habib morovat

Housing price shocks of one region may spread to the housing market of neighboring regions or geographical areas bounded by political border and lead to the formation of price shocks in shock-receiving areas. The housing policies may not be effective when implemented regionally and separately if there is a confirmed network connection between the housing markets of regions. Price shocks to a housing market spreads with a delayto interconnected housing markets, ultimately resulting in the diffusion of the price shock across the entire of the housing network. This researchaims to investigate the housing network between selected cities (centers of the country's provinces) using the VAR model and Forecast Error Variance Decomposition (FEVD). The results of this research confirm the existence of a network connection between the housing markets of the country's provinces, and unlike previous studies, the results show that it is not only the city of Tehran that spreads price shocks to other regions, but also cities such as Karaj, Shiraz, and Arak spread price shocks to other cities. In addition, the results suggest that the recent price jump, since 2019 has significantly increased the density of the housing network in the country. Based on this, price shocks are expected to be distributed more quickly throughout the country.

Over the last decade, there has been a clear increase in ICT expenditures by households, both in value and as a proportion of total expenditure. Such a trend, however, has not affected all households in the same way. This study analyzes the socio-economic determinants of urban household expenditures on ICT goods and services in Iran in the year 2019 based on microdata from the Household Budget Surveys. To achieve this, we have applied Heckman's two-stage model, aiming to identify the determinants affecting the likelihood of spending on ICT and the amount spent. Our analysis further dissects ICT spending into its constituent components, including IT goods, IT services, communication goods, and communication services. Based on the results, per capita income has a significant and positive effect both on the probability of spending and the level of per capita ICT expenditures. Moreover, households led by male heads are more likely to engage in ICT spending, although their actual expenditures are lower. Households with larger sizes and households which have a married head are more likely to spend on ICT. The impact of education level and age of the household’s head on both the probability to use and the amount spent is positive and significant. There is a negative quadratic relationship between the age of the household’s head and both the probability of spending and the level of per capita ICT expenditures. Furthermore, consumption economies of scale exist in ICT expenditures. The likelihood of spending on all types of ICT components is positively influenced by per capita income, size of the household, education and age of the household’s head. Concerning the level of per capita expenditures on IT goods and communication goods and services, they are higher for households who have higher per capita income. There is a negative quadratic relationship between the age of the household’s head and both the probability of spending and the level of communication services expenditures. Households with highly educated heads tend to allocate higher budgets to communication goods and services. Lastly, consumption economies of scale are observed in IT goods, IT services, and communication services.

