1,720,970 research outputs found
Modes of climate variability and their relationships with interhemispheric temperature asymmetry: a Granger causality analysis
Full text linkThe aim of this paper is to investigate the relationships among Interhemispheric Temperature Asymmetry (ITA) and the principal modes of natural variability: the Atlantic Multidecadal Oscillation (AMO), the Southern Oscillation Index (SOI), and the Pacific Decadal Oscillation (PDO). In particular, Granger causality tests are used to capture the linkages among these variables. Our analysis provides strong evidence that AMO causes ITA, the causal role of PDO is weak, and SOI seems to have no causal influenc
Forecasting the number of confirmed new cases of COVID-19 in Italy for the period from 19 May to 2 June 2020
Full text linkIn this paper we forecast the spread of the coronavirus disease 2019 outbreak in Italy in the time window from May 19 to June 2, 2020. In particular, we consider the forecast of the number of new daily confirmed cases. A forecast procedure combining a log-polynomial model together with a first-order integer-valued autoregressive model is proposed. An out-of-sample comparison with forecasts from an autoregressive integrated moving average (ARIMA) model is considered. This comparison indicates that our procedure outperforms the ARIMA model. The Root Mean Square Error (RMSE) of the ARIMA is always greater than that of the our procedure and generally more than twice as high as the our procedure RMSE. We have also conducted Diebold and Mariano (1995) tests of equal mean square error (MSE). The tests results confirm that forecasts from our procedure are significantly more accurate at all horizons. We think that the advantage of our approach comes from the fact that it explicitly takes into account the number of swabs
Testing for Granger non-causality using the autoregressive metric
No full textA new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in finite samples. The empirical relevance of the test is illustrated via an application. © 2013 Elsevier B.V
A comparison between VAR processes jointly modeling GDP and Unemployment rate in France and Germany
Full text linkInvestigating the relationship between Gross Domestic Product and unemployment is one of the most important challenges in macroeconomics. In this paper, we compare French and German economies in terms of the dynamic linkage between these variables. In particular, we use an empirical methodology to investigate how much the relationship between Gross Domestic Product and unemployment growth rates are dynamically different in the two major European economies over the period 2003–2019. To this aim, a Vector Autoregressive model is specified for each country to jointly model the growth rate of the two variables. Then a new statistical test is proposed to assess the distance between the two estimated models. Results indicate that the dynamic linkage between Gross Domestic Product and unemployment is very similar in the two countries. This empirical evidence does not imply identical product and labor markets in France and Germany, but it ensures that in these markets there are common dynamics. This could favor the process of economic convergence between the two countries
Dimensionality problem in testing for non causality between time series. A partial solution
No full textFor Vector Autoregressive models, the problem of dimensionality, associated with an increasing dimension of the model, can affect the power of noncausality tests. In this paper, by a Monte Carlo study, we analyze the impact of high dimensionality on the power of noncausality test and we proposed a testing strategy that, under certain conditions, limit the negative effects of high dimensionality in the causality analysis
Are the autocorrelation structures of the hemispheric‐mean temperatures significantly different?
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The nature of the trend in global and hemispheric temperatures
No full textThe aim of this note is to provide evidence about the existence or non-existence of a stochastic trend in the global temperatures time series by using standard unit root tests. Three sets of data are considered in this paper: global, Northern Hemisphere and Southern Hemisphere monthly temperature anomalies. Our main finding is that there is not a stochastic trend in the temperatures time series and they are stationary around a deterministic trend
Latitudinal variability of the dynamic linkage between temperature and atmospheric carbon dioxide concentrations: Latitudinal variability
No full textIn this paper, a novel data-driven approach is used to investigate the presence of spatial differences in the dynamic linkage between temperature and atmospheric carbon dioxide concentrations. This linkage seems to be latitude dependent. The main findings of the study are as follows. In the latitude belts surrounding the equator (0°− 24° N and 0°− 24° S), the link seems very similar. On the opposite, the patterns of the temperature CO2 link in the Arctic is very distant from those concerning the equatorial regions and other latitude bands in the South Hemisphere. This big distance is consistent with the so-called Arctic amplification phenomenon. Further, it is important to underline that this observational data-based analysis provides an independent statistical confirmation of the results from global circulation modelling
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