اسفنجیر عباسی، علی‌اصغر و رضایی روشن، هدی. (1399). بررسی تأثیر اشتغال زنان بر رشد اقتصادی در کشورهای منتخب خاورمیانه. فصلنامه علمی پژوهشی زن و جامعه، 2(42)، 226-207.
حیدری، حسن. (1390). مدل VAR جایگزین برای پیش‌بینی تورم ایران: کاربردی از تبدیل Bewley . پژوهش‌های اقتصادی ایران، 16 (46)، 77-96.
خورسندی، مرتضی، محمدی، تیمور، ارباب، حمیدرضا و سخایی، عمادالدین. (1401). آثار شوک‌های اقتصادی خارجی بر متغیرهای کلان اقتصادی ایران: رویکرد خودرگرسیون برداری جهانی (GVAR). پژوهش‌های اقتصادی ایران، 27(91)، 9-50. http://dx.doi.org/10.22054/ijer.2020.52537.868
محمدی، تیمور، عزیزخانی، فاطمه، طایی، حسن و بهرامی، جاوید. (1398). پویایی‌های کلان اقتصادی مقررات‌زدایی در بازارهای محصول و کار در کشورهای منا: رهیافت PANEL VAR. پژوهش‌های اقتصادی ایران، 24 (80)، 37-67.
Akaik, H. (1974). A new look at the statistical model identification. IEEE Trans Automat Contr. 19, 716–723.
Arellano-Valle, R B., Ozan, S., Bolfarine, H., Lachos, VH. (2005). Skew-normal measurement error models
. Journal of Multivariate Analysis, 96, 265–281.
doi.org/10.1016/j.jmva.2004.11.002
Arellano-Valle, R.B. & Azzalini, A. (2006). On the unification of families of skew-normal distributions. Scandinavian Journal of Statistics, 33, 561-574. doi.org/10.1111/j.1467-9469.2006.00503.x
Arellano-Valle, R.B. & Genton, M.G. (2005). Fundamental skew distributions. Journal of Multivariate Analysis, 96, 93– 116.
Arellano-Valle, R.B., Gomez, H.W. & Quintana, F.A. (2004). A new class of skew-normal distributions. Communication in Statistics- Theory and Methods, 33,1465-1480. doi.org/10.1081/STA-120037254
Azzalini, A. & Capitanio, A. (1999). Statistical application of the multivariate skew- normal distribution. Journal of Royal Statistical Society, series B, 61, 579-602. doi.org/10.1111/1467-9868.00194
Azzalini, A. & Capitanio, A. (2014). The Skew-Normal and Related Families. Cambridge CB2 8BS, United Kingdom.
Azzalini, A. (1985). A class of distributions which includes the normal ones. Scandinavian Journal of Statistics, 12, 171-178.
doi.org/10.6092/ASSN.1973-2201/711
Azzalini, A. (2005). The skew-normal distribution and related multivariate families. Scandinavi Journal of Statistics, 32, 159-188.
doi.org/10.1111/1467-9469. 2005.00426.x
Balakrishnan, N. & Scarpa, B. (2012). Multivariate measures of skewness for the skew-normal distribution. Journal of Multivariate Analysis, 104, 73–87. doi.org 10.1016/j.jmva.2011.06.017
Box, G. & Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. Holden-Day, San Francis.
Esfanjir, A.A. & Rezaei R.H. (1399). Investigating the effect of women’s employment on economic growth in selected countries of Middle East. Quarterly Journal of Woman and Society. 11,207-226. [ In Persian]
Faryaar, H., Macdonald, R. & Watt, J. (2022). Improving the measurement of the contribution of women to the economy: estimates of GDP.
Economic analysis division at statistics Canada.36, 28-001.
doi.org/10.25318/36280001202201000003-eng
Ghasami, S., Khodadadi, Z. & Maleki, M. (2019). Autoregressive processes with generalized hyperbolic innovations.
Communication in Statistics- Simulation and Computation.49(12), 3080-3092.
doi.org /10.1080/ 03610918.2018. 1535066
Gupta, A. & Chang, C. (2002). Multivariate skew-symmetric distributions
. Applied Mathematics Letters, 16(5),
634-646. doi.org/
10.1016/S0893-9659(03)00060-0
Heidari, H. (2011). An alternative VAR model for forecasting Iranian inflation: An application of bewley transformation. Iranian Journal of Economic Research, 46, 77-96. [ In Persian]
Khorsandi, M., Mohammadi, T., Arab, H. & Sakhaei, E. (2022). The effect of external economic shocks on Iran’s macroeconomic variable: Global VAR approach. Iranian Journal of Economic Research, 27, 9-50. doi.org/10.22054/ijer.2020.52537.868 [ In Persian]
Kilian, L. & Lütkepohl, H. (2017). Structural Vector Autoregressive Analysis. (Themes in Modern Econometrics), Cambridge, United Kingdom.
