scholarly journals DATING BUSINESS CYCLES IN LITHUANIA BY SIMPLE UNIVARIATE METHODS

Ekonomika ◽  
2011 ◽  
Vol 90 (2) ◽  
pp. 7-27
Author(s):  
Simas Kučinskas
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Business Cycles ◽  
Markov Switching ◽  
Switching Regression ◽  
Markov Switching Models ◽  
Switching Models ◽  
Economic Climate ◽  
International Data ◽  
Deep Recession

In this paper, we use three basic univariate techniques, namely, BBQ algorithm, time series filtering, and Markov-switching models, to date and characterize Lithuanian business cycles from 1995 to 2010. We find that economic growth in Lithuania was relatively balanced after the Russian Crisis until late 2006. After that, the economy experienced an extreme, although relatively brief, period of an overheated economic climate before plunging into a very deep recession at the end of 2008. Using the BBQ algorithm, we provide some simple comparisons of the two recessions as well as international data obtained in other studies. Our Markov-switching regression exercise, confirming the findings above, additionally indicates that recessionary periods may have shocks with non-finite variances and economically significant permanent effects on output.

scholarly journals Non-homogeneous hidden Markov-switching models for wind time series

2015 ◽  
Vol 160 ◽  
pp. 75-88 ◽  
Author(s):  
Pierre Ailliot ◽  
Julie Bessac ◽  
Valérie Monbet ◽  
Françoise Pène
Keyword(s):  
Time Series ◽  
Hidden Markov ◽  
Markov Switching ◽  
Markov Switching Models ◽  
Switching Models

scholarly journals Moments of Markov-Switching Models

2014 ◽  
Vol 61 (1) ◽  
pp. 131-140
Author(s):  
Anna Petričková
Keyword(s):  
Time Series ◽  
Regime Switching ◽  
Switching Time ◽  
Markov Switching ◽  
Central Moment ◽  
Time Series Models ◽  
Markov Switching Models ◽  
State Dependent ◽  
Switching Models ◽  
Dependent Model

Abstract In this paper we have focused on the class of regime-switching time series models with regimes determined by unobservable variables, concretely Markov-switching models. We have derived 2nd central moment of the MSW models for two cases-state-independent and state-dependent model

Predicting CCHF incidence and its related factors using time-series analysis in the southeast of Iran: comparison of SARIMA and Markov switching models

2014 ◽  
Vol 143 (4) ◽  
pp. 839-850 ◽  
Author(s):  
H. ANSARI ◽  
M. A. MANSOURNIA ◽  
S. IZADI ◽  
M. ZEINALI ◽  
M. MAHMOODI ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Health Centre ◽  
Markov Switching ◽  
Outbreak Detection ◽  
Related Factors ◽  
Sarima Model ◽  
Series Analysis ◽  
Markov Switching Models ◽  
Switching Models

SUMMARYCrimean-Congo haemorrhagic fever (CCHF) is endemic in the southeast of Iran. This study aimed to predict the incidence of CCHF and its related factors and explore the possibility of developing an empirical forecast system using time-series analysis of 13 years’ data. Data from 2000 to 2012 were obtained from the Health Centre of Zahedan University of Medical Sciences, Climate Organization and the Veterinary Organization in the southeast of Iran. Seasonal autoregressive integrated moving average (SARIMA) and Markov switching models (MSM) were performed to examine the potential related factors of CCHF outbreaks. These models showed that the mean temperature (°C), accumulated rainfall (mm), maximum relative humidity (%) and legal livestock importation from Pakistan (LIP) were significantly correlated with monthly incidence of CCHF in different lags (P < 0·05). The modelling fitness was checked with data from 2013. Model assessments indicated that the MSM had better predictive ability than the SARIMA model [MSM: root mean square error (RMSE) 0·625, Akaike's Information Criterion (AIC) 266·33; SARIMA: RMSE 0·725, AIC 278·8]. This study shows the potential of climate indicators and LIP as predictive factors in modelling the occurrence of CCHF. Our results suggest that MSM provides more information on outbreak detection and can be a better predictive model compared to a SARIMA model for evaluation of the relationship between explanatory variables and the incidence of CCHF.

Sign in / Sign up

Export Citation Format

Share Document