Due to global warming, the world is seeking to use more renewable energy. In this study, we focus on solar energy, which has been receiving increased amounts of attention in the last few decades. The integration of solar energy into electricity networks requires reliable forecast information of solar resources enabling it to quantify the available energy and allowing it to optimally manage the transition between intermittent and conventional energies. Throughout our research, we investigated different forecasting techniques in order to find which one is appropriate for forecasting the daily global solar irradiance for the region of Rabat. The first-tested approach is linear modeling based on classical ARIMA-GARCH and exponential smoothing models. The second approach proposes non-linear modeling based on Artificial Neural Networks (ANNs) models. Numerous research has demonstrated the ability of ANNs to predict time series of weather data. In this study, we will examine a particular structure of ANNs, Multilayer Perceptron (MLP), which has been used the most among ANN structures in renewable energy and time series forecasting broadly. We used some statistical feature parameters to find the optimal structure of MLP in the univariate case and the multivariate case. The results showed that the MLP with exogenous variables performed better than the other models.
{"title":"Artificial Neural Network for Forecasting One Day Ahead of Global Solar Irradiance","authors":"Hamid Ettayyebi, Khalid El Himdi","doi":"10.2139/ssrn.3187061","DOIUrl":"https://doi.org/10.2139/ssrn.3187061","url":null,"abstract":"Due to global warming, the world is seeking to use more renewable energy. In this study, we focus on solar energy, which has been receiving increased amounts of attention in the last few decades. The integration of solar energy into electricity networks requires reliable forecast information of solar resources enabling it to quantify the available energy and allowing it to optimally manage the transition between intermittent and conventional energies. Throughout our research, we investigated different forecasting techniques in order to find which one is appropriate for forecasting the daily global solar irradiance for the region of Rabat. The first-tested approach is linear modeling based on classical ARIMA-GARCH and exponential smoothing models. The second approach proposes non-linear modeling based on Artificial Neural Networks (ANNs) models. Numerous research has demonstrated the ability of ANNs to predict time series of weather data. In this study, we will examine a particular structure of ANNs, Multilayer Perceptron (MLP), which has been used the most among ANN structures in renewable energy and time series forecasting broadly. We used some statistical feature parameters to find the optimal structure of MLP in the univariate case and the multivariate case. The results showed that the MLP with exogenous variables performed better than the other models.","PeriodicalId":105811,"journal":{"name":"Econometric Modeling: Agriculture","volume":"31 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128564117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study monetary policy under climate change in order to answer the question of whether monetary policy should take into account the expected impacts of climate change. The setup is a new Keynesian dynamic stochastic general equilibrium model of a closed economy in which a climate module that interacts with the economy has been incorporated, and the monetary authorities follow a Taylor rule for the nominal interest rate. The model is solved numerically using common parameter values and fiscal data from the euro area. Our results, which are robust to a large number of sensitivity checks, suggest non-trivial implications for the conduct of monetary policy.
{"title":"Monetary Policy Under Climate Change","authors":"G. Economides, A. Xepapadeas","doi":"10.2139/ssrn.3200266","DOIUrl":"https://doi.org/10.2139/ssrn.3200266","url":null,"abstract":"We study monetary policy under climate change in order to answer the question of whether monetary policy should take into account the expected impacts of climate change. The setup is a new Keynesian dynamic stochastic general equilibrium model of a closed economy in which a climate module that interacts with the economy has been incorporated, and the monetary authorities follow a Taylor rule for the nominal interest rate. The model is solved numerically using common parameter values and fiscal data from the euro area. Our results, which are robust to a large number of sensitivity checks, suggest non-trivial implications for the conduct of monetary policy.","PeriodicalId":105811,"journal":{"name":"Econometric Modeling: Agriculture","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114202456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Applying detailed consecutive daily micro data at the gasoline station level from Sweden we estimate a structural model to uncover the degree of competition in the gasoline retail market. We find that retailers do exercise market power, but despite the high upstream concentration, the market power is very limited on the downstream level. The degree of market power varies with both the distance to the nearest station and the local density of gasoline stations. A higher level of service tends to raise a seller’s market power; self-service stations have close to no market power. Contractual form and brand identity also seem to matter. We find a clear result: local station characteristics significantly affect the degree of market power. Our results indicate that local differences in station characteristics can more than offset the average market Power found for the whole market.
