TESTING STRICT STATIONARITY WITH APPLICATIONS TO MACROECONOMIC TIME SERIES

2017 ◽  
Vol 58 (4) ◽  
pp. 1227-1277 ◽  
Author(s):  
Yongmiao Hong ◽  
Xia Wang ◽  
Shouyang Wang
Keyword(s):  
Time Series ◽  
Macroeconomic Time Series ◽  
Strict Stationarity

scholarly journals QUANTILE DOUBLE AUTOREGRESSION

2021 ◽  
pp. 1-47
Author(s):  
Qianqian Zhu ◽  
Guodong Li
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Simulation Studies ◽  
Finite Sample ◽  
Conditional Quantile ◽  
New Model ◽  
Conditional Quantiles ◽  
Financial Time ◽  
Portmanteau Tests ◽  
Strict Stationarity

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation is suggested. Two promising properties of the original double autoregressive model are shown to be preserved. Based on the quantile autocorrelation function and self-weighting concept, three portmanteau tests are constructed to check the adequacy of the fitted conditional quantiles. The finite sample performance of the proposed inferential tools is examined by simulation studies, and the need for use of the new model is further demonstrated by analyzing the S&P500 Index.

scholarly journals Priors for Macroeconomic Time Series and Their Application

1994 ◽  
Vol 10 (3-4) ◽  
pp. 609-632 ◽  
Author(s):  
John Geweke
Keyword(s):  
Time Series ◽  
Prior Distribution ◽  
Odds Ratio ◽  
General Trend ◽  
Stationary Model ◽  
Posterior Odds ◽  
Posterior Odds Ratio ◽  
The Family ◽  
Macroeconomic Time Series ◽  
Posterior Moments

This paper takes up Bayesian inference in a general trend stationary model for macroeconomic time series with independent Student-t disturbances. The model is linear in the data, but nonlinear in parameters. An informative but nonconjugate family of prior distributions for the parameters is introduced, indexed by a single parameter that can be readily elicited. The main technical contribution is the construction of posterior moments, densities, and odd ratios by using a six-step Gibbs sampler. Mappings from the index parameter of the family of prior distribution to posterior moments, densities, and odds ratios are developed for several of the Nelson–Plosser time series. These mappings show that the posterior distribution is not even approximately Gaussian, and they indicate the sensitivity of the posterior odds ratio in favor of difference stationarity to the choice of the prior distribution.

scholarly journals Design of Seasonal Adjustment Filter Robust to Variations in the Seasonal Behaviour of Time Series

2017 ◽  
Vol 33 (1) ◽  
pp. 155-186
Author(s):  
Marcela Cohen Martelotte ◽  
Reinaldo Castro Souza ◽  
Eduardo Antônio Barros da Silva
Keyword(s):  
Time Series ◽  
Good Alternative ◽  
Superior Performance ◽  
Seasonal Adjustment ◽  
Artificial Data ◽  
Seasonal Fluctuations ◽  
Seasonal Behaviour ◽  
Macroeconomic Time Series ◽  
The One ◽  
Made In

Abstract Considering that many macroeconomic time series present changing seasonal behaviour, there is a need for filters that are robust to such changes. This article proposes a method to design seasonal filters that address this problem. The design was made in the frequency domain to estimate seasonal fluctuations that are spread around specific bands of frequencies. We assessed the generated filters by applying them to artificial data with known seasonal behaviour based on the ones of the real macroeconomic series, and we compared their performance with the one of X-13A-S. The results have shown that the designed filters have superior performance for series with pronounced moving seasonality, being a good alternative in these cases.

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