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Panal data and the eviews

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لشرح طريقة تحليل البيانات المقطعية عبر الزمن (بانل داتا) على برنامج الايفيوز الاقتصادي

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Panal data and the eviews

1. 1. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬ ‫اﻟﻔﺼﻞ اﻟﺤﺎدي ﻋﺸﺮ‬ ‫ﻧﻤﺎذج اﻟﺒﻴﺎﻧﺎت اﻟﻤﻘﻄﻌﻴﺔ ﻋﺒﺮ اﻟﺰﻣﻦ‬ ‫‪Panel Data - Longitudinal Data‬‬ ‫ﺗﻤﻬﻴﺪ:‬‫ﻻ ﺷﻚ أن أﻓﻀﻞ وﺳﻴﻠﺔ ﻟﺘﺒﺴﻴﻂ وﺷﺮح ﻣﻮﺿﻮع اﻟﺒﻴﺎﻧﺎت اﻟﻤﻘﻄﻌﻴﺔ ﻋﺒﺮ اﻟﺰﻣﻦ ‪(A group of‬‬ ‫)‪ cross – sectional units who are observed over time‬واﻟﻤﻌﺮوﻓﺔ ﻓﻲ اﻷﻗﺘﺼﺎد‬ ‫اﻟﻘﻴﺎﺳﻲ ﺑﺎﺳﻢ )‪ (Panel Data - Longitudinal Data‬ﻳﻜﻮن ﻣﻦ ﺧﻼل ﺗﻘﺪﻳﻢ وﺷﺮح ﻣﺜﺎل.‬ ‫ﻣﺜﺎل:)‪(Grunfeld’s Investment Data‬‬ ‫ﻳﻌﺮف اﻟﻤﺜﺎل اﻟﺤﺎﻟﻲ ﺑﻌﻨﻮان )‪ (Grunfeld’s Investment Data‬وﻓﻴﻪ ﻳﺘﻢ ﻗﻴﺎس ﺑﻴﺎﻧﺎت ﻋﻦ‬ ‫ﻋﺸﺮة ﻣﺆﺳﺴﺎت )‪ (N = 10 Firms‬ﺧﻼل ﻋﺸﺮﻳﻦ ﺳﻨﺔ )‪ . (T = 20 Years‬وﻳﺮﻣﺰﻟﻠﻤﺘﻐﻴﺮ‬ ‫اﻟﺘﺎﺑﻊ و هﻮ اﻷﺳﺘﺜﻤﺎر ﺑﺎﻟﺮﻣﺰ )‪ I - gross investment (i‬وهﻮ ﻋﺒﺎرة ﻋﻦ ﻗﺪرة اﻟﻤﺆﺳﺴﺔ ﻋﻠﻰ‬‫ﺷﺮاء اﻟﺴﻠﻊ اﻟﻤﺘﻴﻨﺔ ‪ Durable Goods‬وﻳﺮﻣﺰ ﻟﻠﻤﺘﻐﻴﺮ اﻟﻤﺴﺘﻘﻞ اﻷول ﻓﻲ هﺬا اﻟﻤﺜﺎل وهﻮ ﻗﻴﻤﺔ‬ ‫)‪F - market value of the firm at the end of the (F‬‬ ‫اﻟﻤﺆﺳﺴﺔ ﻓﻲ ﻧﻬﺎﻳﺔ اﻟﺴﻨﺔ اﻟﺴﺎﺑﻘﺔ ﺑﺎﻟﺮﻣﺰ‬ ‫‪ previous year‬وﻳﺮﻣﺰ ﻟﻠﻤﺘﻐﻴﺮ اﻟﻤﺴﺘﻘﻞ اﻟﺜﺎﻧﻲ ﻓﻲ هﺬا اﻟﻤﺜﺎل وهﻮ ﻗﻴﻤﺔ اﻟﻤﻌﻤﻞ ﻣﻊ اﻟﻶﻻت‬ ‫ﻓﻲ ﻧﻬﺎﻳﺔ اﻟﺴﻨﺔ اﻟﺴﺎﺑﻘﺔ ﺑﺎﻟﺮﻣﺰ )‪C - capital stock measure (value of the stock of plant and (C‬‬ ‫.)