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

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 ﻋﻠﻰ‬
‫ﺷﺮاء 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. ‫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. ‫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. ‫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 ﺑﻴﺎﻧﺎت‬
‫‪File / New / Workfile‬‬
‫523‬

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. ‫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. ‫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. ‫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. ‫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. ‫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 ﻣﻌﻨﺎ‬
‫ﻓﻲ 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. ‫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 واﻟﺴﻤﺎح‬
‫ﺑﺎﻣﻜﺎﻧﻴﺔ 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 ﺗﺄﺛﻴﺮ‬
‫اﻟﻤﻐﻴﺮات 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 اﻟﻤﺆﺳﺴﺎت‬
‫‪We 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 time‬‬
‫‪All behavioral differences between individual firms and over time are‬‬
‫‪captured by the intercept. Only intercept parameters varies, not the slope‬‬
‫.‪parameters‬‬
‫233‬

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 ﻣﺴﻠﻢ‬
Note: The fixed effects estimator is equivalent to including a dummy
variable for each firm (Cross-Section) . But to employ the dummy
variable approach to control for individual effects is sometime
infeasible and unnecessary since we can use the Fixed Effects
Estimators approach.
Proc / estimate
333

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. ‫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 ‫ال‬
‫اﻟﺤﺴﺎﺑﻲ 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. ‫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-‬ ‫0‬
‫1_‬
‫2_‬
‫3_‬
‫4_‬
‫5_‬
‫?2‪Year? Lwage? South? Union? Exper? exper2? Tenure? tenure‬‬
‫633‬