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ÆäÀÌÁö´Â °è¼Ó ¾÷µ¥ÀÌÆ® µÉ °ÍÀÔ´Ï´Ù. ÀÌ ÆäÀÌÁöÀÇ Á¤º¸´Â EViews 5 ¹öÀü¿¡ ´ëÇÑ Á¤º¸ À§ÁÖ·Î ÀÛ¼ºµÇ¾úÀ¸¸ç,
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EViews ÀÎÅÍÆäÀ̽ºÀÇ ¸ñÀûÀº °·ÂÇÔ°ú Á÷°ü¼º µÎ°¡Áö
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EViews ´Â °´Ã¼ °³³ä Á¢¸ñ µÇ¾î °³¹ßµÇ¾ú½À´Ï´Ù. ½Ã¸®Áî(Series),
¹æÁ¤½Ä ¹× ½Ã½ºÅÛµéÀº µÎ¼ÂÀÇ °´Ã¼ ¿¹Á¦µéÀÔ´Ï´Ù. °¢ °´Á¦´Â ÀÚü À©µµ¿ì, ¸Þ´º, ÀýÂ÷ ±×¸®°í
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°á°ú, °è¼ö °øºÐ»ê Çà·Ä ¹× ±×·¡ÇÈµé »çÀ̸¦ ÀüȯÇÕ´Ï´Ù. ÀÌ ±×·¡ÇȵéÀº Á¾¼Óº¯¼ö, Å×À̺í, ¿¹Ãø ±×·¡ÇÁµé,
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¹°·Ð, °£´ÜÈ÷ ¸Þ´º ¼±ÅÃÀ» ÅëÇØ ÀÌ·¯ÇÑ ±×¸²(view)µéÀ»
ÀÚ¸£±â-ºÙ¿©³Ö±â ±â´ÉÀ» ÀÌ¿ëÇÏ¿© ¿©·¯ºÐÀÌ ÀÚÁÖ ÀÌ¿ëÇÏ´Â ¿öµå ÇÁ·Î¼¼¼ ¾ÈÀ¸·Î ³ÖÀ» ¼ö ÀÖ½À´Ï´Ù. ½ºÇÁ·¹Æ®½ÃÆ®¿Í
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EViews´Â Æø³ÐÀº µ¥ÀÌÅÍ Çü½ÄµéÀ»
Á÷Á¢ ÀÐ°í ¾µ ¼ö ÀÖ½À´Ï´Ù. - ¿¢¼¿( Excel), ÅØ½ºÆ®(ASCII/Text), SAS,
Stata, SPSS, RATS, Html, Access, ÀÌÁø µ¥ÀÌÅÍ(Binary), ODBC µ¥ÀÌÅͺ£À̽º,
ODBC Äõ¸® (ODBC±â´ÉÀº Enterprise ¹öÀü¿¡¼¸¸ °¡´É), À̿ܿ¡ ¸¹Àº ÀÚ·á ÇüµéÀ» Áö¿ø
- ÆÄÀÏÀ» ÀÐÀ» ¶§, ´ëºÎºÐÀÇ °æ¿ì ÇØ´ç ÆÄÀÏÀ» EViews ¾ÈÀ¸·Î ¸¶¿ì½º·Î ²ø¾î´Ù ³õÀ¸¸é
µË´Ï´Ù. |
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¹× ¿¹Ãø ¾÷¹«¿¡¼ ÀÚÁÖ »ç¿ëµÇ´Â ÅøµéÀ» Á¦°øÇÕ´Ï´Ù.