Risk and uncertainty are key factors in making economic decisions. Since individual attitudes towards risk can greatly influence choices, it is crucial to understand the determinants of such preferences in order to predict and comprehend individuals’ behavior. The present study aimed to investigate the impact of several factors on individuals’ attitudes towards risk, specifically the degree of risk aversion, by examining individuals’ optimism and patience (time preference). The study used a questionnaire to collect data from a sample of 304 individuals in Iran selected through random sampling. The research method was a multivariate regression model. The findings indicated that both optimism and income have a significant negative effect on risk aversion, while age has a significant positive effect. Furthermore, the study found that patience does not have a significant impact on risk aversion.IntroductionRisk and uncertainty are critical factors that heavily influence most economic decisions, including investment, education, employment, and the decision to buy a house or insurance. Such decisions involve an element of risk, so they are highly influenced by individuals’ attitudes towards risk. In developing countries, such as Iran, most individuals typically experience unstable incomes, limited access to insurance, and possess few assets to cushion the impact of severe economic shocks. As a result, individuals in these circumstances are more exposed to risk, and these factors can significantly influence their attitudes towards risk. Understanding the determinants of these preferences is crucial to comprehending and predicting people’s behavior, as different attitudes towards risk lead to different choices. The present study was to examine how certain factors, such as optimism and patience (time preference rate), influence individuals’ attitudes towards risk. In addition, socio-economic variables were included as control variables to account for their potential impact.Materials and MethodsTo gather data on individuals’ degrees of risk aversion, optimism, and patience, this study used a questionnaire based on internationally recognized surveys. The model was then estimated by the general multivariate regression through the ordinary least squares (OLS) method.Results and DiscussionThe descriptive information related to demographic variables is presented in Table 1.  Table 1:  Frequency distribution of demographic sVariableVariable levelFrequencyRelative FrequencyGenderMale15549Female16051Total315100Marital statusSingle21970Married9630Total315100AgeLess than 20 years165Between 20-30 years17255Between 30-40 years10032Above 40 years278Total315100Level of educationHigh school41Diploma–BA9931BA–MA13944MA–PhD7323Total315100Employment statusUnemployed5116Retired10Housewife279School student72University student13142Employed9831Total315100The economic situationIncome below 1 million Tomans11236Income between 1–3 million Tomans10637Income between 3–6 million Tomans6320Income above 6 million Tomans3411Total315100Source: research findingsModel  EstimationThe OLS method was used to estimate the model.Table  2.  Model estimation resultsRA                                                                The dependent variableprobabilityt-statCoefficientsVariables0.00-3.71-0.027 * * *Optimism0.0023.070.0003 * * *Wealth0.000-63.60.29 * * *Income0.418-0.81-0.017Patience0.024-26.2-0.19 * *Gender0.0222.310.23 * *Single0.0005.630.044 * * *Age0.2151.240.06Education0.00075.52.16 * * *_Cons29.13F(8,295)304Number of obs0.000Prob > F0.44R-squared0.63Root MSE0.42Adj R-squared     * The coefficient is significant at  10 % level, * * The coefficient is significant at  5 % level and *** The coefficient is significant at  1 % level.Source: research calculationsThe Brush-Pagan and VIF test show that there is no heteroskedasticity and collinearity at estimated residuals.As shown in the table, as the individual’s level of optimism increases, their degree of risk aversion decreases, which is consistent with previous research conducted by Felten and Gibson (2014) and Duhman et al. (2018). In addition, the study found that wealth has a direct and significant impact on risk aversion in Iran, which aligns with the findings of Agassi et al. (2015) and Qanbili (2016). However, this result contradicts the research conducted by Ronald and Grable (2010), and therefore, the effect of wealth on risk aversion warrants further discussion and reflection.Previous research suggests that there is a likelihood that the effect of wealth on risk aversion in Iran may be opposite to that observed in other countries. This could potentially be attributed to errors in measuring wealth In Iran, where information regarding individuals’ assets and wealth is often unclear. In this respect, the present study relied on indicators such as car and house ownership and their estimated values, which were self-reported by the participants and might be subject to bias.The study findings indicated that income has a significant and negative impact on risk aversion in Iran, which is aligned with previous research conducted by Wright (2012; 2014) and Shah et al. (2020). Moreover, it was found that gender has a significant effect on risk aversion, with females being more risk-averse than males. This finding is consistent with Banir and Newbert (2016), Hosseinnejad and Haddadi (2016), and Mohammadi-Majed (2018).The findings also revealed that age has a significantly positive impact on risk aversion in Iran, which is in line with the results of Dankers and Van Suest (1999) and Menadia et al. (2016). Finally, the results showed that time preference rate and education do not have a significant impact on risk aversion in Iran.ConclusionThis research examined the impact of several factors on individuals’ risk aversion in Iran. The investigation of the research hypotheses demonstrated that variables such as optimism and income have a significantly inverse relationship with risk aversion, with higher levels leading to decreased risk aversion. Wealth and age have a significantly positive impact on risk aversion, with higher levels leading to increased risk aversion. Furthermore, the variables of time preference rate and education were found to have no significant effect on risk aversion in Iran. The study also found that married individuals are more risk-averse than single ones, and females are more risk-averse than males.The results indicated that young people, males, and the individuals with higher incomes and lower wealth tend to accept risk more readily. The findings can provide fresh insight for investment consulting and insurance companies in Iran.

Nowadays, Information and Communications Technology is growing rapidly due to the considerable increase in using knowledge-based theories in all countries, especially in developing economies such as Iran. As a non-competitive technology with unlimited use capacity, Information and Communications Technology entry in the general application and social life shows its potential to affect social welfare. This study will evaluate the impact of Information and Communications Technology on Sen's Social Welfare Index in Iranian provinces using data from 2011 to 2016. The paper uses Feasible Generalized Least Squares method to capture variance heteroscedasticities and cross-section correlations. The results indicate that Information and Communications Technology has a significant and positive effect on Iranian social welfare. Moreover, variables such as industrialization, government spending, and urbanization have a substantial and positive impact on social welfare. The inflation rate, on the other hand, has a significant and negative effect.

