2022
DOI: 10.3390/f13050761
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The Seasonal Fluctuation of Timber Prices in Hyrcanian Temperate Forests, Northern Iran

Abstract: Seasonal fluctuations play an important role in the pricing of a timber sale. A good understanding of timber price mechanisms and predictability in the timber market would be very practical for forest owners, managers, and investors, and is crucial for the correct functioning of the timber sector. This research aimed to analyze the effect of sale season on timber (sawlog and lumber) prices of high-value species groups (e.g., oriental beech, chestnut-leaved oak, common alder, velvet maple, and common hornbeam) … Show more

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Cited by 6 publications
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“…In contrast to traditional time series models, like autoregressive integrated moving average (ARIMA) model (Shumway et al, 2017 ), which often require strong pre-existing assumptions about the underlying data distribution and relationships between variables, deep learning techniques such as LSTMs can learn sequential representations without the need for such suppositions, making them effective in modelling complex, non-linear relationships (Siami-Namini et al, 2018b ; Karim et al, 2017 ). Moreover, unlike traditional time series models, which often use seasonal dummies to capture the effect of seasonality, including annual seasonality, ANN, such as LSTM models, do not typically use dummies for seasonal effects, as they can capture seasonal patterns implicitly (Zhang & Qi, 2005 ; Heshmatol Vaezin et al, 2022 ).…”
Section: Introductionmentioning
confidence: 99%
“…In contrast to traditional time series models, like autoregressive integrated moving average (ARIMA) model (Shumway et al, 2017 ), which often require strong pre-existing assumptions about the underlying data distribution and relationships between variables, deep learning techniques such as LSTMs can learn sequential representations without the need for such suppositions, making them effective in modelling complex, non-linear relationships (Siami-Namini et al, 2018b ; Karim et al, 2017 ). Moreover, unlike traditional time series models, which often use seasonal dummies to capture the effect of seasonality, including annual seasonality, ANN, such as LSTM models, do not typically use dummies for seasonal effects, as they can capture seasonal patterns implicitly (Zhang & Qi, 2005 ; Heshmatol Vaezin et al, 2022 ).…”
Section: Introductionmentioning
confidence: 99%
“…Nakajima used time-varying parameters and stochastic volatility to improve the VAR model and set the variance stochastic volatility, which is consistent with reality and enhances the reliability of model analysis results [35]. The equal interval impulse response function and time point impulse response function can be used to analyze the spillover effect of policy on economic growth, the time-varying impact of monetary policy on international capital flow, and the measurement of dynamic impact effects on stock price fluctuation [36][37][38][39]. The existing research in the field of economics reflects on the advantages of TPV and SV.…”
Section: Introductionmentioning
confidence: 99%