±âÃÊ Åë°è·®
Çϳª ¶Ç´Â ±× ÀÌ»óÀÇ º¯¼öµé ¶Ç´Â Ⱦ´Ü¸é ÀÚ·á ¶Ç´Â Period
in Panel ¶Ç´Â Pooled µ¥ÀÌÅÍ¿¡ ±âÃʸ¦ µÎ°í ºÐ·ùÇÑ Àüü Ç¥º»¿¡ ´ëÇØ ¼³¸íÀ» Æ÷ÇÔÇÏ´Â ±âº»ÀûÀÎ
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¼ö ÀÖ°í, ƯÁ¤ °ªµé¿¡ ´ëÇÑ °ËÁ¤, ½Ã¸®Áîµé(series) »çÀÌÀÇ µ¿Àϼº °ËÁ¤, ¶Ç´Â ´Ù¸¥ º¯¼öµé¿¡
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È÷½ºÅä±×·¥,
´©Àû ºÐÆ÷µµ, survivor, quantile(ºÐÀ§¼ö) plots µîÀ» ÀÌ¿ëÇÏ¿© µ¥ÀÌÅÍÀÇ ºÐÆ÷¸¦ ±×·¡ÇÈÀ¸·Î
º¼ ¼ö ÀÖ½À´Ï´Ù. QQ-plots(Quantile-Quantile Plots)Àº µÎ½ÖÀÇ ½Ã¸®Áî ¶Ç´Â
ÀÌ·ÐÀû ºÐÆ÷ÀÇ ´Ù¾ç¼º¿¡ ´ëÇØ ÇϳªÀÇ ½Ã¸®ÁîÀÇ ºÐÆ÷¸¦ ºñ±³ÇÒ ¶§ »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù. ½Ã¸®Áî µ¥ÀÌÅͰ¡
Á¤±Ô ºÐÆ÷¸¦ ÀÌ·ç´ÂÁö, ¶Ç´Â Áö¼öÀÇ, ±ØÄ¡ °ª, ·ÎÁö½ºÆ½(logistic), Ä«ÀÌ Á¦°ö(Chi-square),
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(Kolmogorov-Smirnov), Liliefors, Cramer von Mises, Anderson-Darling
°ËÁ¤À» ¼öÇàÇÒ ¼ö ÀÖ½À´Ï´Ù. ºÐÆ÷¿¡ ´ëÇÑ ÆÄ¶ó¹ÌÅ͵éÀ» ÀÔ·ÂÇϰųª EViews°¡ ÆÄ¶ó¹ÌÅ͸¦ Æò°¡ÇÏ°Ô ÇÒ
¼ö ÀÖ½À´Ï´Ù. EViews´Â Ä¿³Î(kernel) Á¶¹Ðµµ ÃßÁ¤À» °è»êÇϰí, Ordinary, Transformation,
Kernel, ¹× Nearest Neighbor ȸ±Í µîÀ» ÀÌ¿ëÇÏ¿© Ä¿ºê ÇÇÆÃ(fitting)µÈ Scatter
plotsÀ» ±×·ÁÁÝ´Ï´Ù.
¿©·¯ºÐÀÇ µ¥ÀÌÅÍÀÇ ½Ã°è¿ ¼Ó¼ºÀ» ¿¬±¸Çϱâ À§ÇÏ¿©, EViews´Â
Unit root °ËÁ¤(ADF, Phillips-Perron, KPSS, DFGLS, ERS
and Ng-Perron for single time series and Levin-Lin-Chu,
Breitung, Im-Pesaran-Shin, Fisher, and Hadri for panel data
µî), cointegration °ËÁ¤(with MacKinnon-Haug-Michelis critical
values and p-values), Àΰú¼º °ËÁ¤(causality test), ÀÚ±â»ó°ü(autocorrelation)°ú
ºÎºÐ Àڱ⠻ó°ü ÇÔ¼öµé, Q-statistics°ú ±³Â÷»ó°ü(cross-correlation) ±â´ÉµéÀ»
Á¦°øÇÕ´Ï´Ù.
