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Books on the topic 'Dynamic linear models (DLMs)'

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Published: 19 February 2023

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1

Sonia, Petrone, Petris Giovanni, and SpringerLink (Online service), eds. Dynamic Linear Models with R. New York, NY: Springer-Verlag New York, 2009.

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2

Campagnoli, Patrizia, Sonia Petrone, and Giovanni Petris. Dynamic Linear Models with R. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b135794.

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3

Hansen, Lars Peter. Recursive linear models of dynamic economies. Cambridge, MA: National Bureau of Economic Research, 1990.

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4

Pesaran, Hashem. Dynamic linear models for heterogeneous panels. Cambridge: Department of Applied Economics, University of Cambridge, 1995.

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5

Chang-Jin, Kim. Dynamic linear models with Markov-switching. Toronto, Ont: York University, Dept. of Economics, 1991.

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6

Jeff, Harrison, ed. Bayesian forecasting and dynamic models. 2nd ed. New York: Springer, 1997.

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7

R, Prucha Ingmar, ed. Dynamic nonlinear econometric models: Asymptotic theory. New York: Springer-Verlag, 1997.

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8

Catrien C. J. H. Bijleveld. Exploratory linear dynamic systems analysis. Leiden, Netherlands: DSWO Press, University of Leiden, 1989.

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9

West, Mike. Bayesian forecasting and dynamic models. New York: Springer, 1989.

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10

L, Koul H., ed. Weighted empirical processes in dynamic nonlinear models. 2nd ed. New York: Springer, 2002.

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11

Fernández-Villaverde, Jesús. Estimating dynamic equilibrium economies: Linear versus nonlinear likelihood. [Atlanta]: Federal Reserve Bank of Atlanta, 2004.

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12

Veerapen, Parmaseeven Pillay. Recurrence relationships and model monitoring for dynamic linear models. [s.l.]: typescript, 1991.

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13

Gallant, A. Ronald. A unified theory of estimation and inference for nonlinear dynamic models. Oxford [England]: B. Blackwell, 1988.

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14

Pötscher, Benedikt M. Dynamic Nonlinear Econometric Models: Asymptotic Theory. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997.

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15

Singh, Rajendra. Non-linear dynamic analysis of geared systems. [Columbus, Ohio]: The Ohio State University, Dept. of Mechanical Engineering, 1990.

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16

Singh, Rajendra. Non-linear dynamic analysis of geared systems. [Columbus, Ohio]: The Ohio State University, Dept. of Mechanical Engineering, 1990.

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17

service), SpringerLink (Online, ed. Dynamic Response of Linear Mechanical Systems: Modeling, Analysis and Simulation. Boston, MA: Springer Science+Business Media, LLC, 2012.

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18

LQ dynamic optimization and differential games. Chicester, West Sussex, England: J. Wiley & Sons, 2005.

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19

Reanalysis of structures: A unified approach for linear, nonlinear, static, and dynamic systems. Dordrecht, The Netherlands: Springer, 2008.

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20

Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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21

Huang, Jen-Kuang. Indirect identification of linear stochastic systems with known feedback dynamics. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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22

1975-, Sims Robert, and Ueltschi Daniel 1969-, eds. Entropy and the quantum II: Arizona School of Analysis with Applications, March 15-19, 2010, University of Arizona. Providence, R.I: American Mathematical Society, 2011.

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23

Dynamic Linear Economic Models. Taylor & Francis Group, 2018.

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24

Kenkel, James L. Dynamic Linear Economic Models. Taylor & Francis Group, 2018.

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25

Kenkel, James L. Dynamic Linear Economic Models. Taylor & Francis Group, 2018.

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26

Kenkel, James L. Dynamic Linear Economic Models. Routledge, 2018. http://dx.doi.org/10.4324/9781351140720.

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27

Kenkel, James L. Dynamic Linear Economic Models. Taylor & Francis Group, 2018.

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28

Kenkel, James L. Dynamic Linear Economic Models. Taylor & Francis Group, 2016.

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29

Kenkel, James L. Dynamic Linear Economic Models. Taylor & Francis Group, 2018.

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30

Hansen, Lars Peter, and Thomas J. Sargent. Recursive Models of Dynamic Linear Economies. Princeton University Press, 2013.

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31

Hansen, Lars Peter, and Thomas J. Sargent. Recursive Models of Dynamic Linear Economies. Princeton University Press, 2013.

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32

Recursive Linear Models of Dynamic Economies. Princeton University Press, 2007.

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33

Hansen, Lars Peter, and Thomas J. Sargent. Recursive Models of Dynamic Linear Economies. Princeton University Press, 2018.

