sadegh bafandeh imandoust; Mohsen Rastin
Abstract
Export growth hypothesis increased export can perform the role of “engine of economic growth” because it can increase employment, create profit, trigger greater productivity and lead to rise in accumulation of reserves allowing a country to balance their finances.
Export earnings assume vital importance ...
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Export growth hypothesis increased export can perform the role of “engine of economic growth” because it can increase employment, create profit, trigger greater productivity and lead to rise in accumulation of reserves allowing a country to balance their finances.
Export earnings assume vital importance not only for developing, but also for developed countries. Developed countries mainly export capital and final goods, while the main part of export of developing countries consists of mining-industry goods especially natural resources.
Wood and paper industry could be very important for economic development as non-oil export. Because of fraternity and value added aspect in these industries, it has especial effect on country economic.
The main objective of this study is to analyze the impact of changes in the real exchange rate on Wood and Paper Industry Export and to suggest policy proposals which may be useful for policymakers in non-oil export promotion issues.
Theoretical frame work
There is huge number of studies that investigate the impact of exchange rate on export. But according to our research objective we try mainly to focus on studies that investigate this relationship in case of oil dependent economies like Iran.
For investigating relationship between Real Exchange Rate and Export different methods can be utilized like: The Elasticities Approach, The Absorption Approach and MonetaryApproach.
Methodology
The vector autoregression (VAR) is an econometric model used to capture the linear interdependencies among multiple time series. VAR models generalize the univariate autoregressive model (AR model) by allowing for more than one evolving variable. All variables in a VAR are treated symmetrically in a structural sense (although the estimated quantitative response coefficients will not in general be the same); each variable has an equation explaining its evolution based on its own lags and the lags of the other model variables. VAR modeling does not require as much knowledge about the forces influencing a variable as do structural models with simultaneous equations: The only prior knowledge required is a list of variables which can be hypothesized to affect each other intertemporally.
This paper investigates the impact of the real exchange rate on wood industry export during 1977-2010 has been studied. For this purpose vector auto regressive (VAR) model has been used and by Johansson approach, supply and demand of export will be estimated, then by using of error correlation model (ECM) short term and long term relationship have been combined.
Results & Discussion
Based on findings of present study can be concluded that appreciating real exchange rate has positive and significant effect on supply and demand of wood and paper industries. In addition, tariff rate of import has negative effect on export supply.
Since promotion of non-oil export is one of the urgent issues of the strategic economic policy of Islamic Republic of Iran then findings of this study may be useful for policymakers.
Conclusions& suggestions
Real Exchange Rate and Wood& Paper Industry Export are strongly connected. Although, based on findings, increasing real exchange rate has positive and significant effect on supply and demand of wood and paper industries, but worth of national money should be moderate in short range.
In account of high rate of Competition in the world hiking the export price should be avoided.
Mahmoud Mousavi Shiri; Sadegh Bafandeh Imandoust; Mohammad Bolandraftar Pasikhani
Abstract
Due to the effects of companies’ financial distress on stakeholders, financial distress prediction models have been one of the most attractive scopes in financial research. In recent years, after the global financial crisis, the number of bankrupt companies has risen. Since companies' financial distress ...
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Due to the effects of companies’ financial distress on stakeholders, financial distress prediction models have been one of the most attractive scopes in financial research. In recent years, after the global financial crisis, the number of bankrupt companies has risen. Since companies' financial distress is the first stage of bankruptcy, using financial ratios for predicting financial distress have attracted too much attention of the academics as well as economic and financial institutions.
Although in recent years studies on predicting companies’ financial distress in Iran have been increased, but most efforts have exploited traditional statistical methods; and just a few studies have used nonparametric methods. Recent studies demonstrate machine learning techniques outperform traditional statistical methods.
In the present study k-Nearest Neighbor classification method, derived from the field of data mining, is employed to predict financial distress of Tehran Stock Exchange listed companies during 2005-2008. Experimental results show that k-Nearest Neighbor is able to predict corporate financial distress with high accuracy.
Sadegh Bafandeh Imandoust; Ghasemie Hesameddin
Abstract
Examination of demand of money and recognition of important factors, is one of the
important problems in macroeconomic. Identifying important factors that can affect
demand money function, beside other economic factors, guaranty successfulness for
economic policies. Semblance of many studies have ...
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Examination of demand of money and recognition of important factors, is one of the
important problems in macroeconomic. Identifying important factors that can affect
demand money function, beside other economic factors, guaranty successfulness for
economic policies. Semblance of many studies have been carried out to specify
factors affecting demand of money, indicate some scattering on choosing important
factors ,so results are different. Lack of knowledge about descriptive variables and
correct model of money demand, produce wrong model.
To avoid a prejudgment on the effective factors of Iran’s money demand (based on a
specific theory), Bayesian Model Averaging is utilized. As it is common, this
approach is based on the estimation of a regression model for many times (here
14,000) and estimation of coefficients with Bayesian Model Averaging. Data period
is between 1976 -2007.
Results show that GNP, CPI (consumer price index), formal exchange rate, ratio of
budget deficit on GNP, lagged dependent variable and CPI with lagged have
significant effects on demand of money.