EViews´Â 18Á¾ÀÇ ´Ù¸¥ ºÐÆ÷ ºÐ¼®À» À§ÇØ ³¼ö(Random
number) »ý¼º±â(Knuth, L'Ecuyer ¶Ç´Â) Mersenne-Twiste), ¹ÐµµÇÔ¼ö(density
functions) ¹× ´©Àû ºÐÆ÷ÇÔ¼ö µîÀÇ ±â´ÉµéÀ» Á¦°øÇÕ´Ï´Ù. À̱â´ÉµéÀº »õ·Î¿î ½Ã¸®Áî »ý¼º, ¶Ç´Â
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¿äÀÎ Á¶Àý(Seasonal Adjustment) EViews´Â
¹Ì±¹ Àα¸ Á¶»ç±¹ÀÇ X11°ú X12-ARIMA °èÀýÀû Á¶Àý ÇÁ·Î±×·¥ »Ó¸¸ ¾Æ´Ï¶ó À¯·´¿¡¼ ÀÚÁÖ »ç¿ëÇÏ´Â
Tramo/Seats ¼ÒÇÁÆ®¿þ¾î¸¦ »ç¿ëÀÌ Æí¸®Çϵµ·Ï Áö¿øÇÕ´Ï´Ù. ºÎ°¡Àû ¹× Áõ°¡Àû ´Ù¸¥ ¹æ½ÄÀ» ÀÌ¿ëÇÑ
´Ü¼øÇÑ °èÀýÀû Á¶Á¤ ¿ª½Ã EViews¿¡¼ Á¦°øÇÕ´Ï´Ù.
ÇÊÅÍ(Filters)
EViews´ÂHodrick-Prescott ÇÊÅ͸¦ ÀÌ¿ëÇÏ¿©
½Ã°è¿ µ¥ÀÌÅͷκÎÅÍ µ¿ÇâÀ» °è»êÇÕ´Ï´Ù. EViews´Â Baxter-King, Christiano-Fitzgerald
fixed length °ú Christiano-Fitzgerald asymmetric full sample
band-pass (frequency) ÇÊÅÍ ±â´ÉÀ» Á¦°øÇÕ´Ï´Ù. |
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ÁÖû(Estimation)
EViews´Â ½Ã°è¿ ¹× Ⱦ´Ü¸é
µ¥ÀÌÅÍ ºÐ¼®À» À§ÇÑ ±¤¹üÀ§ÇÑ Çϳª ¶Ç´Â ´Ù¼ö ¹æÁ¤½Ä ÃßÁ¤ ±â¼úµéÀ» Á¦°øÇÕ´Ï´Ù. ±âº»ÀûÀÎ ÃßÁ¤·®µé·Î´Â
º¸ÅëÃÖ¼ÒÀÚ½Â(´ÙÁßȸ±Í), À̴ܰè ÃÖ¼ÒÀÚ½Â(two-stage least squares), ºñ¼±ÇüÃÖ¼ÒÀÚ½Â(nonlinear
least squares) µîÀÌ ÀÖ½À´Ï´Ù. ÀÌµé ±â¼úµéÀ» ÀÌ¿ëÇÏ¿© °¡ÁßÃßÁ¤À» ÇÒ ¼ö ÀÖ½À´Ï´Ù. µ¶¸³º¯¼ö¿¡
´ëÇÑ ´ÙÇ×½Ä ·¡±×(lag) ±¸Á¶ Á¤º¸¸¦ ±Ô°Ý(specification)¿¡¼ º¸½Ç ¼ö ÀÖ½À´Ï´Ù.
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ÀÌ·¯ÇÑ ±âº» ÃßÁ¤·®(Estimators)À̿ܿ¡,
EViews´Â Çâ»óµÈ ´Ù¾çÇÑ ¸ðÇüµé¿¡ ´ëÇÑ ÃßÁ¤ ¹× Áø´Ü¹ýµéÀ» Áö¿øÇÕ´Ï´Ù.
EViewsÀÇ Á¤±³ÇÑ ¹ÌÀûºÐ(Calculus) ¿£ÁøÀº ºñ¼±Çü
ȸ±Í ±Ô°Ý(specifications)ÀÇ ´ë´Ù¼ö¿¡ ´ëÇÑ ºÐ¼® µµÇÔ¼öµéÀ» °è»êÇÏ°í µð½ºÇ÷¹ÀÌ ÇØÁÝ´Ï´Ù.

