کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5096910 | 1376557 | 2010 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Short and long run causality measures: Theory and inference
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کلمات کلیدی
C53C15E52C51C12C32Vector autoregression - Autoregression بردارMacroeconomics - اقتصاد کلانBootstrap - بوت استرپInflation - تورمOutput - خروجیMonte Carlo - روش مونت کارلوTime series - سری زمانیGranger causality - علیت گرنجرAutoregressive model - مدل خودمراقبتیInterest rates - نرخ بهرهmoney - پولPredictability - پیش بینی پذیریVAR - که در آن
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آمار و احتمال
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The concept of causality introduced by Wiener [Wiener, N., 1956. The theory of prediction, In: E.F. Beckenback, ed., The Theory of Prediction, McGraw-Hill, New York (Chapter 8)] and Granger [Granger, C. W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods, Econometrica 37, 424-459] is defined in terms of predictability one period ahead. This concept can be generalized by considering causality at any given horizon h as well as tests for the corresponding non-causality [Dufour, J.-M., Renault, E., 1998. Short-run and long-run causality in time series: Theory. Econometrica 66, 1099-1125; Dufour, J.-M., Pelletier, D., Renault, Ã., 2006. Short run and long run causality in time series: Inference, Journal of Econometrics 132 (2), 337-362]. Instead of tests for non-causality at a given horizon, we study the problem of measuring causality between two vector processes. Existing causality measures have been defined only for the horizon 1, and they fail to capture indirect causality. We propose generalizations to any horizon h of the measures introduced by Geweke [Geweke, J., 1982. Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association 77, 304-313]. Nonparametric and parametric measures of unidirectional causality and instantaneous effects are considered. On noting that the causality measures typically involve complex functions of model parameters in VAR and VARMA models, we propose a simple simulation-based method to evaluate these measures for any VARMA model. We also describe asymptotically valid nonparametric confidence intervals, based on a bootstrap technique. Finally, the proposed measures are applied to study causality relations at different horizons between macroeconomic, monetary and financial variables in the US.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Econometrics - Volume 154, Issue 1, January 2010, Pages 42-58
Journal: Journal of Econometrics - Volume 154, Issue 1, January 2010, Pages 42-58
نویسندگان
Jean-Marie Dufour, Abderrahim Taamouti,