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Effectiveness of environmental policies on carbon emissions: A panel threshold analysis

100%
Akbulut H.
Economics and Business Review
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2022
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vol. 8
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issue 3
82-108
EN
The aim of this study is to test the possible non-linear effect of environmental policy stringency on carbon emissions and thus make policy recommendations for emission reduction. For this purpose data for the period 1995–2015 for selected emerging countries were used. According to the findings obtained from fixed-effects panel threshold regressions environmental policy stringency has no significant effect on the relationship between gross domestic product per capita and carbon dioxide emissions. However, it has statistically significant effect if the share of the service sector and the foreign direct investment are taken as regime-dependent variables. Accordingly, in the high policy stringency regime an increase in the share of the service sector and the foreign direct investment reduce emission levels. In the case of using market-based environmental regulations the threshold effect faced by foreign direct investment is much more pronounced. In order to reduce carbon emissions it is recommended to increase environmental policy stringency, especially in market-based tools.
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Forecasting Returns Using Threshold Models

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Jeziorska-Pąpka M., Osińska M., Witkowski M., Nicolaus Copernicus University in Toruń D. o. E. a. S.
Acta Universitatis Lodziensis. Folia Oeconomica
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2005
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vol. 192
EN
In this paper we present the problem of forecasting efficiency of the TAR models. Three methods of forecasting are considered to compare their accuracy: the Monte Carlo method, and the two versions the bootstrap technique. The basic models are two- or three- regimes stationary threshold autoregressive models with the endogenous or exogenus switching variable. The time series set consists of the weekly stock returns of the banking sector quoted at the Warsaw Stock Exchange.
PL
Celem artykułu jest porównanie metod prognozowania nieliniowych modeli progowych. Wykorzystane zostały dwie metody prognozowania: metoda bootstrap w dwóch wariantach oraz metoda Monte Carlo. Przedmiotem analizy są tygodniowe stopy zwrotu spółek sektora bankowego, notowanych na GPW w Warszawie. W konkluzji stwierdza się, że przewidywanie dokładnych wartości stóp zwrotu jest bardzo trudne, natomiast modele progowe dają bardzo dobre wyniki w zakresie przewidywania kierunków zmian w przyszłości.
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