scholarly journals Time stability of the impact of institutions on economic growth and real convergence of the EU countries: implications from the hidden Markov models analysis

Equilibrium ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. 285-323
Author(s):  
Michał Bernardelli ◽  
Mariusz Próchniak ◽  
Bartosz Witkowski
Keyword(s):  
Economic Growth ◽  
Hidden Markov Models ◽  
Structural Breaks ◽  
Markov Models ◽  
Hidden Markov ◽  
Value Added ◽  
Time Stability ◽  
The Impact ◽  
Over Time ◽  
The Eu

Research background: It is not straightforward to identify the role of institutions for the economic growth. The possible unknown or uncertain areas refer to nonlinearities, time stability, transmission channels, and institutional complementarities. The research problem tackled in this paper is the analysis of the time stability of the relationship between institutions and economic growth and real economic convergence. Purpose of the article: The article aims to verify whether the impact of the institutional environment on GDP dynamics was stable over time or diffed in various subperiods. The analysis covers the EU28 countries and the 1995?2019 period. Methods: We use regression equations with time dummies and interactions to assess the stability of the impact of institutions on economic growth. The analysis is based on the partially overlapping observations. The models are estimated with the use of Blundell and Bond?s GMM system estimator. The results are then averaged with the Bayesian Model Averaging (BMA) approach. Structural breaks are identified on the basis of the Hidden Markov Models (HMM). Findings & value added: The value added of the study is threefold. First, we use the HMM approach to find structural breaks. Second, the BMA method is applied to assess the robustness of the outcomes. Third, we show the potential of HMM in foresighting. The results of regression estimates indicate that good institution reflected in the greater scope of economic freedom and better governance lead to the higher economic growth of the EU countries. However, the impact of institutions on economic growth was not stable over time.

scholarly journals Reconciliation of inconsistent data sources using hidden Markov models

2020 ◽  
Vol 36 (4) ◽  
pp. 1261-1279
Author(s):  
Paulina Pankowska ◽  
Dimitris Pavlopoulos ◽  
Bart Bakker ◽  
Daniel L. Oberski
Keyword(s):  
Measurement Error ◽  
Data Collection ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Parameter Estimates ◽  
Collection Process ◽  
Data Collection Process ◽  
Linkage Error ◽  
Over Time

This paper discusses how National Statistical Institutes (NSI’s) can use hidden Markov models (HMMs) to produce consistent official statistics for categorical, longitudinal variables using inconsistent sources. Two main challenges are addressed: first, the reconciliation of inconsistent sources with multi-indicator HMMs requires linking the sources on the micro level. Such linkage might lead to bias due to linkage error. Second, applying and estimating HMMs regularly is a complicated and expensive procedure. Therefore, it is preferable to use the error parameter estimates as a correction factor for a number of years. However, this might lead to biased structural estimates if measurement error changes over time or if the data collection process changes. Our results on these issues are highly encouraging and imply that the suggested method is appropriate for NSI’s. Specifically, linkage error only leads to (substantial) bias in very extreme scenarios. Moreover, measurement error parameters are largely stable over time if no major changes in the data collection process occur. However, when a substantial change in the data collection process occurs, such as a switch from dependent (DI) to independent (INDI) interviewing, re-using measurement error estimates is not advisable.

scholarly journals Developments of Economic Growth and Employment in Bioeconomy Sectors across the EU

2020 ◽  
Vol 12 (11) ◽  
pp. 4507 ◽  
Author(s):  
Tévécia Ronzon ◽  
Stephan Piotrowski ◽  
Saulius Tamosiunas ◽  
Lara Dammer ◽  
Michael Carus ◽  
...  
Keyword(s):  
Economic Growth ◽  
Labour Productivity ◽  
Value Added ◽  
The European Union ◽  
Business Statistics ◽  
Structural Differences ◽  
Subsequent Calculation ◽  
Output Based Approach ◽  
Over Time ◽  
The Eu