The significant growth of international e-commerce in the recent years illustrates the important role of information and communication technology and the Internet in international trade. Therefore, the aim of this paper is to examine the role of the Internet on Iran’s bilateral trade with its major trading partners. Due to the differing natures of the major economic sectors, the Internet can play a different role in expanding the trade in each sector. In this srudy the role of the Internet on Iran’s trade with its major partners in the three sectors of agriculture, non-agriculture (industry) and services during years 1995-2015. For this purpose, the modified gravity model and panel data were used for experimental modeling and data analysis. In all the three estimated models, only the variables of internet access and exchange rate in the exporting country are significant this indicates a change in the patterns of trade between the countries. The next point is that, among the three models, in the non-agricultural (industrial) commodity model, traditional variables still have more explanatory power to explain the trade flows and, in contrast, in the service model, traditional variables cannot explain the trade flows in any way.

Electricity plays an important role in the production of various economic sectors.  Power outages can thus impose high costs on these sectors and ultimately on economic growth and development.  The purpose of this study is to estimate the welfare losses resulting from a possible power outage in the productive economic sectors of Iran. This study was conducted in two stages using provincial panel data for the period 2010-2019. In the first stage, the electricity demand functions in the sectors of industry and mining, agriculture and services are estimated. In the second stage, changes in the welfare surplus of electricity users (due to possible power outage) are calculated using the estimated demands. The results of the first stage show that  electricity and diesel are complements in the industry and mining sector while electricity and natural gas are substitutes in the service sector. The agricultural sector is not sensitive to changes in electricity prices.  Estimated welfare losses of sectors (industry and mining, agriculture and services) at the macro and provincial levels show that  Kohgoluyeh, Boyer-Ahmad and East Azerbaijan provinces have respectively the highest and lowest welfare losses in the industry and mining sector. In the service sector, North Khorasan and Alborz provinces have respectively the highest and lowest welfare losses from possible power outages.

This paper discusses the impact of sanctions imposed by the US, the UN Security Council and the European Union on the lranian industrial technological changes. In this regard, at first, the sanctions imposed by the above three on six common fields (energy, banking affairs, petrochemicals, insurance, shipping and transportation) are being partly extracted in terms of quantity by the use of Principle Component Analysis approach. Then, by contribution of the data and statistics relevant to the estate industry 2_digit lSlC code and adding the sanctions variable, we estimate the model of this study by the Generalized Method of Moments (GMM). The result demonstrates that based on the existing data, on the one hand, imposing these sanctions has increased the benefit of the whole production of nonmetal mineral industries (26) and official and accounting machineries (30) and, on the other hand, sanctions' influence has a reversed result on technological changes of the production of tobacco and cigarette (16) and vehicles, trailers and mini trailers (34).

We develop an agent-based model to study the effects of the electricity market reform on the electricity prices, the power plants’ technology mix as well as their capacity utilization and profits. The reform’s main objective is to open the electricity market to more competition and increase the efficiency. The new wholesale electricity market will operate based on the one-day-ahead auctions organized by the independent system operator. We set up two scenarios in which the market clearing mechanism changes from the pay-as-bid to market clearing price (MCP) and the fuel subsidy to power plants will be removed. The effects of the two scenarios will be analyzed on the market prices, plant revenues, market shares, and power generating units’ capacities. The simulation results show that the electricity prices are higher during the peak load mainly due to the entry of the higher cost plants during those periods. Electricity prices are lower under the MCP scenario and higher under the fuel subsidy removal scenario, leading to lower and higher revenues for the power plants, respectively. The results also indicate that the market shares and the capacity utilization of the more efficient plants will increase under both scenarios.

the theoretical and empirical literature of behavioral economics shows that people in their decision making have a behavioral and cognitive bias. Therefore, the actual behavior of individuals is not always consistent with economic and rational human behavior. Two of the most important behavioral biases that can play an important role in people's environmental attitudes and behavior are the hot-hand effect and hyperbolic discount rate. In this study, we tried to study the effect of these two biases on the attitude and environmental behavior of individuals in Tehran using the semi-experimental approach and difference in difference regression. In this regard, a sample of 90 subjects was divided into two groups (control and treatment group). The data were collected through a questionnaire in two stages before and after the test. And the difference in difference regression was done. the results showed that: First, the training and awareness of behavioral and cognitive bias improve the environmental attitudes and behavior of individuals. Therefore, policymakers should consider information and education about cognitive bias. Second, demographic variables such as level of education, age, gender, and marriage do not have a significant effect on differences in environmental attitudes and behaviors of individuals.

