Nonlinear time series also evokes the development and. This edited collection explores recent theoretical developments of the models and their applications in economics and finance. Thus, the idea of this survey is to present statespace modelling in the field of macroeconomics, with policyrelated. Maximum likelihood estimation of time series models. Browse other questions tagged macroeconomics timeseries simulations or. State space models play a key role in the estimation of timevarying sensitivities in financial markets. Time series data occur naturally in many application areas. Statespace models with regime switching the mit press. The objective of this book is to analyze the relative merits of modern time series techniques, such as markov regime switching and the kalman filter, to model structural changes in the context of widely used concepts in finance. Statespace models as an important mathematical tool has been widely used in many different fields. The statespace representation of a linear dynamic system many dynamic models can usefully be written in what is known as a statespace form. A modern set of state space modeling tools are available in ssfpack which is. Applications of statespace modeling in macroeconomics prezi. In the sequel we shall assume that this distribution is independent of.
Yong zeng is a professor in department of mathematics and statistics at university of missouri at kansas city. From a technical point of view, state space models and the kalman filter play a. While linear regression models use exogenous variables. Statespace models in this lecture we consider statespace models, which often appear in macro, as well as other areas of economics. The ramseycasskoopmans model differs from the solowswan model in that the choice of consumption is explicitly microfounded at a point in time and so endogenizes the. Statespace models applications in economics and finance. A state space forecasting model with fiscal and monetary control september 1997 abstract in this paper we model the u. Smoothing and interpolation with the statespace model. We use monthly data for thirteen macroeconomic variables. You can find a comprehensive treatment in the book bayesian forecasting and dynamic linear models.
Youngx university of virginia february 21, 2012 abstract statespace models have been increasingly used to. In our model, foreign exchange rates are simultaneously determined by. Whether you are researching school selection, minimum wage, gdp, or stock trends, stata provides all the statistics, graphics, and data management tools needed to pursue a broad range of economic questions. Trend and cycle relationships with the usa and china pym manopimoke. This dissertation develops three new econometric models using bayesian state space representation model in order to apply to macroeconomics and international finance. Applications of state space models in finance universitatsverlag. A successful new textbook must be different enough from the old standards to give professors an incentive to switch, but must to similar enough to the old standards to keep the process of switching from requiring professors to throw away. Economics stack exchange is a question and answer site for those who study, teach, research and apply economics and econometrics. A practical guide to state space modeling jinlung lin institute of economics, academia sinica department of economics, national chengchi university march 2006 1. On applications of statespace modelling in macroeconomics. Linear state space models quantitative economics with python.
Financial econometrics using stata by simona boffelli and giovanni urga provides an excellent introduction to timeseries analysis and how to do it in stata for financial economists. One approach, in the classical framework, approximates the likelihood function. Macroeconomics and the reality of mixed frequency data. The book will appeal to graduate students and researchers studying statespace modeling in economics, statistics, and mathematics, as well as to finance professionals. Introduction to state space time series analysis practical. Applications in economics and finance statistics and econometrics for finance book 1 kindle edition by zeng, yong, wu, shu. Modeling macroeconomic series with regimeswitching models. The state variable for this economy turns out to be a crosssectional distribution of wealth across individuals. Economists have relied on stata for over 30 years because of its breadth, accuracy, extensibility, and reproducibility. Introduction to state space time series analysis practical econometrics. Chapter 1 develops a markov mixture model of macroeconomic fundamentals to analyze the shortrun dynamics of foreign exchange rates. The intention of this edited volume is to provide methodological development in statespace models, as well as study their applications, particularly in economics and finance.