The development of the bioeconomy—or the substitution of fossil-based materials and energy by bio-based solutions—is considered a strategic economic orientation by the European Commission and its Green Deal. This paper presents a methodology to monitor the contribution of the bioeconomy to jobs and growth within the European Union (EU) and its Member States. Classified as an ‘‘output-based’’ approach, the methodology relies on expert estimations of the biomass content of the bio-based materials produced in the EU and the subsequent calculation of ‘‘sectoral’’ bio-based shares by using Eurostat statistics on the production of manufactured goods (prom). Sectoral shares are applied to indicators of employment, and value added is reported in Eurostat–Structural business statistics. This paper updates the methodology and time series presented in 2018. The bioeconomy of the EU (post-Brexit composition) employed around 17.5 million people and generated €614 billion of value added in 2017. The study evidences structural differences between EU national bioeconomies, which become more pronounced over time, especially in terms of the level of apparent labour productivity of national bioeconomies. Finally, this paper describes cases of transition over the 2008–2017 period.

scholarly journals Trend shifts in road traffic collisions: An application of Hidden Markov Models and Generalised Additive Models to assess the impact of the 20 mph speed limit policy in Edinburgh

2021 ◽  
pp. 239980832098552
Author(s):  
Valentin Popov ◽  
Glenna Nightingale ◽  
Andrew James Williams ◽  
Paul Kelly ◽  
Ruth Jepson ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Road Traffic ◽  
Hidden Markov ◽  
Seasonal Pattern ◽  
Additive Models ◽  
Speed Limit ◽  
Generalised Additive Models ◽  
Traffic Collision ◽  
The Impact

Empirical study of road traffic collision (RTCs) rates is challenging at small geographies due to the relative rarity of collisions and the need to account for secular and seasonal trends. In this paper, we demonstrate the successful application of Hidden Markov Models (HMMs) and Generalised Additive Models (GAMs) to describe RTCs time series using monthly data from the city of Edinburgh (STATS19) as a case study. While both models have comparable level of complexity, they bring different advantages. HMMs provide a better interpretation of the data-generating process, whereas GAMs can be superior in terms of forecasting. In our study, both models successfully capture the declining trend and the seasonal pattern with a peak in the autumn and a dip in the spring months. Our best fitting HMM indicates a change in a fast-declining-trend state after the introduction of the 20 mph speed limit in July 2016. Our preferred GAM explicitly models this intervention and provides evidence for a significant further decline in the RTCs. In a comparison between the two modelling approaches, the GAM outperforms the HMM in out-of-sample forecasting of the RTCs for 2018. The application of HMMs and GAMs to routinely collected data such as the road traffic data may be beneficial to evaluations of interventions and policies, especially natural experiments, that seek to impact traffic collision rates.

scholarly journals Hidden Markov models and their applications in finance

2015 ◽  
Author(s):  
Αναστάσιος Πετρόπουλος
Keyword(s):  
Hidden Markov Models ◽  
Financial Institutions ◽  
Markov Models ◽  
Temporal Dynamics ◽  
Hidden Markov ◽  
Credit Scoring ◽  
Change Over Time ◽  
Corporate Credit ◽  
Over Time ◽  
Temporal Dependencies