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34

Wolters, J. Stochastic Dynamic Properties of Linear Econometric Models. Springer, 2012.

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35

Non-linear dynamic analysis of geared systems. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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36

Bayesian Forecasting and Dynamic Models Springer Series in Statistics. Springer-Verlag New York Inc., 2013.

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37

Engwerda, Jacob. LQ Dynamic Optimization and Differential Games. Wiley & Sons, Incorporated, John, 2006.

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38

Engwerda, Jacob. LQ Dynamic Optimization and Differential Games. Wiley & Sons, Incorporated, John, 2005.

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39

Engwerda, Jacob. Lq Dynamic Optimization and Differential Games. Wiley & Sons, Incorporated, John, 2005.

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40

Craven, Bruce D., and Sardar M. N. Islam. Optimization in Economics and Finance: Some Advances in Non-Linear, Dynamic, Multi-Criteria and Stochastic Models. Springer, 2006.

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41

Kirsch, Uri. Reanalysis of Structures: A Unified Approach for Linear, Nonlinear, Static and Dynamic Systems. Springer, 2010.

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42

Optimization in Economics and Finance: Some Advances in Non-Linear, Dynamic, Multi-Criteria and Stochastic Models (Dynamic Modeling and Econometrics in Economics and Finance). Springer, 2005.

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43

Witczak, Marcin. Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems: From Analytical to Soft Computing Approaches. Springer London, Limited, 2007.

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44

Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems: From Analytical to Soft Computing Approaches (Lecture Notes in Control and Information Sciences). Springer, 2007.

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45

United States. National Aeronautics and Space Administration., ed. A two dimensional interface element for coupling of independently modeled three dimensional finite element meshes and extensions to dynamic and non-linear regimes: Performance report (summary report). Norfolk, Va: Dept. of Aerospace Engineering, College of Engineering, College of Engineering & Technology, Old Dominion University, 1995.

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46

Back, Kerry E. Dynamic Asset Pricing. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0010.

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The distinction between conditional and unconditional factor pricing models is explained. The conditional CAPM implies that unconditional risk premia are linear in the expected beta and the beta of the beta. The CCAPM and ICAPM are derived as approximate relations in discrete time. Testing conditional models is equivalent to unconditional tests of pricing for managed portfolios. The Gordon growth model is derived, assuming that dividend growth and the single‐period SDF are IID over time. The equity premium and risk‐free rate puzzles are derived from the Gordon growth model with a CRRA investor and lognormal consumption growth. The Campbell‐Shiller linearization implies that dividend yields predict either future returns or future dividend growth.
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47

Tibaldi, Stefano, and Franco Molteni. Atmospheric Blocking in Observation and Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.611.

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The atmospheric circulation in the mid-latitudes of both hemispheres is usually dominated by westerly winds and by planetary-scale and shorter-scale synoptic waves, moving mostly from west to east. A remarkable and frequent exception to this “usual” behavior is atmospheric blocking. Blocking occurs when the usual zonal flow is hindered by the establishment of a large-amplitude, quasi-stationary, high-pressure meridional circulation structure which “blocks” the flow of the westerlies and the progression of the atmospheric waves and disturbances embedded in them. Such blocking structures can have lifetimes varying from a few days to several weeks in the most extreme cases. Their presence can strongly affect the weather of large portions of the mid-latitudes, leading to the establishment of anomalous meteorological conditions. These can take the form of strong precipitation episodes or persistent anticyclonic regimes, leading in turn to floods, extreme cold spells, heat waves, or short-lived droughts. Even air quality can be strongly influenced by the establishment of atmospheric blocking, with episodes of high concentrations of low-level ozone in summer and of particulate matter and other air pollutants in winter, particularly in highly populated urban areas.Atmospheric blocking has the tendency to occur more often in winter and in certain longitudinal quadrants, notably the Euro-Atlantic and the Pacific sectors of the Northern Hemisphere. In the Southern Hemisphere, blocking episodes are generally less frequent, and the longitudinal localization is less pronounced than in the Northern Hemisphere.Blocking has aroused the interest of atmospheric scientists since the middle of the last century, with the pioneering observational works of Berggren, Bolin, Rossby, and Rex, and has become the subject of innumerable observational and theoretical studies. The purpose of such studies was originally to find a commonly accepted structural and phenomenological definition of atmospheric blocking. The investigations went on to study blocking climatology in terms of the geographical distribution of its frequency of occurrence and the associated seasonal and inter-annual variability. Well into the second half of the 20th century, a large number of theoretical dynamic works on blocking formation and maintenance started appearing in the literature. Such theoretical studies explored a wide range of possible dynamic mechanisms, including large-amplitude planetary-scale wave dynamics, including Rossby wave breaking, multiple equilibria circulation regimes, large-scale forcing of anticyclones by synoptic-scale eddies, finite-amplitude non-linear instability theory, and influence of sea surface temperature anomalies, to name but a few. However, to date no unique theoretical model of atmospheric blocking has been formulated that can account for all of its observational characteristics.When numerical, global short- and medium-range weather predictions started being produced operationally, and with the establishment, in the late 1970s and early 1980s, of the European Centre for Medium-Range Weather Forecasts, it quickly became of relevance to assess the capability of numerical models to predict blocking with the correct space-time characteristics (e.g., location, time of onset, life span, and decay). Early studies showed that models had difficulties in correctly representing blocking as well as in connection with their large systematic (mean) errors.Despite enormous improvements in the ability of numerical models to represent atmospheric dynamics, blocking remains a challenge for global weather prediction and climate simulation models. Such modeling deficiencies have negative consequences not only for our ability to represent the observed climate but also for the possibility of producing high-quality seasonal-to-decadal predictions. For such predictions, representing the correct space-time statistics of blocking occurrence is, especially for certain geographical areas, extremely important.
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48

