Linear prediction and estimation methods for regression models with stationary stochastic coefficients

Swamy, P. A. V. B.; Tinsley, Peter

doi:10.1016/0304-4076(80)90001-9

Cited by 176 publications

(60 citation statements)

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“…We then estimate a random coefficient model of moeny velocity to examine the stability of the coefficients empirically. The random coefficient model approach we follow is Swamy and Tinsley's (1980). The results from both the empirical estimation and the theoretical simulation are compared to see whether the empirical behavior of the money velocity can be explained by the model developed in this paper.…”

Section: Some Empirical Resultsmentioning

confidence: 99%

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Capm Risk Adjustment for Exact Aggregation Over Financial Assets

1997

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Barnett originated the Divisia monetary aggregates, which in continuous time exactly track any monetary aggregator function under perfect certainty. With user costs measuring the prices of the services of components, Barnett's aggregates are based on Francois Divisia's derivation of the Divisia line integral from the first-order conditions for optimizing behavior by economic agents under perfect certainty. We derive an extended Divisia index from the first-order conditions (Euler equations) that apply under risk. Our extended Divisia index is the first extension of index number theory into the domain of decision making under risk and thereby produces a route for the extension of all index number theory to permit non-risk-neutrality. We generate simulated data from a modeled rational consumer and investigate the tracking accuracy of the extended Divisia index to the consumer's exact aggregator function.

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Section: Some Empirical Resultsmentioning

confidence: 99%

“…We estimate equation (8.21) with stochastically varying coefficients. We use the Swamy and Tinsley (1980) asymptotically efficient estimation procedure. Letting α t = (a ot , b t ), we assume, as in Swamy and Tinsley (1980):…”

Section: Some Empirical Resultsmentioning

confidence: 99%

Capm Risk Adjustment for Exact Aggregation Over Financial Assets

1997

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“…The concept of a dynamic, frictionless equilibrium is discussed by Frisch (1936). 6 Companion forms for a variety of linear forecasting models are illustrated in Swamy and Tinsley (1980). As shown in the first line of (6), given the matrix of the forecast model coefficients, H, and the discount factor, B, a single friction parameter determines a geometric pattern of rational dynamic responses, = 1 A(1).…”

Section: Rational Error Correction Under Geometric Frictionsmentioning

confidence: 99%

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confidence: 99%

Vector rational error correction

Kozicki

Tinsley

1999

Journal of Economic Dynamics and Control

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Under general conditions, linear decision rules of agents with rational expectations are equivalent to restricted error corrections. However, empirical rejections of rational expectation restrictions are the rule, rather than the exception, in macroeconomics. Rejections often are conditioned on the assumption that agents aim to smooth only the levels of actions or are subject to geometric random delays. Generalizations of dynamic frictions on agent activities are suggested that yield closed-form, higher-order decision rules with improved statistical fits and infrequent rejections of rational expectations restrictions. Properties of these generalized "rational" error corrections are illustrated for producer pricing in maufacturing industries. Keywords: Companion systems, error correction, producer pricing, rational expectations JEL classifications: C5, E3I am indebted for comments on an earlier version of this paper by colleagues and others, especially F. Diebold, S. Johansen, and S. Kozicki. Views presented are those of the author and do not necessarily represent those of the Federal Reserve Board. 1Intertemporal decision rules are indispensable in rational agent interpretations of macroeconomic behavior where a distinction is drawn between agent perceptions, summarized by agent forecasts of market events, and agent responses, subjected to dynamic constraints or "frictions." In theoretical macroeconomic models since Lucas (1976), rational expectations have become the benchmark standard for representing the unobserved forecasts of agents.Unfortunately, the record for empirical implementation of rational expectations models remains dismal. A survey of existent journal publications by Ericsson and Irons (1995) summarizes an extensive accumulation of empirical evidence against rational expectations, including frequent rejections of rational expectations overidentifying restrictions. A review of policy simulation models used by central banks and international agencies, such as documented in Bryant et al. (1993), indicates that many key rational expectations specifications are either imposed or fit by rough empirical calibrations.Macroeconomists have adopted a variety of responses to the absence of strong empirical support for rational expectations. One is to maintain the rational expectations hypothesis, while aiming to interpret a more limited subset of empirical regularities as discussed by Kydland and Prescott (1991). Another approach is to view rational expectations as a limiting case of complete information in a more general treatment of the information processing abilities of agents, such as the "bounded rationality" models of learning reviewed in Sargent (1993). Closely related is the position that rational expectations are more likely to prevail at low frequencies, a view compatible with tests of long-run theoretical restrictions in cointegrating relationships, as discussed by Watson (1994). Others reject the hypothesis of model-based rational expectations, such as the use in Ericsson and Hendry (19...

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“…is reported in which half of the covariance, V ij , is assigned to each of β i and β j , following Swamy and Tinsley (1980). The policy equation was also estimated over the full 1969 -1997 sample containing 280 Greenbooks.…”

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confidence: 99%