Modelling and forecasting Finnish pine sawlog stumpage prices using alternative time-series methods

2007 ◽  
Vol 37 (1) ◽  
pp. 178-187 ◽  
Author(s):  
Ramses Malaty ◽  
Anne Toppinen ◽  
Jari Viitanen
Keyword(s):  
Time Series ◽  
Structural Changes ◽  
Moving Average ◽  
Real Life ◽  
Autoregressive Moving Average ◽  
Vector Autoregressive ◽  
Structural Time Series ◽  
Short Run ◽  
Time Series Approach ◽  
Cyclical Behaviour

This study analyzes the Nordic pine (Pinus sylvestris L.) sawlog markets in four main regions in Finland by using monthly real stumpage prices over the period January 1995 to June 2005. The special emphasis is on the short-run forecasting of different time-series models up to April 2006. As a benchmark case, we compare the models performance in terms of root mean square forecasting errors (RMSE) of standard autoregressive moving average (ARIMA) and vector autoregressive (VAR) models to those of Harvey's (1989) structural time series model (STSM), which, unlike the standard methods, decomposes the time series into unobservable components, such as deterministic and stochastic trend and seasonal and cyclical behaviour. The results indicate that, in most cases, the STSM together with Kalman filter estimation outperform ARIMA and VAR estimation. With hindsight, stumpage markets experienced a price decrease during July–December 2005 and a turning point up in early 2006 that none of these models were able to accurately predict. Based on these results, it seems to be that, in real-life forecasting situations, it is quite difficult to get precise estimates for the stumpage prices solely using the time-series approach, irrespective of how flexible the models may be with respect to structural changes.

scholarly journals The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia

2021 ◽  
Vol 16 (3) ◽  
pp. 197-210
Author(s):  
Utriweni Mukhaiyar ◽  
Devina Widyanti ◽  
Sandy Vantika
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Series Regression ◽  
Vector Autoregressive ◽  
Daily Data ◽  
The Impact

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

ASYMPTOTIC THEORY OF STATISTICAL INFERENCE FOR TIME SERIES

2002 ◽  
Vol 18 (4) ◽  
pp. 993-999
Author(s):  
Offer Lieberman
Keyword(s):  
Time Series ◽  
Statistical Inference ◽  
Asymptotic Theory ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Finite Sample ◽  
Vector Autoregressive ◽  
Time Series Econometrics ◽  
Non Gaussian ◽  
Unit Root Models

Modern time series econometrics involves a diversity of models. In addition to the more traditional vector autoregressive (VAR) and autoregressive moving average (ARMA) systems, cointegration and unit root models are in widespread use for macroeconomic data, nonlinear and non-Gaussian models are popular for financial data, and long memory models are becoming more common in both macroeconomic and financial applications. Much econometric thought relates to issues of estimation and hypothesis testing, and so, in the absence of a usable finite sample theory (as is the case for the models just mentioned), an enormous amount of effort has been given to developing adequate asymptotics for statistical inference. There is often a lag between the introduction of a new model and the development of an asymptotic theory. In consequence, applied econometricians sometimes have to estimate time series models for which no asymptotic theory is available. For instance, multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models have been in use in empirical research for a while, and practitioners have been using asymptotic normality of estimators in this model even though a theoretical justification is not available.

scholarly journals Spatial Transmission Network Construction of Influenza-Like Illness Using Dynamic Bayesian Network and Vector-Autoregressive Moving Average Model

2020 ◽  
Author(s):  
Jianqing Qiu ◽  
Huimin Wang ◽  
Tao Zhang ◽  
Changhong Yang
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Intervention Strategy ◽  
Dynamic Bayesian Network ◽  
Sichuan Province ◽  
Autoregressive Moving Average ◽  
Transmission Network ◽  
Vector Autoregressive ◽  
Vector Autoregressive Moving Average ◽  
Time Lagged

Abstract Background: Influenza is an acute respiratory infection caused by an influenza virus, and the primary intervention strategy is seasonal vaccine. Due to various influenza strains and their rapid mutation each year, how to recognize the key population and timing of the vaccination becomes essential. Considering the importance of finding possible spreading directions and effects of influenza between cities for department of influenza prevention, the construction of influenza transmission network becomes meaningful.Methods: 21 cities in Sichuan province were divided into different learning communities according to whether they were adjacent to each other or not. In each community, the first-order conditional dependencies approximation algorithm was performed to learn the possible structure of the time-lagged correlations between different time series vectors of the ILI estimated weekly number, and the vector autoregressive moving average models were performed for learning the lag orders and parameters of the time-lagged correlations between different time series vectors in each community.Results: It detected a number of significant time-lagged correlations between cities in Sichuan province using two models, and the lag was from 1 week to 3 weeks. The parameters indicating the suspected propagation relationship were between -0.90 and 0.75, and the proportion of the negative values in parameters increased with time. Furthermore, the spreading routes learning from two models were almost in accordance with the traffic network of Sichuan province.Conclusions: This study proposed an innovative framework for exploring the potentially stable transmission routes between different regions and measuring specific size of the transmission effect. It could be used for the infectious disease key area confirmation by considering their adjacent areas’ incidence and the transmission relationship.

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