‪ . equipment at the end of the previous year‬أﻣﺎ اﻟﻤﺆﺳﺴﺎت اﻟﻌﺸﺮ ﻓﻬﻲ:‬ ‫وﻳﺮﻣﺰ ﻟﻬﺬﻩ‬ ‫)..‪(General Motors, Chrysler, General Electric, Westinghouse and U.S. Steel..etc‬‬ ‫اﻟﻤﺆﺳﺴﺎت ﺑﺎﻟﺮﻣﺰ:‬‫‪_AR‬‬‫‪_CH‬‬‫‪_DM‬‬‫‪_GE‬‬‫‪_GM‬‬‫‪_GY‬‬‫‪_IB‬‬‫‪_UO‬‬‫‪_US‬‬‫‪_WH‬‬ ‫223‬
2. 2. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬ ‫وﺗﺘﻀﺢ اﻟﺒﻴﺎﻧﺎت ﻟﻠﻤﺆﺳﺴﺎت اﻟﻌﺸﺮ ﺧﻼل اﻟﻔﺘﺮة ﻣﻦ 5391 اﻟﻰ 4591 ﻓﻲ ﺻﻔﺤﺎت ال‬ ‫‪ Excel‬اﻵﺗﻴﺔ:‬ ‫323‬
3. 3. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬ ‫423‬
4. 4. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬ ‫‪ Excel‬ﻋﻠﻰ ﺻﻔﺤﺔ ال ‪ EViews‬ﻋﻠﻰ اﻟﻨﺤﻮ اﻵﺗﻲ:‬ ‫وﻳﺘﻢ ﻗﺮاءة ﺑﻴﺎﻧﺎت‬‫‪File / New / Workfile‬‬ ‫523‬
5. 5. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬ ‫623‬
6. 6. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬‫‪Object / New Object /Pool‬‬ ‫‪ Copy / Paste‬اﻟﺮﻣﻮز ﻟﻠﻤﺆﺳﺴﺎت واﻟﻤﺒﻴﻨﺔ ﻓﻲ اﻟﺼﻔﺤﺔ 223 أﻋﻼﻩ:‬ ‫723‬
7. 7. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬‫)‪Proc / Import Pooled Data (excel‬‬ ‫823‬
