Dynamic Simultaneous Equation Model Research Articles

On univariate time series methods and simultaneous equation econometric models

Systematic testing of the implications of the structural assumptions for the properties of the final equations and transfer functions associated with a dynamic econometric model, as proposed by Zellner and Palm (1974–1975), proved to be useful in model building. This paper contains several remarks on the use of univariate time series methods to empirically check out the implications of a linear dynamic simultaneous equation model.

Testing the dynamic specification of an econometric model with an application to Belgian data

In this paper, we analyze the Final Equation and Transfer Function form associated with a linear dynamic simultaneous equation model and use the empirical findings as a guidance to a structural form specification in accordance with the information in a sample of monthly Belgian data.

Testing for Serial Correlation in Dynamic Simultaneous Equation Models

Testing for Serial Correlation in Dynamic Simultaneous Equation Models

Several efficient two-step estimators for the dynamic simultaneous equations model with autoregressive disturbances

Several asymptotically efficient methods are suggested on both the full and the limited information approach to estimate the simultaneous equations model in which the lagged endogenous variables and the autoregressive disturbances coexist. They are two-step procedures and do not involve iterations. A method is suggested also for the case where any portion of the autoregressive parameter matrix is specified to be zero. Since the consistency and efficiency depend upon the asymptotic, local identifiability, the necessary and sufficient condition is derived for it. It does not depend on the exclusion of the lagged endogenous variables.

Corrigendum: On the Global Identification of the Dynamic Simultaneous Equations Model with Stationary Disturbances

Corrigendum: On the Global Identification of the Dynamic Simultaneous Equations Model with Stationary Disturbances

On the Global Identification of the Dynamic Simultaneous Equations Model with Stationary Disturbances

On the Global Identification of the Dynamic Simultaneous Equations Model with Stationary Disturbances

Asymptotic properties of full information estimators in dynamic autoregressive simultaneous equation models

Asymptotic properties of full information estimators in dynamic autoregressive simultaneous equation models

Stochastic Specification in an Aggregate Demand Model of the United Kingdom

An eight equation dynamic model of aggregate demand in the United Kingdom is estimated by a variety of methods which make different assumptions about, and provide different treatments of, the problems of simultaneity and serial correlation, the latter being both within and between equations. Although the system appears to perform quite well on conventional criteria, the alternative estimators reveal a number of misspecifications and demonstrate the sensitivity of the results to estimator choice. The paper also considers the methodological problems involved in estimating dynamic simultaneous equation models with possibly auto-correlated errors using seasonally unadjusted quarterly data.

Time series analysis and simultaneous equation econometric models

In this paper we take up the analysis of dynamic simultaneous equation models (SEM’s) within the context of general linear multiple time series processes such as studied by Quenouille (1957). As noted by Quenouille, if a set of variables is generated by a multiple time series process, it is often possible to solve for the processes generating individual variables, namely the ‘final equations’ of Tinbergen (1940), and these are in the autoregressive-moving average (ARMA) form. ARMA processes have been studied intensively by Box and Jenkins (1970). Further, if a general multiple time series process is appropriately specialized, we obtain a usual dynamic SEM in structural form. By algebraic manipulations, the associated reduced form and transfer function equation systems can be derived. In what follows, these equation systems are presented and their properties and uses are indicated. It will be shown that assumptions about variables being exogenous, about lags in structural equations of SEM’s, and about serial correlation properties of structural disturbance terms have strong implications for the properties of transfer functions and final equations that can be tested. Further, we show how large sample posterior odds and likelihood ratios can be used to appraise alternative hypotheses. In agreement with Pierce and Mason (1971), we believe that testing the implications of structural assumptions for transfer functions and, we add, final equations is an important element in the process of iterating in on a model that is reasonably in accord with the information in a sample of data. To illustrate these general points and to provide applications of the above methods, a

A Comparison of Some Limited Information Estimators for Dynamic Simultaneous Equations Models with Autocorrelated Errors

In this paper we consider a number of estimators for the linear structural simultaneous equations model containing lagged endogenous variables and autocorrelated errors. The special case is considered in which the matrix of autocorrelation coefficients of the (vector) structural error process is diagonal. We consider the two stage least squares analogue (C2SLA) in this case, its relation to the estimators proposed earlier by Fair, the estimator obtained when the autocorrelation matrix is known, and a number of instrumental variables estimrators, as well as a modification of the method of scoring which yields an estimator that is asymptotically equivalent to the C2SLSA estimator. The asymptotic distributions of such estimators are obtained and we determine their relative asymptotic efficiencies.