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4 edition of Identification of continuous time rational expectations models from discrete time data found in the catalog.

Identification of continuous time rational expectations models from discrete time data

Lars Peter Hansen Hansen

Identification of continuous time rational expectations models from discrete time data

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  • 31 Currently reading

Published by Federal Reserve Bank of Minneapolis in [Minneapolis, Minn.] .
Written in English

    Subjects:
  • Time-series analysis.

  • Edition Notes

    StatementLars Peter Hansen and Thomas J. Sargent.
    SeriesFederal Reserve Bank of Minneapolis, Research Department staff report -- 73, Staff report (Federal Reserve Bank of Minneapolis. Research Dept. : Online) -- 73.
    ContributionsSargent, Thomas J.
    Classifications
    LC ClassificationsHB1
    The Physical Object
    FormatElectronic resource
    ID Numbers
    Open LibraryOL16412976M
    LC Control Number2007702540

    Representative Agent Models of Consumption and Leisure Choice under Uncertainty,” The Quarterly Journal of Economics, , 51– Hansen, L. and K. Singleton (): “Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models,” Econometrica, 50,


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Identification of continuous time rational expectations models from discrete time data by Lars Peter Hansen Hansen Download PDF EPUB FB2

This paper shows how the cross-equation restrictions implied by dynamic rational expectations models can be used to resolve the aliasing identification problem. Using a continuous time, linear-quadratic optimization environment, this paper describes how the resulting restrictions are sufficient to identify the parameters of the underlying.

Library of Congress Cataloging-in-Publication Data Hansen, Lars Peter. Rational expectations econometrics / by Lars Peter Hansen and Identification of Continuous Time Rational Expectations Models from Discrete Time Data into the forms that appear in this book.

Several of the ~hapters in. Identification of continuous time rational expectations models from discrete time data. By Lars Peter Hansen and Thomas J. Sargent. Get PDF ( KB) Abstract. This paper shows how the cross-equation restrictions implied by dynamic rational expectations models can be used to resolve the aliasing identification problem.

Author: Lars Peter Hansen and Thomas J. Sargent. Rational Expectations Models from Discrete Time Data by Lars Peter HANSEN and Thomas J. SARGENT 1.

Introduction III' This paper proves two propositions about identification in a con-tinuous time version of a linear stochastic rational expectations model. The model is a continuous time version of Lucas and Prescott (), in.

] BOOK REVIEWS solution of linear rational expectations models in discrete time. Chapter 9 is concerned with the identification problem of continuous-time rational expectations models from discrete-time series observations.

The last chapter in the volume (by Marcet) considers. most powerful technique for solving discrete time continuous state dynamic economic models. The collocation method is highly °exible. It may be used to solve discrete and continuous choice Markov decision models and rational expectations models.

Bounds and general constraints on variables can also be handled easily. equidistant discrete data. The identification problem is therefore more severe for continuous-time models compared with discrete-time models but, on the other hand, discrete-time models suffer from a lack of time invariance.

If Gaussianity is assumed, the general problem is to find a necessary and sufficient condition. Identification of continuous time rational expectations models from discrete time data rational expectations models from discrete time observations.

The method is important since various. Prediction Formulas for Continuous Time Linear Rational Expectations Models p. Notes p. Identification of Continuous Time Rational Expectations Models from Discrete Time Data p. Notes p. Temporal Aggregation of Economic Time Series p.

Notes p. Notes p. References p. Contributors p. Downloadable. This paper shows how the cross-equation restrictions implied by dynamic rational expectations models can be used to resolve the aliasing identification problem.

Using a continuous time, linear-quadratic optimization environment, this paper describes how the resulting restrictions are sufficient to identify the parameters of the underlying continuous time process when it is known.

Abstract. Most modelling of economic time series works with discrete time, yet time is in fact continuous. While in many instances simple intuitive connections exist between results with discrete time data and the underlying continuous time dynamics, it is possible for discretization to create bias or have unintuitive effects.