8. 8. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬‫)‪View / Spreadsheet (stacked data‬‬ ‫923‬
9. 9. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬ ‫033‬
10. 10. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬ ‫وﻳﺘﻤﻴﺰ ﻣﻮﺿﻮع اﻟﺒﻴﺎﻧﺎت اﻟﻤﻘﻄﻌﻴﺔ ﻋﺒﺮ اﻟﺰﻣﻦ ‪(A group of cross – sectional‬‬ ‫)‪ units who are observed over time‬واﻟﻤﻌﺮوﻓﺔ ﻓﻲ اﻷﻗﺘﺼﺎد اﻟﻘﻴﺎﺳﻲ ﺑﺎﺳﻢ‬ ‫)‪ (Panel Data - Longitudinal Data‬ﻋﻦ ﻣﻮﺿﻮع اﻟﻤﺘﻐﻴﺮات اﻟﺘﺮﻣﻴﺰﻳﺔ اﻟﺬي ﻣﺮ ﻣﻌﻨﺎ‬‫ﻓﻲ اﻟﻔﺼﻞ اﻟﺮاﺑﻊ ﻣﻦ هﺬا اﻟﻜﺘﺎب ﺑﺄﻧﻪ ﻻ ﻳﺤﺘﺎج اﻟﻰ اﻓﺘﺮاض ﺛﺒﺎت اﻟﺘﺒﺎﻳﻦ ﺑﻴﻦ اﻟﻮﺣﺪات اﻟﻤﻘﻄﻌﻴﺔ.‬ ‫أﻣﺎ ﻣﻦ ﺣﻴﺚ ﺗﺤﻠﻴﻞ اﻷﻗﺘﺼﺎد اﻟﻘﻴﺎﺳﻲ ﻓﺴﻮف أﻋﻤﻞ ﻋﻠﻰ ﺗﻘﺪﻳﻢ وﺷﺮح ﻃﺮق ﻗﻴﺎﺳﻴﺔ ﻟﻠﺘﻌﺎﻣﻞ ﻣﻊ‬ ‫اﻟﻤﺜﺎل أﻋﻼﻩ )‪ (Grunfeld’s Investment Data‬وﻓﻴﻪ ﻧﺄﺧﺬ:‬ ‫)‪i = F(f , c‬‬ ‫133‬
11. 11. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬ ‫ﺷﺒﻴﻬﺔ ﺑﻄﺮﻳﻘﺔ‬ ‫‪Seemingly Unrelated Regression SUR‬‬ ‫ﺗﻌﺘﺒﺮ ﻃﺮﻳﻘﺔ‬ ‫ﻣﺘﻐﻴﺮات اﻟﺘﺮﻣﻴﺰ اﻟﺘﻲ ﻣﺮت ﻓﻲ اﻟﻔﺼﻞ اﻟﺮاﺑﻊ اﻻأﻧﻪ ﻳﻌﻴﺐ ﻃﺮﻳﻘﺔ اﻟﻤﺘﻐﻴﺮات اﻟﺘﺮﻣﻴﺰﻳﺔ ﻟﻠﻔﺼﻞ‬ ‫اﻟﺮاﺑﻊ أﻧﻬﺎ ﺗﻔﺘﺮض ﺛﺒﺎت اﻟﺘﺒﺎﻳﻦ و ﺑﺎﻟﺘﺎﻟﻲ ﺗﻔﺘﺮض ﻋﺪم وﺟﻮد ﻋﻼﻗﺎت ﺑﻴﻦ اﻟﻤﺘﻐﻴﺮات اﻟﻌﺸﻮاﺋﻴﺔ‬ ‫وهﻮ أﻣﺮ ﻳﺼﻌﺐ ﺗﺒﺮﻳﺮﻩ ذﻟﻚ أن ﺗﺄﺛﻴﺮ اﻟﻤﺘﻐﻴﺮات اﻟﻤﺤﺬوﻓﺔ ﻋﻠﻰ اﻟﻤﺆﺳﺴﺔ اﻷوﻟﻰ ﻗﺪ ﻳﻜﻮن ذات‬ ‫اﻟﺘﺄﺛﻴﺮ ﻋﻠﻰ اﻟﻤﺆﺳﺴﺔ اﻟﺜﺎﻧﻴﺔ أو اﻟﺜﺎﻟﺜﺔ أو....