Wood import and export and its relation to major macroeconomics variables in Iran

Limaei, Soleiman Mohammadi; Heybatian, Roghayeh; Vaezin, Seyed Mahdi Heshmatol; Torkman, Javad

doi:10.1016/j.forpol.2011.03.001

Cited by 16 publications

(5 citation statements)

References 5 publications

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“…Emang et al (2010) used univariate time series models, including Holt-wintersseasonal, ARAR algorithm, and seasonal ARIMA modeling to forecast the future volume of exporting wooden products (including chipboard and moulding) in Malaysia. Mohammadi Limaei et al (2011) analyzed time series and an autoregressive procedure to predict the export and import of wood in Iran. Tajdini et al (2014) forecasted the consumption of wood-based panels in Iran using exponential smoothing and ARIMA models until 2014.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Tavakkoli¹,

Hemmasi²,

Talaeipour³

et al. 2015

BioResources

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The performance of the Autoregressive Integrated Moving Average )ARIMA) model and Double and Holt-winters exponential smoothing techniques for forecasting the consumption of particleboard in Iran are compared. Annual time series data from 1978 to 2009 in the modeling process, and observations from 2010 to 2012 were used to check the accuracy of the models' forecasting performance. Also, the models' performances were calculated in terms of RMSE criterion, and the consumption of particleboard in Iran was forecasted up to the year 2017 using the most appropriate model. The results of comparing different forecast models showed that the ARMA (2,1) model yielded the lowest RMSE value compared to the other two models, which makes it more appropriate for the prediction of consumption of particleboard in Iran. Results also revealed that there might be an increasing trend in the consumption of particleboard, i.e., an average annual increasing rate calculated as 5% for particleboard. Thus, it was predicted that the consumption of particleboard would increase from 901,652 m 3 in 2012 to 1,178,320 m 3 in 2017.

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Section: Introductionmentioning

confidence: 99%

Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Tavakkoli¹,

Hemmasi²,

Talaeipour³

et al. 2015

BioResources

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show abstract

“…Similar papers examining the relationship between the volume of imported or exported goods and macroeconomic variables continue to be available, such as Limaei and Todshki and they all came to a positive conclusion [9,10]. This suggests that there is some relationship between import and export goods and macroeconomic variables, and relevant studies can be conducted.…”

Section: Introductionmentioning

confidence: 87%

Relationship between Tea Export and Major Macroeconomics Variables in China

Liang

2023

AEMPS

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Along with the impact of the COVID-19, China's import and export trade suffered a huge impact in 2020. Among them, tea exports have seen a rare decline. In order to recover the tea industry, this study analyzes the factors influencing China's tea exports. In economic theory, macroeconomic variables occupy an important position, and they allow a reliable assessment of the overall development of the industry at the macro level. In this paper, several macroeconomic variables from 2013 to 2021 are selected and used as independent variables, and the tea export volume in the same period is used as the dependent variable for regression analysis. However, the calculation of VIF revealed that the conventional multiple linear regression would be disturbed by multicollinearity, leading to unsatisfactory results. Considering the problem of multicollinearity, this study uses ridge regression and K-folds cross validation for processing and derive the corresponding ridge regression coefficients so that the significance of each variable can be examined. The results of the analysis are that GDP is most closely related to tea exports, while population size and local tea production are slightly less closely related, and exchange rate is the least related variable.

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“…However, one of the limitations of this approach is that it assumes the stationarity of the data, a condition that is usually violated for economic data (Tzanova 2017). For example, by using a multivariate regression model, Limaei et al (2011) investigated the relationship between Iranian wood import and export, and macroeconomic variables such as GDP, world oil prices, population size, and domestic wood production. Their results showed that while population size, GDP, and domestic wood production had an impact on wood exports, GDP had the highest influence on Iranian wood imports.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting models for Quebec’s lumber demand and exports using multivariate regression technique

Ferguene

Lehoux

Dadouchi

2023

The Forestry Chronicle

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The business environment of the forest products industry is impacted by a variety of factors that makes it hard to predict the market’s behavior. Moreover, companies operating in this industry are continuously seeking to improve their understanding of the market by transforming available data into valuable knowledge and meaningful forecasts. This paper proposes a methodology to extract and use open data for Quebec’s lumber demand and exports forecasts using multivariate regression techniques. A number of methods were applied to estimate the models’ coefficients using a training data set, namely the Ordinary Least Squares method with a “backward” variable selection approach, LASSO and RIDGE regressions, and the Two-Step Least Squares method. Then their forecast accuracy was tested on an out-of-sample data set. The best selected models in terms of forecast accuracy succeeded in predicting Quebec lumber demand and exports on the testing data set, with a Root Mean Square Error of 0.12 and 0.08 respectively, and a Mean Absolute Error of 0.1 and 0.06 respectively. Furthermore, the developed data visualization tool appeared as a powerful tool to highlight the reliable forecasts generated by the models, while deducing relevant information through interactive graphics. Such a visualization tool could therefore help in better understanding the market when making decisions related to the evolution of lumber demand.

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Wood import and export and its relation to major macroeconomics variables in Iran

Cited by 16 publications

References 5 publications

Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Forecasting of Particleboard Consumption in Iran Using Univariate Time Series Models

Relationship between Tea Export and Major Macroeconomics Variables in China

Forecasting models for Quebec’s lumber demand and exports using multivariate regression technique

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