This process, inspired from the fhmv model proposed by augustyniak et al. Section 2 deals with i the general state space model and its specific ssfpack state space representation, ii descriptions of some functions for. These models are usually designed to examine the comparative statics and dynamics of aggregate quantities such as the total amount of goods and services produced, total income earned, the level of employment of productive resources, and the level of prices. Markovian structure, linear gaussian state space, and optimal kalman filtering 47 3. Ramsey, with significant extensions by david cass and tjalling koopmans. Nevertheless there are very few contributions that discuss how those models can be used in practice and why they are such a powerful tool for practitioners. Perhaps the most novel feature of the book is its use of kalman filtering together. A state space forecasting model with fiscal and monetary. May 29, 2003 abstract thispapersurveyssomecommonstatespacemodelsusedinmacroeconomicsand. Aimed at researchers, graduate students, and industry practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in stata, and illustrates how to interpret the. The new 2015 release contains an updated version of chapter 6, now including material on gmm estimation and inference, as well as a new chapter 10, covering statespace models and the kalman filter. We treat the federal deficit as a proxy for fiscal policy and the fed funds rate as a proxy for.
Textbook treatments of state space models are given in harvey 1989, har. Economic fluctuations and growth, monetary economics, public economics robert solow has criticized our 2006 journal of economic perspectives essay describing modern macroeconomics in practice. This feature makes the model interesting as distributional aspects of all kinds of government policies can be. Download for offline reading, highlight, bookmark or take notes while you read lectures on behavioral macroeconomics. The mixed frequency var provides an alternative to commonly used state space models involving mixed frequency data.
Applications of state space models in finance this work is licensed under the creative commons license 2. The book includes nonlinear and nongaussian time series models. The authors show in this book that arima and classic adhoc models holtwinters, exponentially weighted moving averages, etc are particular cases of the gaussian dynamic linear models. The initial state distributions can be stationary, constant, or diffuse. After creating a statespace model, you can estimate any unknown parameters using timeseries data, obtain filtered states, smooth states, or. Model uncertainty, state uncertainty, and statespace models yulei luoy the university of hong kong jun niez federal reserve bank of kansas city eric r. The book will appeal to graduate students and researchers studying statespace modeling in economics, statistics, and mathematics, as well as to finance. The chapter concludes with a brief discussion of alternative approaches to nonlinear filtering. An introduction to state space time series analysis. The history of macroeconomics from keyness general. The state space representation and estimation of a timevarying parameter var with. This book presents recent advances in econometric methods that make feasible the estimation of models that have both features. Both statespace models and markov switching models have been highly productive paths for empirical research in macroeconomics and finance. The ramseycasskoopmans model, or ramsey growth model, is a neoclassical model of economic growth based primarily on the work of frank p.
Harvey, london school of economics and political science. Statespace models with regime switching department of. Due to the flexibility of the statespace approach, it is both a useful tool for research purposes and highly. A macroeconomic model is an analytical tool designed to describe the operation of the problems of economy of a country or a region. An introduction to state space time series analysis practical. In other words, the long run multiplier for constant government spending is higher than the shortrun one period multiplier. Model uncertainty, state uncertainty, and statespace models yulei luo, jun nie, and eric r. The book includes nonlinear and nongaussian time series models, regime switching and hidden markov models, continuous or discretetime state processes. Forecasting structural time series models and kalman filter. The most studied and applied state space models are gaussian dynamic linear models. The statespace model structure is a good choice for quick estimation because it requires you to specify only one input, the model order, n. Linear statespace models in macroeconomics and finance.
Furthermore, we derive its autocovariance structure and show its promising performance to fit 21 widely used macroeconomic data sets. State space modeling in macroeconomics and finance using. University of washington, seattle ronin capital llc, chicago free university, amsterdam august 4, 2002 this version. The model order is an integer equal to the dimension of xt and relates to, but is not necessarily equal to, the number of delayed inputs and outputs used in the corresponding linear difference equation. For more information please visit the publishers webpage. Search the worlds most comprehensive index of fulltext books. You can create a standard or diffuse statespace model using ssm or dssm, respectively. This book has been accepted as doctoral thesis by the faculty of economics and. 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. Take your hr comms to the next level with prezi video. Model uncertainty, state uncertainty, and statespace models. As godley and lavoie discuss, the model will converge towards a steady state if the government imposes a nonzero tax rate for t0 as shown below and the steady state gdp will have an even higher multiplier. Leading macroeconomics books, such as ljungqvist and sargent 2004 and canova 2007, provide a comprehensive treatment of state space models and related methods. Only issue is which set of states to use there are many choices.
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