The recent housing market experience has led many to concern that the developments in the housing sector are not just a passive reflection of macroeconomic activities but instead might be one of the driving forces of them. In this context, it is crucial to understand the nature of the economy by considering the housing market and build a suitable monetary policy. In this paper, using Bayesian methods, we develop and estimate a DSGE model for Iran from 1988q1 to 2017q4, which explicitly models the housing in the banks’ portfolio to study the macroeconomic effects of monetary authority’s reaction to the housing price inflation. Our findings indicate that this reaction amplifies all the implications of the structural shocks.

Energy is one of the main and essential factors in economic growth. Electricity as one of the most important resources of energy supply for economic sectors on one hand, and as an index of welfare, on the other hand, is one of the economic development benchmarks. The growth of GDP will increase the demand for energy resources, including electricity. Hence, policymakers should pay attention to secure electricity supply in their economic planning process. In this study, due to the different structure of each economic sector and the impact of geographical and climate conditions, the electricity demand function in different economic sectors (industry, agriculture, and services) between 31 Iranian provinces during the period of 2010-2014 is estimated in which different panel data method was employed. The results of this study show that the value-added (income) elasticity of electricity demand in all economic sectors is significant, positive and smaller than unity. The elasticities estimated for the industry, agriculture, and service sectors are 0.39, 0.6 and 0.53, respectively. Also, the own price elasticity of electricity demand for industry and service sectors is significant and smaller than unity and is not significant for the agricultural sector. Additionally, the results indicate that the value-added of the Iranian economic sectors has a positive and significant effect on the electricity demand of each sector.

We apply  Almost Ideal Demand System (AIDS) to estimate internet demand by Iranian urban households. using the Seemingly Unrelated Regressions (SUR) method and pooled data of more than 130 thousands of Iranian urban households for the period 2008-2014. In order to increase robustness in estimation, we use provincial price indices and divide goods into 15 groups. The results show that the internet in Iran is a luxury (income elasticity of demand equals to 1.94) and an elastic (price elasticity of demand equals -1.5) service. Moreover, the results show that the internet is substitute with recreation, newspapers and other communication devices and is complement with telephone and education.

Tourism Industry has important role in the job creating and economic development. Because of it, identifying effective factors on foreign tourism demand and attractiveness of tourism destinations is important. We used panel data of incoming tourist and characteristics of tourism destinations in 147 countries between years 2007 to 2015 to examine which factors have significant effect on foreign tourism demand. We used three aggregate indices to identify characteristics of tourism destinations that are regulatory framework, business environment and infrastructure, and human, cultural and natural resources.  Our results show that business environment, tourism infrastructures such as air and ground transportation infrastructure and price competitiveness have significant effects on foreign tourism demand. We have also used thirteen more detailed indices of tourism destination characteristics and show that safety and security (such as the costliness of common crime and violence as well as terrorism, and the extent to which police services can be relied upon for protection from crime) ,  natural and cultural resources (such as cultural heritage and nationally protected areas)  and price competitiveness indices have a significant effect on foreign tourism demand.

Despite extensive studies on the housing market, most of these studies are related to housing demand and not on housing supply. However, identifying the factors affecting supply, especially in the short run and medium term, are key issues in this sector. In this regard, this research has been tried to estimate the price elasticity of the supply of housing according to provincial data for the period of 14 years (2000-2013) by studying the factors affecting the supply of new housing in Iran. For this purpose, using partial equilibrium of housing market, housing supply and demand functions are estimated using the simultaneous equation econometric method and panel data. Given the dynamism of the demand equation, the Generalized Moment Method (GMM) has been used to estimate the parameters. The results show that the price elasticity of the supply of housing in the short run is 0.25 and in the medium term is 0.5. Therefore, due to the lack of supply of housing, shocks on demand side will lead to a sharp increase in prices in this sector.