{"title":"Measuring Market Power in Gasoline Retailing: A Market - or Station Phenomenon?","authors":"M. Nguyen, Frode Steen","doi":"10.2139/ssrn.3164299","DOIUrl":"https://doi.org/10.2139/ssrn.3164299","url":null,"abstract":"Applying detailed consecutive daily micro data at the gasoline station level from Sweden we estimate a structural model to uncover the degree of competition in the gasoline retail market. We find that retailers do exercise market power, but despite the high upstream concentration, the market power is very limited on the downstream level. The degree of market power varies with both the distance to the nearest station and the local density of gasoline stations. A higher level of service tends to raise a seller’s market power; self-service stations have close to no market power. Contractual form and brand identity also seem to matter. We find a clear result: local station characteristics significantly affect the degree of market power. Our results indicate that local differences in station characteristics can more than offset the average market Power found for the whole market.","PeriodicalId":105811,"journal":{"name":"Econometric Modeling: Agriculture","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134395780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dukpa Kim, Tatsushi Oka, F. Estrada, Pierre Perron
What transpires from recent research is that temperatures and radiative forcing seem to be characterized by a linear trend with two changes in the rate of growth. The first occurs in the early 60s and indicates a very large increase in the rate of growth of both temperature and radiative forcing series. This was termed as the “onset of sustained global warming”. The second is related to the more recent so-called hiatus period, which suggests that temperatures and total radiative forcing have increased less rapidly since the mid-90s compared to the larger rate of increase from 1960 to 1990. There are two issues that remain unresolved. The first is whether the breaks in the slope of the trend functions of temperatures and radiative forcing are common. This is important because common breaks coupled with the basic science of climate change would strongly suggest a causal effect from anthropogenic factors to temperatures. The second issue relates to establishing formally via a proper testing procedure that takes into account the noise in the series, whether there was indeed a ‘hiatus period’ for temperatures since the mid 90s. This is important because such a test would counter the widely held view that the hiatus is the product of natural internal variability. Our paper provides tests related to both issues. The results show that the breaks in temperatures and radiative forcing are common and that the hiatus is characterized by a significant decrease in their rate of growth. The statistical results are of independent interest and applicable more generally.
{"title":"Inference Related to Common Breaks in a Multivariate System With Joined Segmented Trends With Applications to Global and Hemispheric Temperatures","authors":"Dukpa Kim, Tatsushi Oka, F. Estrada, Pierre Perron","doi":"10.2139/ssrn.3184646","DOIUrl":"https://doi.org/10.2139/ssrn.3184646","url":null,"abstract":"What transpires from recent research is that temperatures and radiative forcing seem to be characterized by a linear trend with two changes in the rate of growth. The first occurs in the early 60s and indicates a very large increase in the rate of growth of both temperature and radiative forcing series. This was termed as the “onset of sustained global warming”. The second is related to the more recent so-called hiatus period, which suggests that temperatures and total radiative forcing have increased less rapidly since the mid-90s compared to the larger rate of increase from 1960 to 1990. There are two issues that remain unresolved. The first is whether the breaks in the slope of the trend functions of temperatures and radiative forcing are common. This is important because common breaks coupled with the basic science of climate change would strongly suggest a causal effect from anthropogenic factors to temperatures. The second issue relates to establishing formally via a proper testing procedure that takes into account the noise in the series, whether there was indeed a ‘hiatus period’ for temperatures since the mid 90s. This is important because such a test would counter the widely held view that the hiatus is the product of natural internal variability. Our paper provides tests related to both issues. The results show that the breaks in temperatures and radiative forcing are common and that the hiatus is characterized by a significant decrease in their rate of growth. The statistical results are of independent interest and applicable more generally.","PeriodicalId":105811,"journal":{"name":"Econometric Modeling: Agriculture","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129520842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Over the past three decades, the US natural gas market has witnessed significant changes. Utilizing a standard Bayesian model comparison method, this paper formally determines four regimes existing in the market. It then employs a Markov switching vector autoregressive model to investigate the regime-dependent responses of the market to its fundamental shocks. The results reveal that the US natural gas market tends to be much more sensitive to shocks occurring in regimes existing after the Decontrol Act 1989 than the other regimes. The paper also finds that shocks to the natural gas demand and price have negligible effects on natural gas production while the price of natural gas is mainly driven by specific demand shocks. Augmenting the model by incorporating the price of crude oil, the results show that the impacts of oil price shocks on natural gas prices are relatively small and regime-dependent.
