scholarly journals Cross-taxa generalities in the relationship between population abundance and ambient temperatures

2017 ◽  
Vol 284 (1863) ◽  
pp. 20170870 ◽  
Author(s):  
Diana E. Bowler ◽  
Peter Haase ◽  
Christian Hof ◽  
Ingrid Kröncke ◽  
Léon Baert ◽  
...  
Keyword(s):  
Climate Change ◽  
Central Europe ◽  
Time Series Data ◽  
Interspecific Variation ◽  
Series Data ◽  
Dispersal Ability ◽  
Aquatic Communities ◽  
Positive Effects ◽  
Effects Of Temperature ◽  
The Relationship

Identifying patterns in the effects of temperature on species' population abundances could help develop a general framework for predicting the consequences of climate change across different communities and realms. We used long-term population time series data from terrestrial, freshwater, and marine species communities within central Europe to compare the effects of temperature on abundance across a broad range of taxonomic groups. We asked whether there was an average relationship between temperatures in different seasons and annual abundances of species in a community, and whether species attributes (temperature range of distribution, range size, habitat breadth, dispersal ability, body size, and lifespan) explained interspecific variation in the relationship between temperature and abundance. We found that, on average, warmer winter temperatures were associated with greater abundances in terrestrial communities (ground beetles, spiders, and birds) but not always in aquatic communities (freshwater and marine invertebrates and fish). The abundances of species with large geographical ranges, larger body sizes, and longer lifespans tended to be less related to temperature. Our results suggest that climate change may have, in general, positive effects on species’ abundances within many terrestrial communities in central Europe while the effects are less predictable in aquatic communities.

scholarly journals Examining the Effects of Climate Change and Political Instability on Maize Production in Somalia

2021 ◽  
Author(s):  
Abdimalik Ali Warsame ◽  
Ibrahim Abdukadir Sheik-Ali ◽  
Galad Mohamed Barre ◽  
Abdulnasir Ahmed
Keyword(s):  
Climate Change ◽  
Time Series Data ◽  
Political Instability ◽  
Least Square ◽  
Series Data ◽  
Favourable Effect ◽  
Maize Production ◽  
Positive Effects ◽  
Long Run ◽  
The Impact

Abstract Agricultural production is sensirive to climate variabilities, so climate change-agriculture sector nexus is topical in developing countries. Given that, this study models the impact of climate change variables – rainfall and temperature – on maize production in Somalia. The study also incorporates political instability as a main determinant variable of maize production. Several econometric methodologies – Johansen and juselius cointegration, Fully modified ordinary least square(FMOLS), Canonical cointegration regression(CCR), variance decomposition(VD) and impulse response function(IRF) – were espoused with a time series data spanning between 1980 and 2018. The empirical results of Johansen and juselius cointegration show that the explanatory variables are cointegrated to the maize output in the long-run. The study also revealed that average temperature and political instability significantly inhibit maize production in the long-run. Whereas rainfall has a favourable effect on maize production in the long-run. Furthermore, land degradation and land area under maize cultivation – incorporated in the model as control variables – are observed to have positive effects on maize production in the long-run. Based on these findings, the study emphesizes the importane of the de-escalation of conflicts and the implementation of irrigation facilities which will enhance the prdocutiivity of maize crop production.

Testing Structural Break in the Relationship Between Exchange Rate and Macroeconomic Variables

2019 ◽  
Vol 23 (4) ◽  
pp. 442-453 ◽  
Author(s):  
Saidia Jeelani ◽  
Joity Tomar ◽  
Tapas Das ◽  
Seshanwita Das
Keyword(s):  
Exchange Rate ◽  
Structural Breaks ◽  
Time Series Data ◽  
Structural Break ◽  
Series Data ◽  
Ripple Effect ◽  
Macroeconomic Factors ◽  
Parameter Instability ◽  
Macroeconomic Variables ◽  
The Relationship