Nowadays information and communications technology (ICT) is an impartible section in the development countries, Unequal access is a common topic has been accepted in our era. We are looking for ways to increase Internet adoption and narrow its disparity across countries the global "digital divide". Previous work has focused on demographic, economic, and infrastructure determinants of Internet access that are difficult to change in the short run. In the research, content is a factor affective and short-time on internet adoption and digital divide economic has been considered. Our estimates, the language is accepted to adoption content. For this purpose by using simultaneous equation ways among 107 countries and languages, in 2005 to 2014 perioud in panel date shape. The results have been shown, that content has both directly and indirectly effective issue from internet acceptance canal on digital divide

The impact on various sectors of the economy from exchange rate’s changes, considering to the share of the intermediary goods of that section of total imported intermediary goods and the value added sensitivity (elasticity) to imported intermediary goods is very different. Considering the importance of Assessing the quantity and manner of effecting of exchange rate changes, nominal and real, on the production and development of different economic sectors for policy makers, we try to evaluate the effects of nominal and real exchange rate fluctuations on economic sectors (agriculture, industry and services) in this research. We used dynamic system models with data from 1981 to 2010 and found that real exchange rate has no significant effects on agriculture and services sectors, but has significant effects on value added and employment in industry sector.

The Walrasian equilibrium model (including the dynamic stochastic general equilibrium model) has been criticized for over-simplification and lack of empirical power. The most important problem with this approach is to model the relationship between micro and macro structure. The economic system is dynamic, adaptive, evolving and complex. Therefore, another approach is needed to model the behavior of this system. With increase in the processing power of computers, it was possible to use agent-based computational methods to study complex economic phenomena and processes. In this approach, there are no limiting and simplistic assumptions existing in the Walrasian approach. This approach addresses the computational study of dynamic and complex economic systems in which heterogeneous agents have interactions with each other. It is a combination of evolutionary economics, cognitive psychology and computer science. Unlike conventional macroeconomics, agent-based macro models are built from bottom to up. Over the past decade, this approach has played a major role in analyzing macroeconomic and policy issues. It is one of the main methods of behavioral economics modeling. Due to high flexibility, agent-based macroeconomics have many applications in various branches of economics, including modeling of electricity markets, the development of institutions and social norms, financial economics, industrial organization, and labor market. In this paper, features, conceptual foundations, modeling components and the advantages and disadvantages of this approach are discussed

Some researchers consider trade liberalization as a factor for increasing the quality of the environment. However, others argue that trade liberalization leads to some countries specialize in the production of pollution-intensive, energy-intensive or capital-intensive goods and therefore the quality of the environment is reduced. Since theory offers grounds for both positive and negative relationship between trade and the environment, the issue must be settled through empirical analysis. In this context, the main question here is how international trade affects quality of environment in oil exporting countries? For this purpose, in the form of composition, scale and technical effects, we construct a panel data analysis to identify the relationship between international trade and carbon emission of selected oil exporting countries during 1990–2011. Our results suggest that there is positive relationship between trade openness and carbon emission and thus, pollution heaven hypothesis for these countries is not rejected. Also, the relationship between per capita output and carbon emission is N-shape and scale effect is negative. Furthermore, increasing comparative advantage and foreign direct investment leads to a reduction in oil exporting countries’ carbon emissions; which respectively indicate scale and technical effects of the international trade to be negative.

In this paper by considering importance of inflation expectations, its formation quality has investigated by regarding heterogeneous economic agents and by factor based computing model, expected inflation in Iran economy has simulated with lest forecasting error squares during 1979-2013. On the base of the assumptions, individuals have been divided into two groups with extroverted and regressive to trend expectations in which the ratio of these groups can change over time. The results show that the economic agents with extroverted inflationary expectations have an important role in inflation durability and changing the behavioral parameters of economic agents affects inflation expectation. Therefore, because of the possibility of endogenous dynamism mobility in the model, it is better that Policy-makers notice to the condition of inflationary expectations change in making polices.

