Estimation of Continuous and Discrete Time Co-integrated Systems with Stock and Flow Variables

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Daniel González Olivares ◽  
Isai Guizar

Abstract This paper proposes an exact discrete time error correction model for co-integrated systems in continuous time and outlines a computationally efficient algorithm that leads to the Gaussian estimates of the model’s parameters. Its performance in estimation is assessed by contrasting our estimates with those obtained after applying Johansen’s discrete time approach to cointegrated systems. The data, for analysis, consist of two simulated systems; one comprised entirely of stock variables and another one formed by flow variables. In the results, we show that for the system with stock variables Johansen’s approach and ours perform similarly. For the system with flow variables, however, Johansen’s estimates show a persistent estimation bias with negligible improvements in larger samples, while ours yields a smaller bias that lowers as the sample size increases. As our model incorporates a moving average component in the error term that permits full dynamics, we argue that Johansen’s bias reflects the cost of ignoring aggregation in the specification.

1988 ◽  
Vol 4 (1) ◽  
pp. 108-124 ◽  
Author(s):  
Peter Zadrozny

For purposes of maximum likelihood estimation, we show how to compute the Gaussian likelihood function when the data are generated by a higher-order continuous-time vector ARMAX model and are observed as stocks and flows at different frequencies. Continuous-time ARMAX models are analogous to discrete-time autoregressive moving-average models with distributed-lag exogenous variables. Stocks are variables observed at points in time and flows are variables observed as integrals over sampling intervals. We derive the implied state-space model of the discrete-time data and show how to use it to compute the Gaussian likelihood function with Kalman-filtering, prediction-error, decomposition of the data.


2009 ◽  
Vol 25 (4) ◽  
pp. 1030-1049 ◽  
Author(s):  
Marcus J. Chambers

This paper derives an exact discrete time representation corresponding to a triangular cointegrated continuous time system with mixed stock and flow variables and observable stochastic trends. The discrete time model inherits the triangular structure of the underlying continuous time system and does not suffer from the apparent excess differencing that has been found in some related work. It can therefore serve as a basis for the study of the asymptotic sampling properties of estimators of the model's parameters. Some further analytical and computational results that enable Gaussian estimation to be implemented are also provided.


2011 ◽  
Vol 28 (1) ◽  
pp. 219-238 ◽  
Author(s):  
Marcus J. Chambers ◽  
Michael A. Thornton

This paper derives exact discrete time representations for data generated by a continuous time autoregressive moving average (ARMA) system with mixed stock and flow data. The representations for systems comprised entirely of stocks or of flows are also given. In each case the discrete time representations are shown to be of ARMA form, the orders depending on those of the continuous time system. Three examples and applications are also provided, two of which concern the stationary ARMA(2, 1) model with stock variables (with applications to sunspot data and a short-term interest rate) and one concerning the nonstationary ARMA(2, 1) model with a flow variable (with an application to U.S. nondurable consumers’ expenditure). In all three examples the presence of an MA(1) component in the continuous time system has a dramatic impact on eradicating unaccounted-for serial correlation that is present in the discrete time version of the ARMA(2, 0) specification, even though the form of the discrete time model is ARMA(2, 1) for both models.


2017 ◽  
Vol E100.C (10) ◽  
pp. 858-865 ◽  
Author(s):  
Yohei MORISHITA ◽  
Koichi MIZUNO ◽  
Junji SATO ◽  
Koji TAKINAMI ◽  
Kazuaki TAKAHASHI

Psychometrika ◽  
2021 ◽  
Author(s):  
Oisín Ryan ◽  
Ellen L. Hamaker

AbstractNetwork analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.


1967 ◽  
Vol 4 (1) ◽  
pp. 192-196 ◽  
Author(s):  
J. N. Darroch ◽  
E. Seneta

In a recent paper, the authors have discussed the concept of quasi-stationary distributions for absorbing Markov chains having a finite state space, with the further restriction of discrete time. The purpose of the present note is to summarize the analogous results when the time parameter is continuous.


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