The article aims to study the relationship between those macroeconomic factors that the affect (INR/USD) exchange rate (ER). Time series data of 40 years on ER, GDP, inflation, interest rate (IR), FDI, money supply, trade balance (TB) and terms of trade (ToT) have been collected from the RBI website. The considered model has suggested that only inflation, TB and ToT have influenced the ER significantly during the study period. Other macroeconomic variables such as GDP, FDI and IR have not significantly influenced the ER during the study period. The model is robust and does not suffer from residual heteroscedasticity, autocorrelation and non-normality. Sometimes the relationship between ER and macroeconomic variables gets affected by major economic events. For example, the Southeast Asian crisis caused by currency depreciation in 1997 and sub-prime loan crisis of 2008 severely strained the national economies. Any global economic turmoil will affect different economic variables through ripple effect and this, in turn, will affect the ER of different economies differently. The article has also diagnosed whether there is any structural break or not in the model by applying Chow’s Breakpoint Test and have obtained multiple breaks between 2003 and 2009. The existence of structural breaks during 2003–2009 is explained by the fact that volume of crude oil imported by India is high and oil price rise led to a deficit in the TB alarmingly, which caused a structural break or parameter instability.

scholarly journals Non-bank financial institutions and economic growth: Evidence from Africa’s three largest economies

2017 ◽  
Vol 20 (1) ◽  
Author(s):  
Ronald Rateiwa ◽  
Meshach J. Aziakpono
Keyword(s):  
Economic Growth ◽  
South Africa ◽  
Financial Institutions ◽  
Time Series Data ◽  
Bank Lending ◽  
Series Data ◽  
The Sustainable Development ◽  
Long Run ◽  
The Relationship

Background: In order for the post-2015 world development agenda – termed the sustainable development goals (SDGs) – to succeed, there is a pronounced need to ensure that available resources are used more effectively and additional financing is accessed from the private sector. Given that traditional bank lending has slowed down, the development of non-bank financing has become imperative. To this end, this article intends to empirically test the role of non-bank financial institutions (NBFIs) in stimulating economic growth.Aim: The aim of this article is to empirically test the existence of a long-run equilibrium relationship between economic growth and the development of NBFIs, and the causality thereof.Setting: The empirical assessment uses time-series data from Africa’s three largest economies, namely, Egypt, Nigeria and South Africa, over the period 1971–2013.Methods: This article uses the Johansen cointegration and vector error correction model within a country-specific setting.Results: The results showed that the long-run relationship between NBFI development and economic growth is relatively stronger in Egypt and South Africa, than in Nigeria. Evidence in respect of Nigeria shows that such a relationship is weak. The nature of the relationship between NBFI development and economic growth in Egypt is positive and significant, and predominantly bidirectional. This suggests that a virtuous relationship between NBFIs and economic growth exists in Egypt. In South Africa, the relationship is positive and significant and predominantly runs from NBFI development to economic growth, implying a supply-leading phenomenon. In Nigeria, the results are weak and mixed.Conclusion: The study concludes that in countries with more developed financial systems, the role of NBFIs and their importance to the economic growth process are more pronounced. Thus, there is need for developing policies targeted at developing the NBFI sector, given their potential to contribute to economic growth.

scholarly journals Retrospectives: The Introduction of the Cobb–Douglas Regression

2012 ◽  
Vol 26 (2) ◽  
pp. 223-236 ◽  
Author(s):  
Jeff Biddle
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Mathematical Representation ◽  
Least Squares Regression ◽  
Empirical Measures ◽  
Stable Relationship ◽  
Initial Application ◽  
Economic Association ◽  
The Relationship ◽  
Theory Of Production

At the 1927 meetings of the American Economic Association, Paul Douglas presented a paper entitled “A Theory of Production,” which he had coauthored with Charles Cobb. The paper proposed the now familiar Cobb–Douglas function as a mathematical representation of the relationship between capital, labor, and output. The paper's innovation, however, was not the function itself, which had originally been proposed by Knut Wicksell, but the use of the function as the basis of a statistical procedure for estimating the relationship between inputs and output. The paper's least squares regression of the log of the output-to-capital ratio in manufacturing on the log of the labor-to-capital ratio—the first Cobb–Douglas regression—was a realization of Douglas's innovative vision that a stable relationship between empirical measures of inputs and outputs could be discovered through statistical analysis, and that this stable relationship could cast light on important questions of economic theory and policy. This essay provides an account of the introduction of the Cobb–Douglas regression: its roots in Douglas's own work and in trends in economics in the 1920s, its initial application to time series data in the 1927 paper and Douglas's 1934 book The Theory of Wages, and the early reactions of economists to this new empirical tool.

scholarly journals PENGARUH KORUPSI, DEMOKRASI DAN POLITIK TERHADAP KEMISKINAN DI DELAPAN NEGARA ASEAN DENGAN PERTUMBUHAN EKONOMI SEBAGAI VARIABEL MODERASI