The average of electricity consumption of Iranian households is higher than the global average. This could be due to price factors (such as low cost of electricity in Iran) and non-price factors (such as socio - demographic and psychological factors). In this study, the role of non-price factors such as socio - demographic and psychological factors in the electricity consumption of urban households evaluated in Tehran. In this regard, using theoretical principles of behavioral economics and theory of planned behavior (TPB( analysis will discuss this topic. The purpose of the mentioned factors is providing non-price policies for more efficient type of household electricity consumption in Iran. The data on household consumption behavior through questionnaires and based on field data collected from 200 households in Tehran. Then, using linear regression is estimated based on econometrics techniques in which the dependent variable is electricity consumption (a 45-day period in Winter 2017) and the independent variables included socio-demographic variables: age, gender, size of family, income and the theory of planned behavior variables include attitude, subjective norms, and perceived behavioral control have been. The results showed that income and the number of household members is a significant and positive effect on electricity consumption, but the gender has no significant effect. Psychological variables perceived behavioral control has a significant effect on electricity consumption. Finally, with using behavioral biases that may cause attitudes and beliefs do not lead to action, the results of the study were analyzed.

Exchange rate is one of the most important factors in open economies. So determining the factors which affect exchange rate behaviors is necessary. In this research we try to analyze the role of extrapolative expectations and chartists in the instability of exchange rate market. We use unofficial nominal exchange rate data (Rial/Dollar) in weekly, monthly and quarterly horizons (from 1991 to 2015). We use fundamentalists- chartists approach and agent-based model (ABM) for simulation. The results show that when there is instability in market, the weight of chartists is much more than fundamentalists and vice versa. Also we show that chartists gain from this market so they don’t like to leave the market.

Housing sector fluctuations have many impacts on economic growth، job creating and households welfare. So، it is very important to determine main reasons of speculative bubbles in housing markets. There is almost general agreement about the role of expectations of buyers and sellers in making of speculative bubbles but، only a few researches try to model it. In this research we try to model speculative bubble in Tehran housing market using agent- based economics. Agents have heterogeneous expectations about long-run fundamentals but change their views because of «social dynamics». Agents meet randomly and those with tighter priors are more likely to convert other agents to their beliefs. Results of modeling show the role of non-fundamentals and heterogeneous expectations in speculative bubbles in the Tehran housing market

In this article, we test Fractal Market Hypothesis (FMH) using the daily returns in Tehran Stock Exchange. We estimated Hurst exponent using three approaches such as classic R/S analysis, modified R/S analysis and ARFIMA model. Without removing short term dependencies in returns, we rejected null hypothesis (Efficient Market Hypothesis), but after removing the short-term dependencies using ARMA model, we found that there is no long-term dependencies, fractal structure and persistence in returns. The R/S analysis overestimates the Hurst exponent; so, for determining critical values of testing statistics we used Monte Carlo simulation.

Importance of risk and uncertainty in financial markets became more apparent after financial crisis in 2007. Volatility is the most important measure of risk in financial markets. Thus, modeling volatility of financial markets is one of the important issues in finance and economics. In this paper first we tried to specify key features of volatility of daily returns of Tehran stock exchange price index (TEPIX) and then using these features, we modeled volatility of this index. We found that this index is fat-tailed distributed and mean- reverting. Furthermore, we showed that there is no leverage effect, so using of TARCH and EGARCH models are not convenient. Finally, we found that AR(2)-GARCH(1,1) Model can capture features of data in the best way.

The very complex movements in the stock prices are usually taken as random or stochastic, but they may be produced by a deterministic data generating process. Chaos refers to the nonlinear dynamic deterministic process that generates a series, which appears like random, but has a long memory. In the Economics and Finance literature, stock prices are known to be random due to their complexities, and therefore being unpredictable. In this paper, we test for chaos in the stock prices using the data from the daily and weekly stock prices listed in the Tehran Exchange Market (TEPIX) in 1377-1382 (1998-2003). We apply three tests for chaos, namely; BDS, Lyaponov Exponent, and Neural Networks; to the residuals of linear (ARIMA) and nonlinear (GARCH) models. The BDS and the Neural Networks tests results show that there exists nonlinearity in the ARIMA residuals, but not in the GARCH residuals. However, the Lyaponove exponent test result is positive for all different dimensions indicating that the TEPIX is chaotic.