اﻟﻌﺎﺷﺮة ﻓﻜﻞ هﺬﻩ اﻟﻤﺆﺳﺴﺎت ﺗﻨﺘﺞ ﺳﻠﻌﺎ ﻣﺘﺸﺎﺑﻬﺔ‬ ‫ﻟﺬﻟﻚ ﻧﺠﺪ أن ﻃﺮﻳﻘﺔ ‪ SUR‬واﻟﺘﻲ ﺗﺸﺒﻪ ‪ Least Dummy Variables LDV‬ﺗﺘﻤﻴﺰ‬ ‫ﻋﻦ ﻃﺮﻳﻘﺔ اﻟﺘﺮﻣﻴﺰ اﻟﺘﻘﻠﻴﺪﻳﺔ ﻓﻲ ﻋﺪم اﻓﺘﺮاض ﺛﺒﺎت اﻟﺘﺒﺎﻳﻦ ﺑﻴﻦ اﻟﻮﺣﺪات اﻟﻤﻘﻄﻌﻴﺔ واﻟﺴﻤﺎح‬‫ﺑﺎﻣﻜﺎﻧﻴﺔ وﺟﻮد ﻋﻼﻗﺔ ﺑﻴﻦ اﻟﻤﺘﻐﻴﺮات اﻟﻌﺸﻮاﺋﻴﺔ ﻟﻠﻮﺣﺪات اﻟﻤﻘﻄﻌﻴﺔ. وﺑﻤﻌﻨﻰ أدق ﻓﺎﻧﻪ ﻳﻮﺟﺪ ﻋﻼﻗﺔ‬ ‫ﺑﻴﻦ ﺗﺄﺛﻴﺮ اﻟﻤﻐﻴﺮات اﻟﻤﺤﺬوﻓﺔ ﻟﻠﻮﺣﺪات اﻟﻤﻘﻄﻌﻴﺔ. ﻓﺎذا أﺧﺬﻧﺎ اﻟﺒﻴﺎﻧﺎت ﻟﻠﻌﺸﺮﻳﻦ ﺳﻨﺔ ﻟﻜﻞ ﻣﻦ‬ ‫‪ General Motors‬و‪ Chrysler‬ﻣﺜﻼ ﻟﻮﺟﺪﻧﺎ أﻧﻪ ﻻ ﻳﺠﺪ ﻋﻼﻗﺔ ﺑﻴﻦ1‪ e‬و 2‪ e‬و 3‪e20 …… e‬‬ ‫ﻟﻠﻤﺆﺳﺴﺔ اﻷوﻟﻰ ‪Chrysler‬‬ ‫آﺬﻟﻚ ﻻ ﻳﺠﺪ ﻋﻼﻗﺔ ﻋﻼﻗﺔ ﺑﻴﻦ1‪ e‬و 2‪ e‬و 3‪ e20 …… e‬ﻟﻠﻤﺆﺳﺴﺔ اﻟﺜﺎﻧﻴﺔ ‪General Motors‬‬ ‫ﻟﻜﻦ ﻧﻈﺮا ﻷن ﺗﺄﺛﻴﺮ اﻟﻤﻐﻴﺮات اﻟﻤﺤﺬوﻓﺔ ﻋﻠﻰ اﻟﻤﺆﺳﺴﺔ اﻷوﻟﻰ ‪ Chrysler‬ﻗﺪ ﺗﻜﻮن ذات‬ ‫اﻟﻤﻐﻴﺮات اﻟﻤﺤﺬوﻓﺔ ﻋﻠﻰ اﻟﻤﺆﺳﺴﺔ اﻟﺜﺎﻧﻴﺔ ‪ General Motors‬ﻟﺬﻟﻚ ﻳﻤﻜﻨﻨﺎ اﻓﺘﺮاض أن ﺗﺄﺛﻴﺮ‬‫اﻟﻤﻐﻴﺮات اﻟﻤﺤﺬوﻓﺔ 1‪ e‬ﻟﻠﻤﺆﺳﺴﺔ اﻷوﻟﻰ ‪ Chrysler‬ﻟﻪ ﻋﻼﻗﺔ ﺑﺘﺄﺛﻴﺮ اﻟﻤﻐﻴﺮات اﻟﻤﺤﺬوﻓﺔ 1‪e‬‬ ‫ﻟﻠﻤﺆﺳﺴﺔ اﻟﺜﺎﻧﻴﺔ ‪ General Motors‬و ﺗﺄﺛﻴﺮ اﻟﻤﻐﻴﺮات اﻟﻤﺤﺬوﻓﺔ 2‪ e‬ﻟﻠﻤﺆﺳﺴﺔ اﻷوﻟﻰ‬ ‫‪ Chrysler‬ﻟﻪ ﻋﻼﻗﺔ ﺑﺘﺄﺛﻴﺮ اﻟﻤﻐﻴﺮات اﻟﻤﺤﺬوﻓﺔ 2‪ e‬ﻟﻠﻤﺆﺳﺴﺔ اﻟﺜﺎﻧﻴﺔ ‪ General Motors‬وهﻜﺬا‬ ‫ﺑﺎﻟﻨﺴﺒﺔ ﻟﺒﻘﻴﺔ اﻟﻤﺘﻐﻴﺮات اﻟﻌﺸﻮاﺋﻴﺔ ﻟﻜﻞ اﻟﻤﺆﺳﺴﺎت اﻟﻌﺸﺮ وهﻮ ﻣﺎ ﻳﻌﺮف ﺑﻔﺮﺿﻴﺔ‬ ‫‪ Contemporaneous Correlation‬ﻟﺬﻟﻚ ﻓﺎﻧﻨﺎ ﻧﺴﺘﺨﺪم ﻃﺮﻳﻘﺔ أآﺜﺮ ﻋﻤﻮﻣﻴﺔ وهﻲ‬ ‫اﻟﻤﻌﺮوﻓﺔ ب ‪ GLS‬أي اﻟﻌﺮوﻓﺔ ب ‪Generalized Least Square Method‬‬ ‫اﻟﺠﺪﻳﺮ ذآﺮﻩ أن ﻃﺮﻳﻘﺔ ‪ Fixed Effects‬واﻟﺘﻲ ﺳﻮف ﻧﻌﺮﺿﻬﺎ اﻵن ﺗﻔﺘﺮض أن ﻗﻴﻤﺔ‬ ‫اﻟﺜﺎﺑﺖ 0‪ b‬ﻳﺨﺘﻠﻒ ﻟﻜﻞ ﻣﺆﺳﺴﺔ ﺑﻴﻨﻤﺎ ﻳﻔﺘﺮض ﺛﺒﺎت ﻣﻌﺎﻣﻞ اﻷﻧﺤﺪار 1‪ b‬ﻟﻜﻞ اﻟﻤﺆﺳﺴﺎت‬ ‫واﻟﺬي ﻧﻬﺘﻢ ﺑﻪ ﻧﺘﻴﺠﺔ هﺬا اﻟﺘﺤﻠﻴﻞ هﻮ ﺗﻔﺴﻴﺮ ﻗﻴﻤﺔ ﻣﻌﺎﻣﻞ اﻷﻧﺤﺪار 1‪ b‬ﻟﻜﻞ اﻟﻤﺆﺳﺴﺎت‬‫‪We allow the intercept for each firm to vary across firms not over time‬‬‫.‪but restricts the slope parameter to be constant across all firms and time‬‬‫‪All behavioral differences between individual firms and over time are‬‬‫‪captured by the intercept. Only intercept parameters varies, not the slope‬‬‫.‪parameters‬‬ ‫233‬
12. 12. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ . " ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ‬ . ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ‬Note: The fixed effects estimator is equivalent to including a dummyvariable for each firm (Cross-Section) . But to employ the dummyvariable approach to control for individual effects is sometimeinfeasible and unnecessary since we can use the Fixed EffectsEstimators approach.Proc / estimate 333
13. 13. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬ ‫هﻮ ﺻﻔﺮا‬ ‫اﻟﺠﺪﻳﺮ ذآﺮﻩ أن ﻣﺠﻤﻮع ال )‪Fixed Effects (cross‬‬ ‫‪Fixed Effects‬‬ ‫87255.11-‬ ‫8946.061‬ ‫9728.671-‬ ‫46439.03‬ ‫78278.55-‬ ‫46285.53‬ ‫435908.7-‬ ‫282891.1‬ ‫33874.82-‬ ‫1671.25‬ ‫‪SUM‬‬ ‫0‬ ‫433‬