Discrete Time Models. We discuss briefly two extensions of the proportional hazards model to discrete time, starting with a definition of the hazard and survival functions in discrete time and then proceeding to models based on the logit and the complementary log-log transformations.

Discrete Hazard and Survival. Identification of continuous time rational expectations models from discrete time data by Lars Peter Hansen & Thomas J. Sargent On the mechanics of. A second class of discrete time continuous state dynamic model examined includes models of strategic gaming between a small number of individuals, ¯rms, or institutions.

Dynamic game models attempt to capture the behavior Examples of rational expectations models include arbitrage pricing models for ¯nancial and physical assets. Each exploits restrictions on an econometric model imposed by the hypothesis that agents within the model have rational expectations.

TABLE OF CONTENTS Exact linear rational expectations models; identification of continuous time rational expectations models from discrete time data; two difficulties with interpreting vector auto-regressions. The DSGE models in this book are based on the idea of RE and this is why this section introduces to the reader how to model rational expectations.

Although Lucas, see [11], is credited with introducing rational expectations into macroeconomics, the idea can be traced back to. If you estimated a discrete-time state-space model from time-domain data, then use d2c to transform it into a continuous-time model.

Linear ODEs (grey-box) models If the MATLAB ® file returns continuous-time model matrices, then estimate the ordinary differential equation (ODE) coefficients using either time- or frequency-domain data.

Prediction Formulas for Continuous Time Linear Rational Expectations Models by Lars Peter HANSEN and Thomas J. SARGENT 9. Identification of Continuous Time Rational Expectations Models from Discrete Time Data by Lars Peter HANSEN and Thomas J.

SARGENT Temporal Aggregation of Economic Time Series by Albert MARCET References Downloadable. This paper shows how the cross-equation restrictions delivered by the hypothesis of rational expectations can serve to solve the aliasing identification problem.

It is shown how the rational expectations restrictions uniquely identify the parameters of a continuous time model from statistics of discrete time models.

Methods for estimating continuous-time rational-expectations models from discrete-time data. Staff Report No. 59, Research Department, Federal Reserve Bank, Minneapolis, Minnesota, Identification of continuous time rational expectations models from discrete time data by Lars Peter Hansen & Thomas J.

Sargent The Elasticity of Substitution and Cyclical Behavior of Productivity, Wages, and Labor's Share. to be a continuous-time process describing the evolution of state variable(s), but the process is sampled, or observed, at discrete timeintervals.

The issues that arise, and the problems that are of interest, at theinterface between the continuous-time model and the discrete-time data are quite di fferent from those that we typically. Discrete-Time Valuation of American Options with Stochastic Interest Rates. Review of Financial Studies, Vol.

8, Issue. 1, p. Identification of continuous-time rational expectations models from discrete-time data. Tugnait, J.K. Global identification of continuous-time systems with unknown noise covar-iance. These include indirect methods where a discrete-time model is first obtained from the input-output data and then transformed into a continuous-time model, as well as direct methods where the.

Cobweb plot •A visual tool to study the behavior of 1-D iterative maps •Take x t-1 and x t for two axes •Draw the map of interest (x t=F(x t-1)) and the “x t=x t-1” reference line – They will intersect at “equilibrium points” •Trace how time series develop from an.

Identification of continuous time rational expectations models from discrete time data Staff Report, Federal Reserve Bank of Minneapolis View citations (3) Beyond demand and supply curves in macroeconomics Staff Report, Federal Reserve Bank of Minneapolis View citations (30) See also Journal Article in American Economic Review ().

Discretization of Continuous-Time State Variable Models Discrete-Time Models of Continuous-Time Systems Discrete-Time Approximations of Continuous-Time Systems Glossary Bibliography Biographical Sketches Summary When a digital controller is designed to control a continuous-time plant it is important.

Both discrete- and continuous-time models can be locally identified but not globally identified due to aliasing. Although aliasing has been considered mostly for continuous-time models observed with discrete-time data (Phillips,Hansen and Sargent, ), aliasing can also occur in discrete-time models observed with discrete-time data.

Standard treatments of continuous-time processes typically don't mention how to adapt the discrete-time linear model concepts and lag operator methods to continuous time. This book attempts that translation and exposits the techniques to make the translation from familiar discrete-time ideas.

Fitting the DTSA Model to Data First, Add a Continuous Predictor to the pp Dataset Grade at First IntercourseData (ALDA, Fig.p. ) PAS is a continuous time-invariant measure of parents’ antisocial behavior during the child’s formative years. Scores on the measure have been standardized to mean 0, standard deviation 1.

The rational expectations theory is the dominant assumption model used in business cycles and finance as a cornerstone of the efficient market hypothesis (EMH).

Get this from a library. Continuous-time linear models. [John H Cochrane; National Bureau of Economic Research.] -- I translate familiar concepts of discrete-time time-series to contnuous-time equivalent. I cover lag operators, ARMA models, the relation between levels and differences, integration and.

The choice of the survival model should be guided by the underlying phenomenon. In this case it appears to be continuous, even if the data is collected in a somewhat discrete manner. A resolution of one month would be just fine over a 5-year period.

Discrete time views values of variables as occurring at distinct, separate "points in time", or equivalently as being unchanged throughout each non-zero region of time ("time period")—that is, time is viewed as a discrete a non-time variable jumps from one value to another as time moves from one time period to the next.

This view of time corresponds to a digital clock that. Identification of Continuous Time Rational Expectations Models From Discrete Time Data Lars Peter Hansen and Thomas J.

Sargent Published in: _Rational expectations econometrics_ (. Boulder and Oxford: Westview Press,pp. "Prediction Formulas for Continuous Time Linear Rational Expectations Models," Lars Peter Hansen and Thomas J. Sargent, in L. Hansen and T. Sargent: Rational Expectations Econometrics.

Consider a discrete-time dynamic optimization model with an infinite time horizon in-dexed by t= 1 2∞. Suppose the state of the market at time t that is observable by the researcher can be summarized by random vector St ∈ S ⊆ RL.

Additionally, let εt ∈ E ⊆ RK+1 and ηt ∈ H ⊆ R denote the remaining state variables that are not ob. f0,1,Kg. After making the discrete choice Dt, the agent observes the realization of the random variable St, #t, Dt, and ht, the agent makes a continuous choice Ct 2C R and receives a payoff U(Dt,Ct,St,#t,ht).

The state St and the choices Dt and Ct are observed by the researcher, but #t and ht are only observed by the agent. The agent is forward-looking and discounts future. Software Item File Downloads Abstract Views; Last month: 3 months: 12 months: Total: Last month: 3 months: 12 months: Total: Code and data files for "A Life-Cycle Model of.

The book aims at advanced master students, graduate students, situations of continuous–time models with discrete data i.e. factor GARCH mod-els or ARMA models with discrete random variables. The chapter discusses re- rational expectations model and Campbell-Shiller present value model.

The decision of entry is discrete, and the decision of investment is continuous. Blevins () provides identification results of the class of dynamic discrete-and- continuous-choice models.

We show the discrete-and-continuous model is equivalent to the agents' making decisions that map every possible state to an outcome simultaneously.Continuous-Time Finance.

Robert C. Merton. Oxford, U.K.: Easil Blackwell, pp., $ (cloth). ISBN In his essay on “Ramsey as an Economist,” John Maynard Keynes alludes to economics in the following terms: 1 “ the delightful paths of our own most agreeable branch of moral sciences, in which theory and fact, intuitive imagination and practical judgment, are.I am interested in an overview over the connection and correspondence between time series models in continuous vs.

discrete time in finance. E.g. take ARMA(p,q) or GARCH(s,r) or ARMA(p,q)-GARCH(s,r) from discrete time, list their counterparts in continuous time and show how they are related (e.g.

how a continuous time process is a limit for the discrete time process as observation frequency.