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.

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.

scholarly journals Application of multiple regression models for inflation modelling

2021 ◽  
Vol 47 ◽  
Author(s):  
Ana Čuvak ◽  
Žilvinas Kalinauskas
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Regression Models ◽  
Stationary Time Series ◽  
Exogenous Variables ◽  
Price Inflation ◽  
Vector Error Correction ◽  
Vector Error ◽  
Multiple Regression Models ◽  
Augmented Dickey Fuller

This paper examines the Lithuanian consumer price inflation from 1996 January till 2006 December using a modern non-stationary time series and econometric theory.  The multiple regressionmodels are proposed for inflation modeling. The stationarity of Lithuanian inflation and the main explored exogenous variables are analyzed using the augmented Dickey–Fuller test.  All indicators are integrated of order one.  Vector error correction (VECM) model of Lithuanian inflation processes is investigated and proposed for inflation modeling.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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