14. 14. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬‫وﻳﻌﻮد اﻟﺴﺒﺐ ﻓﻲ أن ﻣﺠﻤﻮع ال ‪ Fixed Effects‬هﻮ ﺻﻔﺮا اﻟﻰ أﻧﻨﺎ ﻟﻮ أﺧﺬﻧﺎ اﻷﺳﺘﺜﻤﺎر‬ ‫داﻟﺔ ﻓﻲ اﻟﻤﺘﻐﻴﺮﻳﻦ اﻟﻤﺴﺘﻘﻠﻴﻦ اﺿﺎﻓﺔ اﻟﻰ ﻋﺸﺮة ﻣﺘﻐﻴﺮات ﺗﺮﻣﻴﺰﻳﺔ ﺗﻘﻴﺲ اﻷﺧﺘﻼف ﺑﻴﻦ‬ ‫اﻟﻤﺆﺳﺴﺎت اﻟﻌﺸﺮة ﻟﺤﺼﻠﻨﺎ ﻋﻠﻰ:‬ ‫وﻳﻼﺣﻆ أﻋﻼﻩ أن ﻣﻌﺎﻣﻼت اﻷﻧﺤﺪار ﻟﻠﻤﺘﻐﻴﺮات اﻟﻤﺴﺘﻘﻠﺔ هﻲ ذاﺗﻬﺎ اﻟﺘﻲ ﺣﺼﻠﻨﺎ ﻋﻠﻴﻬﺎ ﺑﺎﺳﺘﺨﺪام‬ ‫‪ Fixed Effects Model‬آﻤﺎ وأن ﻗﻴﻤﺔ اﻟﺜﺎﺑﺖ 0‪ 58.78 = b‬هﻮ اﻟﻮﺳﻂ‬ ‫ال‬‫اﻟﺤﺴﺎﺑﻲ ﻟﻤﻌﺎﻣﻼت اﻷﻧﺤﺪار اﻟﻌﺸﺮة ﻟﻠﻤﺘﻐﻴﺮات اﻟﺘﺮﻣﻴﺰﻳﺔ .ﻋﻠﻤﺎ أن ال ‪Fixed Effects‬‬ ‫هﻲ اﻟﻔﺮق ﺑﻴﻦ ﻣﻌﺎﻣﻞ اﻷﻧﺤﺪار ﻟﻠﻤﺘﻐﻴﺮ اﻟﺘﺮﻣﻴﺰي واﻟﻮﺳﻂ اﻟﺤﺴﺎﺑﻲ )ﻗﻴﻤﺔ اﻟﺜﺎﺑﺖ 0‪= b‬‬ ‫87.85( ﻟﻠﻤﺘﻐﻴﺮات اﻟﺘﺮﻣﻴﺰﻳﺔ.‬ ‫5341.96-‬ ‫1674414.01-‬ ‫533‬
15. 15. ‫ﺍﻟﺼﻼﺓ ﻭ ﺍﻟﺴﻼﻡ ﻋﻠﻰ ﺳﻴﺪﻧﺎ ﳏﻤﺪ ﺍﻟﻘﺎﺋﻞ " ﺇﺫﺍ ﻣﺎﺕ ﺍﺑﻦ ﺁﺩﻡ ﺍﻧﻘﻄﻊ ﻋﻤﻠﻪ ﺇﻻ‬ ‫ﻣﻦ ﺛﻼﺙ : ﺻﺪﻗﺔ ﺟﺎﺭﻳﺔ ، ﻭ ﻋﻠﻢ ﻳﻨﺘﻔﻊ ﺑﻪ ، ﻭ ﻭﻟﺪ ﺻﺎﱀ ﻳﺪﻋﻮ ﻟﻪ " .‬ ‫ﺭﻭﺍﻩ ﻣﺴﻠﻢ .‬ ‫4268.001‬ ‫9304195.951‬ ‫911.532-‬ ‫696983.671-‬ ‫536.72-‬ ‫9320490.13‬ ‫713.511-‬ ‫1698785.65-‬ ‫6370.32-‬ ‫9334556.53‬ ‫9286.66-‬ ‫1629359.7-‬ ‫6853.75-‬ ‫9304073.1‬ ‫772.78-‬ ‫1600845.82-‬ ‫72645.6-‬ ‫9437281.25‬ ‫927.85-‬ ‫0‬‫1_‬‫2_‬‫3_‬‫4_‬‫5_‬‫?2‪Year? Lwage? South? Union? Exper? exper2? Tenure? tenure‬‬ ‫633‬