’ characteristics that have an impact on expenditure on pet goods. This article used the Polish Household Budget Surveys for 2018. The findings from the logistic regression models suggest that the household’s socio-economic group, place of living, children in household and whether the household rents the flat/accommodation impact on determining the probability of owning a pet among Polish house-holds; analyses of interactions between significant variables were also conducted. However, the human-animal bond could not be included in analysis, which is a limitation, the overall work is pioneering, as it shows the quantitative approach to household economy that highlights the need to elaborate the economic family theories of Becker and Leibenstein by a new family member – a pet.
{"title":"Pet Goods Consumption in Polish Households","authors":"N. Gromek, J. Perek-Białas","doi":"10.15611/eada.2022.3.01","DOIUrl":"https://doi.org/10.15611/eada.2022.3.01","url":null,"abstract":"Abstract This paper expands the considerations of Becker’s and Leibenstein’s family theories with a focus on the additional member of the household (pet/animal) in the analysis of consumption. It is the first analytical approach regarding pet goods consumption with references to microeconomic theories based on Polish data. The study analyses the households’ characteristics that have an impact on expenditure on pet goods. This article used the Polish Household Budget Surveys for 2018. The findings from the logistic regression models suggest that the household’s socio-economic group, place of living, children in household and whether the household rents the flat/accommodation impact on determining the probability of owning a pet among Polish house-holds; analyses of interactions between significant variables were also conducted. However, the human-animal bond could not be included in analysis, which is a limitation, the overall work is pioneering, as it shows the quantitative approach to household economy that highlights the need to elaborate the economic family theories of Becker and Leibenstein by a new family member – a pet.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":"26 1","pages":"1 - 20"},"PeriodicalIF":1.5,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43706316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-24DOI: 10.3390/econometrics10030030
Jian Kang, J. Jakobsen, Annastiina Silvennoinen, T. Teräsvirta, Glen Wade
We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by simulation. An empirical example involving daily returns of 26 stocks included in the Dow Jones stock index is given.
{"title":"A Parsimonious Test of Constancy of a Positive Definite Correlation Matrix in a Multivariate Time-Varying GARCH Model","authors":"Jian Kang, J. Jakobsen, Annastiina Silvennoinen, T. Teräsvirta, Glen Wade","doi":"10.3390/econometrics10030030","DOIUrl":"https://doi.org/10.3390/econometrics10030030","url":null,"abstract":"We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by simulation. An empirical example involving daily returns of 26 stocks included in the Dow Jones stock index is given.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43009910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-11DOI: 10.3390/econometrics10030029
Shiyun Cao, Qiankun Zhou
In this paper, we consider the estimation of a dynamic panel data model with non-stationary multi-factor error structures. We adopted the common correlated effect (CCE) estimation and established the asymptotic properties of the CCE and common correlated effects mean group (CCEMG) estimators, as N and T tend to infinity. The results show that both the CCE and CCEMG estimators are consistent and the CCEMG estimator is asymptotically normally distributed. The theoretical findings were supported for small samples by an extensive simulation study, showing that the CCE estimators are robust to a wide variety of data generation processes. Empirical findings suggest that the CCE estimation is widely applicable to models with non-stationary factors. The proposed procedure is also illustrated by an empirical application to analyze the U.S. cigar dataset.
{"title":"Common Correlated Effects Estimation for Dynamic Heterogeneous Panels with Non-Stationary Multi-Factor Error Structures","authors":"Shiyun Cao, Qiankun Zhou","doi":"10.3390/econometrics10030029","DOIUrl":"https://doi.org/10.3390/econometrics10030029","url":null,"abstract":"In this paper, we consider the estimation of a dynamic panel data model with non-stationary multi-factor error structures. We adopted the common correlated effect (CCE) estimation and established the asymptotic properties of the CCE and common correlated effects mean group (CCEMG) estimators, as N and T tend to infinity. The results show that both the CCE and CCEMG estimators are consistent and the CCEMG estimator is asymptotically normally distributed. The theoretical findings were supported for small samples by an extensive simulation study, showing that the CCE estimators are robust to a wide variety of data generation processes. Empirical findings suggest that the CCE estimation is widely applicable to models with non-stationary factors. The proposed procedure is also illustrated by an empirical application to analyze the U.S. cigar dataset.","PeriodicalId":11499,"journal":{"name":"Econometrics","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47891118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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