{"title":"Understanding the US Natural Gas Market: A Markov Switching VAR Approach","authors":"Chenghan Hou, B. Nguyen","doi":"10.2139/ssrn.3156000","DOIUrl":"https://doi.org/10.2139/ssrn.3156000","url":null,"abstract":"Over the past three decades, the US natural gas market has witnessed significant changes. Utilizing a standard Bayesian model comparison method, this paper formally determines four regimes existing in the market. It then employs a Markov switching vector autoregressive model to investigate the regime-dependent responses of the market to its fundamental shocks. The results reveal that the US natural gas market tends to be much more sensitive to shocks occurring in regimes existing after the Decontrol Act 1989 than the other regimes. The paper also finds that shocks to the natural gas demand and price have negligible effects on natural gas production while the price of natural gas is mainly driven by specific demand shocks. Augmenting the model by incorporating the price of crude oil, the results show that the impacts of oil price shocks on natural gas prices are relatively small and regime-dependent.","PeriodicalId":105811,"journal":{"name":"Econometric Modeling: Agriculture","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124780142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines whether investment in the agriculture and food sectors in Africa significantly increases overall economic growth and, hence, reduces food and nutrition insecurity. To this end, the study examines the causal link between agricultural growth, food production, quality of governance, and overall economic growth using panel data compiled from 44 African countries for a 53-year period from 1961 to 2014. The estimation result from the fully modified least squares, the panel cointegration, and Granger causality tests suggest that agricultural growth, government commitment, and quality of governance Granger causes overall economic growth. The study also identifies the 10 African countries where investment in the agriculture and food sectors is expected to yield the highest returns and the 10 African countries having the lowest returns in terms of reducing food insecurity and poverty. The result indicates that Botswana, Burkina Faso, Ethiopia, Kenya, Malawi, Mali, Mozambique, Rwanda, Seychelles, and Sierra Leone are the top 10 African countries where such an investment is expected to yield the highest returns. Cameroon, Congo, Egypt, Equatorial Guinea, Eritrea, Gabon, Gambia, Libya, Mauritania, and Somalia are the bottom 10 countries where such investment is expected to yield the lowest return.
{"title":"From Agricultural to Economic Growth: Targeting Investments Across Africa","authors":"T. Getahun, H. Baumüller, Yalemzewd Nigussie","doi":"10.2139/ssrn.3160555","DOIUrl":"https://doi.org/10.2139/ssrn.3160555","url":null,"abstract":"This paper examines whether investment in the agriculture and food sectors in Africa significantly increases overall economic growth and, hence, reduces food and nutrition insecurity. To this end, the study examines the causal link between agricultural growth, food production, quality of governance, and overall economic growth using panel data compiled from 44 African countries for a 53-year period from 1961 to 2014. The estimation result from the fully modified least squares, the panel cointegration, and Granger causality tests suggest that agricultural growth, government commitment, and quality of governance Granger causes overall economic growth. The study also identifies the 10 African countries where investment in the agriculture and food sectors is expected to yield the highest returns and the 10 African countries having the lowest returns in terms of reducing food insecurity and poverty. The result indicates that Botswana, Burkina Faso, Ethiopia, Kenya, Malawi, Mali, Mozambique, Rwanda, Seychelles, and Sierra Leone are the top 10 African countries where such an investment is expected to yield the highest returns. Cameroon, Congo, Egypt, Equatorial Guinea, Eritrea, Gabon, Gambia, Libya, Mauritania, and Somalia are the bottom 10 countries where such investment is expected to yield the lowest return.","PeriodicalId":105811,"journal":{"name":"Econometric Modeling: Agriculture","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133198867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A large literature exists on the impact of disamenities, such as landfills and airports, on home prices. Less frequently analyzed is the effect of rock quarries on property values, and what little evidence is available is dated and conflicting. This question of price effects is a policy relevant one, with one study in particular used frequently to support “not in my backyard” campaigns against new quarry sites. In this policy paper, we revisit the literature and conduct a new analysis of the price effects of quarries, estimating the effect of quarries on home prices with data from four locations across the United States and a wide range of econometric specifications and robustness checks along with a variety of temporal circumstances from the lead-up to quarry installation to subsequent operational periods. We find no compelling statistical evidence that either the anticipation of, or the ongoing operation of, rock quarries negatively impact home prices. Our study likewise highlights a number of shortcomings in the empirical methodologies generally used to estimate the effect of disamenities on real estate prices. First and foremost, many existing studies are naïve as to the empirical conditions necessary to identify a causal relationship and do not establish credible strategies to estimate the counter-factual outcome. Second, the inclusion of “distance to the site” regressors in hedonic models is shown to be an unreliable statistical method. Using the method of randomized inference, the null hypothesis of “no effect” of placebo quarries is rejected in as much as 93% of simulations.