Raydugin, Yuri G. Modern Risk Quantification in Complex Projects. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198844334.001.0001.

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There are multiple complaints that existing project risk quantification methods—both parametric and Monte Carlo—fail to produce accurate project duration and cost-risk contingencies in a majority of cases. It is shown that major components of project risk exposure—non-linear risk interactions—pertaining to complex projects are not taken into account. It is argued that a project system consists of two interacting subsystems: a project structure subsystem (PSS) and a project delivery subsystem (PDS). Any misalignments or imbalances between these two subsystems (PSS–PDS mismatches) are associated with the non-linear risk interactions. Principles of risk quantification are developed to take into account three types of non-linear risk interactions in complex projects: internal risk amplifications due to existing ‘chronic’ project system issues, knock-on interactions, and risk compounding. Modified bowtie diagrams for the three types of risk interactions are developed to identify and address interacting risks. A framework to visualize dynamic risk patterns in affinities of interacting risks is proposed. Required mathematical expressions and templates to factor relevant risk interactions to Monte Carlo models are developed. Business cases are discussed to demonstrate the power of the newly-developed non-linear Monte Carlo methodology (non-linear integrated schedule and cost risk analysis (N-SCRA)). A project system dynamics methodology based on rework cycles is adopted as a supporting risk quantification tool. Comparison of results yielded by the non-linear Monte Carlo and system dynamics models demonstrates a good alignment of the two methodologies. All developed Monte Carlo and system dynamics models are available on the book’s companion website.
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49

Fernández-Villaverde, Jesús, Pablo Guerrón-Quintana, and Juan Rubio-Ramírez. Futures markets, Bayesian forecasting and risk modelling. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.14.

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This article demonstrates the utility of the Bayesian approach in forecasting and risk modelling regarding speculative trading strategies in financial futures markets. It first provides an overview of subjective expectations that are motivated as fair prices of futures contracts before discussing the futures markets and a portfolio mean-variance efficiency generalization. In particular, it considers the critical role of hedging to ensue attractive risk-adjusted performance. It also describes general Bayesian dynamic models and specific Bayesian dynamic linear models for assessing risk models in terms of their hedging effectiveness in the context of the risk-adjusted performance of trading strategies. The article showcases applied Bayesian thinking in the context of financial investment management, highlighting the corresponding concepts of betting and investing, prices and expectations, and coherence and arbitrage-free pricing in futures markets over the period 1990–2008.
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50

Walker, James C. G. Numerical Adventures with Geochemical Cycles. Oxford University Press, 1991. http://dx.doi.org/10.1093/oso/9780195045208.001.0001.

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The dynamic, evolving Earth, and the mathematical representation of its geochemical changes are the subject of this timely, helpful handbook. Global warming, changes in the ocean, and the effects of fossil fuel combustion are just a few of the phenomena that make the development of geochemical models critical. But what computational methods will help to accurately carry out this task? This new text teaches the methodology of computational simulation of environmental change. The author presents interesting applications of his methods to describe the response of the ocean and atmosphere to the infusion of pollutants, the effect of evaporation on seawater composition, climate change, and many other aspects of the Earth's evolving ecosystem. He also presents simple approaches for solving non-linear systems, calculating isotope ratios, and dealing with chains of identical reservoirs. With creative programs that can be executed on any personal computer, Walker offers earth scientists the techniques necessary to address the key problems in their field.
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