ARMA Memory Index Modeling of Economic Time Series

1988 ◽  
Vol 4 (1) ◽  
pp. 35-59 ◽  
Author(s):  
Herman J. Bierens
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Nonlinear Function ◽  
Random Variable ◽  
Relevant Information ◽  
Model Specification ◽  
Autoregressive Moving Average ◽  
Economic Time Series ◽  
Specification Testing ◽  
Economic Time

In this paper, it will be shown that if we condition a k-variate rational-valued time series process on its entire past, it is possible to capture all relevant information on the past of the process by a single random variable. This scalar random variable can be formed as an autoregressive moving average of past observations; Since economic data are usually reported in a finite number of digits, this result applies to virtually all economic time series. Therefore, economic time series regressions generally take the form of a nonlinear function of an autoregressive moving average of past observations. This approach is applied to model specification testing of nonlinear ARX models.

Komparasi Regresi Ekonometri pada Perekonomian Indonesia 2SLS, VEC, dan ARIMA

2002 ◽  
Vol 2 (2) ◽  
pp. 88-112
Author(s):  
Henry Viriya Surya ◽  
Prastowo Cahjadi
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Moving Average ◽  
Strong Support ◽  
Comparison Process ◽  
Economic Time Series ◽  
Vector Error Correction ◽  
Autoregressive Integrated Moving Average ◽  
Vector Error ◽  
Economic Time

This paper compares three models of econometric analysis on economy, in this case the Indonesian economy. The regression models are the two stage least squares (2SLS) which has a strong support from the economic theory of aggregate expenditure, the Vector Error Correction (VEC) and Autoregressive Integrated Moving Average (ARIMA) which both comes from the time series analysis, that do not have to be economic time series. The study tries to find out which are most suitable in analyzing the time series of Indonesian economy. After all the estimation and comparison process, we finally agree that the use of those different methods must be sinchronized with the purpose of the user's study of the economic time series.

scholarly journals Three essays on causality

2013 ◽  
Author(s):  
Χρήστος Μπούρας
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Relevant Information ◽  
Economic Time Series ◽  
Policy Makers ◽  
Second Moments ◽  
Wide Range ◽  
Causality Tests ◽  
Portfolio Managers ◽  
Economic Time

Nowadays, Granger causality tests are standard tools to investigate causal relationships between financial and economic time series. Econometric advances in the field have shown that the causal relationship between two variables is not invariant to the integration and cointegration properties of the processes nor the relevant information that is available and included in the analysis. Hence, various notions of Granger non-causality are developed in the context of linear bivariate or multivariate stationary or nonstationary discrete time processes. Several of these causality concepts are reviewed in this thesis. Their extended concept is contrasted to the standard Granger causality concept. A wide range of causality tests have been used to investigate the independence between the second moments of the time series. There is currently much interest in testing causality-in-variance by policy makers, portfolio managers, and academic researchers. […]

THE PROPERTIES OF KULLBACK–LEIBLER DIVERGENCE FOR THE UNIT ROOT HYPOTHESIS

2009 ◽  
Vol 25 (6) ◽  
pp. 1662-1681 ◽  
Author(s):  
Patrick Marsh
Keyword(s):  
Unit Root ◽  
Moving Average ◽  
Unit Roots ◽  
Model Specification ◽  
Economic Time Series ◽  
Finite Sample ◽  
Natural Choice ◽  
Leibler Divergence ◽  
Different Types ◽  
Economic Time

The fundamental contributions made by Paul Newbold have highlighted how crucial it is to detect when economic time series have unit roots. This paper explores the effects that model specification has on our ability to do that. Asymptotic power, a natural choice to quantify these effects, does not accurately predict finite-sample power. Instead, here the Kullback–Leibler divergence between the unit root null and any alternative is used and its numeric and analytic properties detailed. Numerically it behaves in a similar way to finite-sample power. However, because it is analytically available we are able to prove that it is a minimizable function of the degree of trending in any included deterministic component and of the correlation of the underlying innovations. It is explicitly confirmed, therefore, that it is approximately linear trends and negative unit root moving average innovations that minimize the efficacy of unit root inferential tools. Applied to the Nelson and Plosser macroeconomic series the effect that different types of trends included in the model have on unit root inference is clearly revealed.

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