{"title":"Quarry Operations and Property Values: Revisiting Old and Investigating New Empirical Evidence","authors":"George S. Ford, Alan Seals","doi":"10.2139/ssrn.3138712","DOIUrl":"https://doi.org/10.2139/ssrn.3138712","url":null,"abstract":"A large literature exists on the impact of disamenities, such as landfills and airports, on home prices. Less frequently analyzed is the effect of rock quarries on property values, and what little evidence is available is dated and conflicting. This question of price effects is a policy relevant one, with one study in particular used frequently to support “not in my backyard” campaigns against new quarry sites. In this policy paper, we revisit the literature and conduct a new analysis of the price effects of quarries, estimating the effect of quarries on home prices with data from four locations across the United States and a wide range of econometric specifications and robustness checks along with a variety of temporal circumstances from the lead-up to quarry installation to subsequent operational periods. We find no compelling statistical evidence that either the anticipation of, or the ongoing operation of, rock quarries negatively impact home prices. Our study likewise highlights a number of shortcomings in the empirical methodologies generally used to estimate the effect of disamenities on real estate prices. First and foremost, many existing studies are naïve as to the empirical conditions necessary to identify a causal relationship and do not establish credible strategies to estimate the counter-factual outcome. Second, the inclusion of “distance to the site” regressors in hedonic models is shown to be an unreliable statistical method. Using the method of randomized inference, the null hypothesis of “no effect” of placebo quarries is rejected in as much as 93% of simulations.","PeriodicalId":105811,"journal":{"name":"Econometric Modeling: Agriculture","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125245086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this study is to identify critical consumer-demographic characteristics that are associated with the consumption of hemp products and investigate their effect on total expenditure in the U.S. by utilizing Nielsen consumer panel data from 2008 to 2015. To estimate the likelihood of market participation and consumption level, Heckman selection model is employed using the maximum likelihood estimation procedure. Results indicate marketing strategies targeting consumers with higher education and income levels can attract new customers and increase sales from current consumers. Head-of-household age in different regions shows mixed effects on decisions to purchase hemp products and consumption levels. Findings will provide a basic understanding of a consumer profile and overall hemp market since no empirical studies of hemp in the U.S market exist. As the industry continues to move forward, policymakers are going to need a deeper understanding of the factors driving the industry if they are going to create regulations that support the development of the industry.
{"title":"Who Are Consuming Hemp Products in the U.S.? Evidence from Nielsen Homescan Data","authors":"GwanSeon Kim, T. Mark","doi":"10.2139/ssrn.3176016","DOIUrl":"https://doi.org/10.2139/ssrn.3176016","url":null,"abstract":"The objective of this study is to identify critical consumer-demographic characteristics that are associated with the consumption of hemp products and investigate their effect on total expenditure in the U.S. by utilizing Nielsen consumer panel data from 2008 to 2015. To estimate the likelihood of market participation and consumption level, Heckman selection model is employed using the maximum likelihood estimation procedure. Results indicate marketing strategies targeting consumers with higher education and income levels can attract new customers and increase sales from current consumers. Head-of-household age in different regions shows mixed effects on decisions to purchase hemp products and consumption levels. Findings will provide a basic understanding of a consumer profile and overall hemp market since no empirical studies of hemp in the U.S market exist. As the industry continues to move forward, policymakers are going to need a deeper understanding of the factors driving the industry if they are going to create regulations that support the development of the industry.","PeriodicalId":105811,"journal":{"name":"Econometric Modeling: Agriculture","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127518233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paulina A. Rowińska, Almut E. D. Veraart, P. Gruet
We introduce a three-factor model of electricity spot prices, consisting of a deterministic seasonality and trend function as well as short- and long-term stochastic components, and derive a formula for futures prices. The long-term component is modelled as a Levy process with increments belonging to the class of generalised hyperbolic distributions. We describe the short-term factor by Levy semistationary processes: we start from a CARMA(2,1), i.e. a continous-time ARMA model, and generalise it by adding a short-memory stochastic volatility. We further modify the model by including the information about the wind energy production as an exogenous variable. We fit our models to German and Austrian data including spot and futures prices as well as the wind energy production and total load data. Empirical studies reveal that taking into account the impact of the wind energy generation on the prices improves the goodness of fit.