Hidden Markov Models, usually referred to as HMMs, are one of the most successful concepts in statistical modeling conceived and analyzed in the last 40 years. They belong to the stochastic mixture models family and have been broadly implemented in numerous sectors to address the problem of data model fitting and forecasting. Their structure usually is comprised by an observed sequence which is conditioned on an underlying hidden (unobserved) process. This way HMMs provide flexibility to address various complicated problems and can be implemented for modeling univariate and multivariate financial time series. Moreover, based on current literature, economic variables exhibit patterns dependent on different economic regimes which can be successfully captured by HMMs. Their parsimonious structure and attractive properties along with the existence of efficient algorithms for their estimation were the main drivers for the selection of HMM as the main topic of this thesis. Consequently, in this thesis we thoroughly investigate HMMs and their capabilities to simulate financial systems. The contribution of this study is threefold: First we perform an extensive review of HMM theory and applications. Our aim is to summarize the most significant applications of HMM with special focus in the field of finance. We offer a thorough and compact summary of the uses and the results of HMM in the last 40 years. Secondly, we extend the framework of HMMs by proposing a theoretical variation, injecting greater flexibility in their structure. Based on bibliography, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. We address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed financial data. Finally in the third part of this thesis we investigate the HMM efficiency in the problem of corporate credit scoring. We propose a novel corporate credit rating system based on Student’s-t hidden Markov models (SHMMs). Corporate credit scoring is widely used by financial institutions for portfolio risk management, and for pricing financial products designed for corporations. In addition, from a regulatory perspective, internal rating models are commonly used for establishing a more risk-sensitive capital adequacy framework for financial institutions. We evaluate our method against other state of the art statistical techniques like Neural Networks, SVM, and logistic regression and conclude that SHMM offer significant improved forecasting capabilities.

scholarly journals IMPROVING PERFORMANCE OF VAT SYSTEM IN DEVELOPING EU COUNTRIES: ESTIMATING THE DETERMINANTS OF THE RATIO C-EFFICIENCY IN THE PERIOD 1997-2017

2019 ◽  
pp. 239
Author(s):  
Marina Đorđević ◽  
Jadranka Đurović Todorović ◽  
Milica Ristić
Keyword(s):  
Economic Growth ◽  
Growth Rate ◽  
Developing Countries ◽  
Collection Efficiency ◽  
Value Added ◽  
Household Consumption ◽  
Economic Growth Rate ◽  
Eu Countries ◽  
The Impact ◽  
The Eu

Indirect taxes have a significant place in developing EU countries’ tax systems. The article sums up scholars of different scientists, dealing with the impact of VAT efficiency determinants. The purpose of this study is to investigate the determinants of VAT collection efficiency in the EU developing countries. The study relies on relevant data in transparent international statistical databases, covering the period from 1997 to 2017. The main research question in this paper is: does rise in value added tax rate negatively affect VAT collection efficiency in the EU developing countries. Accordingly, one of the independent variables included in the survey is standard annual VAT rate. In addition to standard VAT rate, as a determinant of VAT collection efficiency, we analyze: economic growth rate, export of goods, export of services, wages and salaries, household consumption. The hypotheses set are analyzed using correlation and regression analyses. Empirical results show a positive effect of economic growth rate, export of goods, and the negative effect of two variables: standard VAT rate and household consumption. The two observed variables, export of services and wages and salaries, do not show a statistically significant effect. The results obtained using appropriate statistical tools serve as guidelines to macroeconomic policy makers to generate higher tax revenues from VAT. By analyzing the C-efficiency determinant, we design a relevant development strategy approach for economically underdeveloped EU countries.

scholarly journals ANALYSIS OF THE EUROPEAN FRAMEWORK ON THE CIRCULAR ECONOMY

2021 ◽  
Vol IV(1) ◽  
Author(s):  
Viorica Popa ◽  
◽  
Nicolae Popa ◽  
Keyword(s):  
Economic Growth ◽  
Circular Economy ◽  
Success Factors ◽  
Raw Materials ◽  
Economic Activities ◽  
Security Of Supply ◽  
The Impact ◽  
Over Time ◽  
The Eu

In the transition to a circular economy, monitoring key trends and patterns is essential to understand how the various elements of the circular economy develop over time, identify Member States' success factors and to assess whether sufficient action has been taken. Thus, the EU Council, based on the results of the monitoring, must be based on setting new priorities for achieving the long-term goal of the circular economy in the future. The crisis caused by Covid-19 mitigates part of the impact of economic activities on the environment and climate. Thus, the transition to a more circular economy could bring benefits such as reducing pressure on the environment, improving security of supply of raw materials, increasing competitiveness, stimulating innovation, stimulating economic growth, creating jobs. The authors in this study analyze the European framework on the circular economy.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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