scholarly journals Between cointegration and multicointegration: Modelling time series dynamics by cumulative error correction models

2013 ◽  
Vol 31 ◽  
pp. 511-517
Author(s):  
Marcus Scheiblecker
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Error Correction Models ◽  
Cumulative Error

Auxiliary Modeling Procedures

2019 ◽  
pp. 136-153
Author(s):  
David McDowall ◽  
Richard McCleary ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Unit Root ◽  
Model Building ◽  
Causal Effect ◽  
Information Criteria ◽  
Unit Root Tests ◽  
Error Correction Models ◽  
Control Time ◽  
Model Building Strategy

Chapter 5 describes three sets of auxiliary methods that have emerged as add-on supplements to the traditional ARIMA model-building strategy. First, Bayesian information criteria (BIC) can be used to inform incremental modeling decisions. BICs are also the basis for the Bayesian hypothesis tests introduced in Chapter 6. Second, unit root tests can be used to inform differencing decisions. Used appropriately, unit root tests guard against over-differencing. Finally, co-integration and error correction models have become a popular way of representing the behavior of two time series that follow a shared path. We use the principle of co-integration to define the ideal control time series. Put simply, a time series and its ideal counterfactual control time series are co-integrated up the time of the intervention. At that point, if the two time series diverge, the magnitude of their divergence is taken as the causal effect of the intervention.

An Essay on Cointegration and Error Correction Models

1992 ◽  
Vol 4 ◽  
pp. 185-228 ◽  
Author(s):  
Robert H. Durr
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Empirical Models ◽  
Nonstationary Time Series ◽  
Longitudinal Analyses ◽  
Correlated Errors ◽  
Error Correction Models ◽  
Selective Perception ◽  
Post Hoc

For political scientists who engage in longitudinal analyses, the question of how best to deal with nonstationary time-series is anything but settled. While many believe that little is lost when the focus of empirical models shifts from the nonstationary levels to the stationary changes of a series, others argue that such an approach erases any evidence of a long-term relationship among the variables of interest. But the pitfalls of working directly with integrated series are well known, and post-hoc corrections for serially correlated errors often seem inadequate. Compounding (or perhaps alleviating, if one believes in the power of selective perception) the difficult question of whether to difference a time-series is the fact that analysts have been forced to rely on subjective diagnoses of the stationarity of their data. Thus, even if one felt strongly about the superiority of one modeling approach over another, the procedure for determining whether that approach is even applicable can be frustrating.

Time Series Analysis with Two or More Time Series

2017 ◽  
Author(s):  
Youseop Shin
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Error Correction ◽  
Memory Effect ◽  
Violent Crime ◽  
Crime Rates ◽  
Error Correction Models ◽  
Unemployment Rates ◽  
Independent Variables ◽  
Series Analysis

Chapter Six explains time series analysis with one or more independent variables. The dependent variable is the monthly violent crime rates and the independent variables are unemployment rates and inflation. This chapter discusses several topics related to the robustness of estimated models, such as how to prewhiten a time series, how to deal with trends and seasonal components, how to deal with autoregressive residuals, and how to discern changes of the dependent variable caused by independent variables from its simple continuity. This chapter also discusses the concepts of co-integration and long-memory effect and related topics such as error correction models and autoregressive distributive lags models.

scholarly journals HUBUNGAN PEMBANGUNAN EKONOMI TERHADAP KUALITAS LINGKUNGAN HIDUP DI PROVINSI JAWA TIMUR

2020 ◽  
Vol 2 (2) ◽  
pp. 58
Author(s):  
Selly Febriana Putri
Keyword(s):  
Time Series ◽  
Error Correction ◽  
Granger Causality ◽  
Cross Section ◽  
Time Series Model ◽  
Error Correction Models ◽  
Vector Error Correction ◽  
Vector Error ◽  
Vector Error Correction Models

Penelitian ini bertujuan untuk mengetahui seberapa besar hubungan pembangunan ekonomi yang difokuskan pada sisi laju pertumbuhan Sektor Pertanian, Industri, dan Transportasi terhadap Kualitas Lingkungan Hidup di Provinsi Jawa Timur. Metode analisis yang digunakan dalam penelitian ini adalah analisis data panel dengan menggabungkan data cross section dan time series. Model yang digunakan dalam penelitian ini adalah Vector Error Correction Models (VECM) dan metode yang dipilih dalam penelitian ini adalah Granger Causality. Hasil penelitian dari metode analisis Granger Causality menunjukkan bahwa hubungan kausal antara laju pertumbuhan sektor Industri terhadap Indeks Kualitas Lingkungan Hidup sebesar 0.0470 signifikan dalam taraf 5%. Sektor Transportasi memiliki hubungan kausal sebesar 0.0000 terhadap Indeks Kualitas Lingkungan Hidup signifikan dalam taraf 5%. Sektor Pertanian memiliki hubungan kausal terhadap Indeks Kualitas Lingkungan Hidup signifikan dalam taraf 5%. Hipotesis Environmental Kuznet Curve terbukti di Jawa Timur berbentuk U-terbalik yang melandai.

scholarly journals Demonstrating The Use Of Vector Error Correction Models Using Simulated Data

2012 ◽  
Vol 8 (4) ◽  
pp. 377
Author(s):  
Carl B. McGowan, Jr. ◽  
Izani Ibrahim
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Error Correction ◽  
Random Component ◽  
Error Correction Models ◽  
Vector Error Correction ◽  
Series Analysis ◽  
Use Of Time ◽  
Vector Error ◽  
Vector Error Correction Models

In this paper, we demonstrate the use of time series analysis, including unit roots tests, Granger causality tests, cointergation tests and vector error correction models. We generate four time series using simulation such that the data has both a random component and a growth trend. The data are analyzed to demonstrate the use of time series analysis procedures.

Sign in / Sign up

Export Citation Format

Share Document