Koop, G. & Korobilis, D. (2009). Bayesian multivariate time series methods for empirical macroeconomics. Foundation and Trends in Econometrics, 3, 267-351. doi.org/10.1561/0800000013
Liu, J., Kumar, S. & Palomar, D. (2019). Parameter estimation of heavy-tailed AR model with missing data via stochastic EM. IEEE Transaction on Signal Processing, 67(8), 2159- 2172. doi.org/10.1109/ TSP.2019. 2899816
Liu, Y., Sang, R. & Liu, Sh. (2016). Diagnostic analysis for a vector autoregressive model under Student’s t- distributions statistic.
Neerlandica, 71(2), 86- 114. doi.org/
10.1111/stan.12102
Lütkepohl, H. (1991). Introduction to Multiple Time Series Analysis. Springer-Verlag. Berlin and New York.
Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer Science & Business Media.
Lütkepohl, H. (2020). Structural vector autoregressive models with more shocks than Variables identified via heteroscedasticity. Economics Letters, 195, 19510458. doi.org /10.1016/j.econlet.2020.109458
Maleki, M., Wraith, D., Mahmoudi, R. & Javier, E. (2019). Asymmetric heavy-tailed vector auto-regressive processes with application to financial data. Journal of Statistical Computation and Simulation, 90(2), 324-340. doi.org/10.1080/ 00949655. 2019.168067
Mardia, K. (1970). Measure of multivariate skewness and kurtosis with application. Biometrika, 36, 519-530. doi.org/10.1093/biomet/57.3.519
Mardia, K. (1974). Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhy- a, ser. B. 36, 115–128. doi.org/10.1080/00369791.1974.11659939
Mirzaei, H., Razban, N., Mohammadi, T. & Morovat, H. (2023). Analyzing the housing market network among Iran’s. Provinces: New evidence through variance decomposition of dorecast errors, 88, 120-157. doi.org/10.22054/joer.2024.75890.1159
Mohammadi, T., Azizkhani, F., Taei, H. & Javid, B. (1398). Macroeconomic dynamics of deregulation in product markets and work in Mena countries: Panel VAR. Iranian Journal of Economic Reasearch, 80, 37-67. doi.org/10.22054/ijer.2019.11112
Neethling, A., Ferreira, J. & Naderi, M. (2020). Skew generalized normal innovations for AR(p) process endorsing asymmetry. Symmetry, 12(8),1253. doi.org /10.3390/sym12081253
Ni, Sh. & Sun, D. (2005). Bayesian estimates for Vector autoregressive.
Journal of Business and Economic Statistics, 23, 105-117. doi.org /
10.1198/073500104000000622.
Pourahmadi, M. (2001). Foundation of Time Series Analysis and Prediction Theory; John Wiley & Sons, Inc: Hoboken, NJ, USA.
Schwarz, G. (1978). Estimating the dimension of a model. Ann Stat, 6(2), 461–464. doi.org /10.1214/aos/ 117634 4136
Sharafi, M. & Nematollahi, A. (2016). AR(1) model with skew-normal innovations. Metrika, 79, 1011–1029. doi.org /10.1007/s00184-016-0587-7.
Sims, C. (1980). Macroeconomic and reality. Econometric, 48, 1-48. doi.org/10.2307/1912017
Stock, J.H. & Watson, M.W. (2001). Vector autoregressive. Journal of Economic Perspectives, 15(4), 101-115. doi.org /10.1257/jep.15.4.10
Sujit, S., Dipak, D. & Marco, B. (2003). A new class of multivariate distributions with applications to Bayesian regression models. Canadian Journal of Statistics, 129(31), 129-150.
Tarami, B. & Pourahmadi, M. (2003). Multi-variate t autoregressive: innovations, prediction variances and exact Likelihood equations.
Journal of Time Series Analysis, 24, 739-754.
doi.org /
10.1111/j.1467- 9892. 2003.00332.x
Tsay, R.S. (2002). Analysis of Financial Time Series. John Wiley & Sons, Inc. ISBN: 0-471-41544-8
Tsung, L. (2009). Maximum likelihood estimation for multivariate skew normal mixture models
. Journal of Multivariate Analysis, 100(2),
257-265
. doi.org/10.1016/j.jmva.2008.04.010
Tsung, L., Hsiu, H. & Chiang, Chen. (2009). Analysis of multivariate skew normal models with incomplete data. Journal of Multivariate Analysis, 100, 2337-2351. doi.org/10.1016/j.mva.2009.07.005
Wai, C., Mumtaz, H. & Pinter, G. (2017). Forecasting with VAR models: fat tails and stochastic volatility.
International Journal of Forecasting,
33(4), 1124-1143.
doi.org/ 10.1016/j.ijforecast.2017.03.001
Wei, W. (2006). Time Series Analysis Univariate and Multivariate Methods. Boston, Pearson Addison Wesley.
Zamani Mehryan, S. & Sayyareh, A. (2015). Statistical inference in autoregressive models with non-negative residuals. Statistical Research and Training Center, Iran JSRI 2015. 12(1), 83-104.
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