{"title":"A Multifactor Approach to Modelling the Impact of Wind Energy on Electricity Spot Prices","authors":"Paulina A. Rowińska, Almut E. D. Veraart, P. Gruet","doi":"10.2139/ssrn.3110554","DOIUrl":"https://doi.org/10.2139/ssrn.3110554","url":null,"abstract":"We introduce a three-factor model of electricity spot prices, consisting of a deterministic seasonality and trend function as well as short- and long-term stochastic components, and derive a formula for futures prices. The long-term component is modelled as a Levy process with increments belonging to the class of generalised hyperbolic distributions. We describe the short-term factor by Levy semistationary processes: we start from a CARMA(2,1), i.e. a continous-time ARMA model, and generalise it by adding a short-memory stochastic volatility. We further modify the model by including the information about the wind energy production as an exogenous variable. We fit our models to German and Austrian data including spot and futures prices as well as the wind energy production and total load data. Empirical studies reveal that taking into account the impact of the wind energy generation on the prices improves the goodness of fit.","PeriodicalId":105811,"journal":{"name":"Econometric Modeling: Agriculture","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127897822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Bandyopadhyay, S. Dasgupta, Z. Khan, D. Wheeler
Recurrent cyclonic storms in the Bay of Bengal inflict massive losses on the coastal regions of Bangladesh and India. Information on occurrences and severities of cyclones is necessary for understanding household and community responses to cyclone risks. This paper constructs a georeferenced panel database that can be used to obtain such information for Bangladesh, West Bengal, and Odisha. Cyclone strike locations and impact zones are analyzed for several historical periods between 1877 and 2016. The findings indicate that although the median location has shifted eastward, there is a marked variability in location, especially after 1960. Impacts also have varied considerably within and across zones over time, with the highest-impact zones in northern Odisha and the Sundarbans region of West Bengal. The pronounced spatial and temporal variation in cyclone impacts will provide robust controls for comparative research on household and community adaptation to cyclones in the study region. The methodology developed in the paper is general and could be expanded to an arbitrarily large set of coastal locations.
{"title":"Cyclonic Storm Landfalls in Bangladesh, West Bengal and Odisha, 1877-2016: A Spatiotemporal Analysis","authors":"S. Bandyopadhyay, S. Dasgupta, Z. Khan, D. Wheeler","doi":"10.1596/1813-9450-8316","DOIUrl":"https://doi.org/10.1596/1813-9450-8316","url":null,"abstract":"Recurrent cyclonic storms in the Bay of Bengal inflict massive losses on the coastal regions of Bangladesh and India. Information on occurrences and severities of cyclones is necessary for understanding household and community responses to cyclone risks. This paper constructs a georeferenced panel database that can be used to obtain such information for Bangladesh, West Bengal, and Odisha. Cyclone strike locations and impact zones are analyzed for several historical periods between 1877 and 2016. The findings indicate that although the median location has shifted eastward, there is a marked variability in location, especially after 1960. Impacts also have varied considerably within and across zones over time, with the highest-impact zones in northern Odisha and the Sundarbans region of West Bengal. The pronounced spatial and temporal variation in cyclone impacts will provide robust controls for comparative research on household and community adaptation to cyclones in the study region. The methodology developed in the paper is general and could be expanded to an arbitrarily large set of coastal locations.","PeriodicalId":105811,"journal":{"name":"Econometric Modeling: Agriculture","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132169930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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