2019 ◽  
Vol 1 (3) ◽  
pp. 845
Author(s):  
Yolanda Yolanda
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Predictor Variable ◽  
Combination Method ◽  
Annual Reports ◽  
Series Data ◽  
Transparency International ◽  
Negative Effect ◽  
Asean Countries ◽  
The Relationship

This study aims the influence of corruption, democracy and politics on poverty in ASEAN countries with economic growth as a moderating variable. The method used is using the panel regression model. This data uses a combination method between time series data from 2013 - 2016 and a cross section consisting of 8 countries. Data obtained from World Bank annual reports, Transparency International and Freedom House. The results of this study indicate that (1) Corruption Perception Index (CPI) has a significant and negative effect on poverty, meaning that if the CPI increases then poverty will decrease (2) Democracy has no significant and negative effect on poverty. This means that if democracy increases, poverty will decrease (3) Politics has a significant and negative effect on poverty, meaning that if politics increases, poverty will decrease (4) Economic growth has a significant and positive effect on poverty, meaning if economic growth increases then poverty will decline (3) Economic growth unable to moderate the relationship between corruption, democracy and politics towards poverty in 8 ASEAN countries. Economic growth as an interaction variable is a predictor variable (Predictor Moderate Variable), which means that economic growth is only an independent variable.

scholarly journals PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA CURAH HUJAN EMPAT KABUPATEN DI PROVINSI JAWA TENGAH

2019 ◽  
Vol 8 (4) ◽  
pp. 418-427
Author(s):  
Eko Siswanto ◽  
Hasbi Yasin ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Space Time ◽  
Series Data ◽  
Inverse Distance Weighted ◽  
Modeling And Forecasting ◽  
Distance Weighted ◽  
The Relationship ◽  
Inverse Distance

In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling volume of rainfall four locations in Jepara Regency, Kudus Regency, Pati Regency, and Grobogan Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR (11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on rainfall Pati Regency influenced by the rainfall in other regencies. Keywords: GSTAR, RMSE, Rainfall

scholarly journals Capital structure and profitability: An empirical study of South African banks

2016 ◽  
Vol 14 (1) ◽  
pp. 8-19 ◽  
Author(s):  
Kudzai Raymond Marandu ◽  
Athenia Bongani Sibindi
Keyword(s):  
Capital Structure ◽  
Credit Risk ◽  
South African ◽  
Time Series Data ◽  
Emerging Market ◽  
Strong Relationship ◽  
Capital Adequacy ◽  
Series Data ◽  
Bank Capital ◽  
The Relationship

The bank capital structure debacle in the aftermath of the 2007-2009 financial crises continues to preoccupy the minds of regulators and scholars alike. In this paper we investigate the relationship between capital structure and profitability within the context of an emerging market of South Africa. We conduct multiple linear regressions on time series data of big South African banks for the period 2002 to 2013. We establish a strong relationship between the ROA (profitability measure) and the bank specific determinants of capital structure, namely capital adequacy, size, deposits and credit risk. The relationship exhibits sensitivity to macro-economic shocks (such as recessions), in the case of credit risk and capital but is persistent for the other determinants of capital structure.

scholarly journals COINTEGRATING RELATION BETWEEN EXCHANGE RATE AND GOLD PRICE

2017 ◽  
Vol 5 (10) ◽  
pp. 263-269
Author(s):  
Ranjusha ◽  
Devasia ◽  
Nandakumar
Keyword(s):  
Exchange Rate ◽  
Time Series Data ◽  
Unit Root Test ◽  
Gold Price ◽  
Series Data ◽  
Annual Data ◽  
Causality Test ◽  
Long Run ◽  
Integration Test ◽  
The Relationship

The very purpose of this paper is to analyse the relationship between gold price and Rupee – Dollar exchange rate in India. The study utilises the annual data of exchange Rate (ER) and Gold Price (GP) from 1970 to 2015 to determine the relationship. Different econometric tools like Unit root test, Johansen co integration test, Vector error correction model, Granger causality test are used for detecting the long run relation, if any between the mentioned variables. The result shows that there exists a long run cointegrating relation between the variables. That is we can stabilise the Gold Price movement by controlling the exchange rate fluctuations. Likewise it also shows that Exchange rate doesn’t Granger cause to Gold price and vice versa. It means that the time series data of one vasriable cannot be used to predict another.

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