The pairwise approach to model a large set of disaggregates with common trends
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Summary
Results indicate that the treatment of outliers and breaks in the context of the pairwise procedure is designed and its properties studied by Monte Carlo may considerably improve the procedure's performance when series are 'contaminated'.
- Type
- preprint
- Published
- 2014-05-01
- Cited by
- 0
- References
- 63
- OpenAlex
- https://openalex.org/W18201356
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:107371727
Keywords
Pairwise comparison, Monte Carlo method, Context (archaeology), Outlier, Computer science
References
- A Course in Time Series Analysis
- Forecasting Aggregates by Disaggregates
- The Cointegrated VAR Model: Methodology and Applications
- A PANIC Attack on Unit Roots and Cointegration
- A robust multivariate long run analysis of European electricity prices
- Cointegration Analysis in the Presence of Structural Breaks in the Deterministic Trend
- COMMON TRENDS AND COMMON CYCLES
- Dealing with Structural Breaks
- An analysis of the indicator saturation estimator as a robust regression
- Detecting Level Shifts in Time Series
- Inference on cointegrating ranks using lr and lm tests based on pseudo-likelihoods
- Testing for the Cointegrating Rank of a VAR Process With Structural Shifts
- Comparison of tests for the cointegrating rank of a VAR process with a structural shift
- Joint Estimation of Model Parameters and Outlier Effects in Time Series
- Estimating cross-section common stochastic trends in nonstationary panel data
- Outlier Detection in Cointegration Analysis
- Influential observations in cointegrated VAR models: Danish money demand 1973-2003
- Forecasting an aggregate of cointegrated disaggregates
- Sticky Prices and Monetary Policy: Evidence from Disaggregated U.S. Data
- Inferential Theory for Factor Models of Large Dimensions
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