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Cement is an important material for society, being a base component of concrete. Indeed, concrete
is widely used in construction. But the production of cement is energy-intensive and thus has a high
impact on environment. The objective of this thesis is to give operators of cement rotary kilns a reliable
model on which they may test possible actions and see the future effect of these actions. The possibility of
choosing the most appropriate action will thus improve production efficiency. The model must be quickly
solved. The shorter the computation time, the more actions the operator may test. For this objective, a
1D partial-differential-equations model is developed. Its equations system is based on mass and energy
balances which are expressed with mass and energy transfers. Due to the high differences between the
flame and no-flame zones of the kiln, the equations are different in the flame and no-flame zones. A
second model is then developed. The second model presented here includes modified equations such that
the sizes of the different zones adapt to the exact flame length. It also allows representing a time-varying
flame length. In that model, the number of nodes assigned to the flame zone is predetermined. Next,
the implementation is performed in Matlab using the method of lines with finite differences from the
Matmol toolbox. At the end, several scenarios are simulated in order to test the model. It shows the
physical coherence of the model, the coherence of both models, that the mass and energy balances are
respected and most importantly that the second model is more efficient. Indeed, it requires less nodes and
a notably smaller computation time. In conclusion, the modeling of cement rotary kilns has been improved.

Cement kiln --- Modeling --- Time-varying flame length --- Ingénierie, informatique & technologie > Energie --- Ingénierie, informatique & technologie > Ingénierie chimique

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A vector autoregression model with time-varying coefficients is used to examine the evolution of wage cyclicality in four Latin American economies: Brazil, Chile, Colombia and Mexico, during the period 1980-2010. Wages are highly pro-cyclical in all countries up to the mid-1990s except in Chile. Wage cyclicality declines thereafter, especially in Brazil and Colombia. This decline in wage cyclicality is in accordance with declining real-wage flexibility in a low-inflation environment. Controlling for compositional effects caused by changes in labor force participation along the business cycle does not alter these results.

Bayesian Estimation --- Downward Wage Rigidity --- Economic Theory & Research --- Environment --- Environmental Economics & Policies --- Governance --- Indexation --- Labor Markets --- Labor Policies --- Macroeconomics and Economic Growth --- Real Wage Cyclicality --- Social Protections and Labor --- Time Varying Coefficients --- Vector Autoregression --- Youth & Governance

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This book was published in 2004. The estimation of noisily observed states from a sequence of data has traditionally incorporated ideas from Hilbert spaces and calculus-based probability theory. As conditional expectation is the key concept, the correct setting for filtering theory is that of a probability space. Graduate engineers, mathematicians and those working in quantitative finance wishing to use filtering techniques will find in the first half of this book an accessible introduction to measure theory, stochastic calculus, and stochastic processes, with particular emphasis on martingales and Brownian motion. Exercises are included. The book then provides an excellent users' guide to filtering: basic theory is followed by a thorough treatment of Kalman filtering, including recent results which extend the Kalman filter to provide parameter estimates. These ideas are then applied to problems arising in finance, genetics and population modelling in three separate chapters, making this a comprehensive resource for both practitioners and researchers.

Kalman filtering --- Measure theory --- 305.974 --- AA / International- internationaal --- Lebesgue measure --- Measurable sets --- Measure of a set --- Algebraic topology --- Integrals, Generalized --- Measure algebras --- Rings (Algebra) --- Filtering, Kalman --- Control theory --- Estimation theory --- Prediction theory --- Stochastic processes --- Time varying coefficients. Kalman Filter --- Measure theory. --- Kalman filtering.

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This 2004 volume offers a broad overview of developments in the theory and applications of state space modeling. With fourteen chapters from twenty-three contributors, it offers a unique synthesis of state space methods and unobserved component models that are important in a wide range of subjects, including economics, finance, environmental science, medicine and engineering. The book is divided into four sections: introductory papers, testing, Bayesian inference and the bootstrap, and applications. It will give those unfamiliar with state space models a flavour of the work being carried out as well as providing experts with valuable state of the art summaries of different topics. Offering a useful reference for all, this accessible volume makes a significant contribution to the literature of this discipline.

State-space methods --- System analysis --- Congresses --- AA / International- internationaal --- 304.5 --- 303.5 --- 305.974 --- -System analysis --- -003 --- Network theory --- Systems analysis --- System theory --- Mathematical optimization --- Techniek van de statistische-econometrische voorspellingen. Prognose in de econometrie. --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek). --- Time varying coefficients. Kalman Filter. --- Conferences - Meetings --- 003 --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek) --- Techniek van de statistische-econometrische voorspellingen. Prognose in de econometrie --- Time varying coefficients. Kalman Filter --- Business, Economy and Management --- Economics --- State-space methods - Congresses --- System analysis - Congresses --- Quantitative methods (economics) --- econometrie

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Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data, models, parameters and parameter restriction uncertainties, in a unified and coherent framework. This book contributes to this literature by collecting a set of carefully evaluated contributions that are grouped amongst two topics in financial economics. The first three papers refer to macro-finance issues for real economy, including the elasticity of factor substitution (ES) in the Cobb–Douglas production function, the effects of government public spending components, and quantitative easing, monetary policy and economics. The last three contributions focus on cryptocurrency and stock market predictability. All arguments are central ingredients in the current economic discussion and their importance has only been further emphasized by the COVID-19 crisis.

Technology: general issues --- unconventional monetary policy --- transmission channel --- Bayesian TVP-SV-VAR --- Bayesian econometrics --- portfolio choice --- sentiments --- stock market predictability --- cryptocurrency --- Bitcoin --- forecasting --- point forecast --- density forecast --- dynamic model averaging --- dynamic model selection --- forgetting factors --- military and civilian spending --- DSGE model --- fiscal policy --- monetary policy --- Bayesian estimation --- Bayesian VAR --- density forecasting --- time-varying volatility --- ES --- CES function --- Bayesian nonlinear mixed-effects regression --- MCMC methods --- macroeconomic and financial applications --- unconventional monetary policy --- transmission channel --- Bayesian TVP-SV-VAR --- Bayesian econometrics --- portfolio choice --- sentiments --- stock market predictability --- cryptocurrency --- Bitcoin --- forecasting --- point forecast --- density forecast --- dynamic model averaging --- dynamic model selection --- forgetting factors --- military and civilian spending --- DSGE model --- fiscal policy --- monetary policy --- Bayesian estimation --- Bayesian VAR --- density forecasting --- time-varying volatility --- ES --- CES function --- Bayesian nonlinear mixed-effects regression --- MCMC methods --- macroeconomic and financial applications