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¸ðÇüµé ½Ã¸®ÁîÀÇ
ºÐ»êÀÌ ½Ã°£¿¡ ´ëÇØ ºÒ±ÔÄ¢ÇÏ°Ô º¯Çϸé, EViews´Â ³Ð°í ´Ù¾çÇÑ ARCH(Autoregressive
Conditional Heteroskedasticity) ¸ðÇüµéÀ» ÀÌ¿ëÇÏ¿© ºÐ»êÀÇ °æ·Î¸¦ ÃßÁ¤ÇÒ ¼ö
ÀÖ½À´Ï´Ù. EViews´Â GARCH(p,q), EGARCH(p,q), TARCH(p,q),
PARCH(p,q), °ú Component GARCH specifications µîÀ» ´Ù·ê ¼ö ÀÖ°í,
Ç¥ÁØ Student's t ¶Ç´Â Generlized Error Distribution ÀÌÈÄÀÇ ¿ÀÂ÷¸¦
À§ÇÑ ÃÖ´ë¿ìµµ ÃßÁ¤(maximum likelihood estimation)À» ÇÒ ¼ö ÀÖ½À´Ï´Ù. ARCH
¸ðÇüÀÇ Æò±Õ ¹æÁ¤½ÄÀº ARCH¿Í ARMA termsÀ» Æ÷ÇÔ ÇÒ ¼ö ÀÖ°í, Æò±Õ°ú ºÐ»ê ¹æÁ¤½ÄÀº ¿Ü»ýº¯¼öµéÀ»
°í·ÁÇÒ ¼ö ÀÖ½À´Ï´Ù.
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ÀϹÝÈ Àû·ü¹ý(Generalized Method
of Moments)
EViews´Â Ⱦ´Ü¸é°ú ½Ã°è¿
µ¥ÀÌÅÍ(Çϳª ¶Ç´Â ´ÙÁß ¹æÁ¤½Ä)¿¡ ´ëÇÑ GMM ÃßÁ¤ ±â¹ýÀ» Á¦°øÇÕ´Ï´Ù. °¡Áß ¿É¼ÇµéÀº Ⱦ´Ü¸é µ¥ÀÌÅÍ¿¡
White °øºÐ»ê Çà¿ ±×¸®°í ½Ã°è¿ µ¥ÀÌÅÍ¿¡ ´ëÇÑ ´Ù¾çÇÑ HAC °øºÐ»ê Çà¿À» Æ÷ÇÔÇÕ´Ï´Ù. HAC
¿É¼ÇµéÀº prewhitening, quadratic, Bartlett kernels, ±×¸®°í fixed,
Andrews, Newey-West bandwith ¼±Åà ¹æ¹ýµéÀ» Æ÷ÇÔÇÕ´Ï´Ù.
Á¦ÇÑµÈ Á¾¼Ó º¯¼öµé
Á¾¼Ó º¯¼ö°¡ Á¦ÇÑµÈ °ªµé·Î ÇÑÁ¤µÇ°Å³ª ÀϺΠ»èÁ¦ ¶Ç´Â À߸°
°æ¿ì, EViews´Â ÃßÁ¤ °úÁ¤¿¡¼ ÀÌ·¯ÇÑ Á¤º¸¸¦ ÀνÄÇÏ¿© ºÐ¼®ÇÕ´Ï´Ù. ÀÌÁø, Á¤·Ä, »èÁ¦, À߸° ¸ðÇüµéÀº
Ç¥ÁØ, ·ÎÁö½ºÆ½(logistic), ±ØÄ¡°ª ¿ÀÂ÷ µî¿¡ ±âÃÊÇÑ ¿ìµµ ÇÔ¼ö(likelihood functions)¿¡
´ëÇØ ÃßÁ¤µË´Ï´Ù. °è¼ö ¸ðÇü(Count model)Àº Æ÷¾Æ¼Û, À½ÀÌÇ×, QML(ÁØÃÖ´ë°¡´Éµµ) ±Ô°Ý µîÀ»
ÀÌ¿ëÇÕ´Ï´Ù. EViews´Â ¼±ÅÃÀûÀ¸·Î ÀϹÝÈ ¼±Çü ¸ðÇü ¶Ç´Â QML Ç¥ÁØ ¿ÀÂ÷ Á¤º¸¸¦ º¸¿©ÁÝ´Ï´Ù.
½Ã½ºÅÛ ÃßÁ¤(System Estimation)
EViews´Â OLS, À̴ܰèÃÖ¼ÒÁ¦°ö, °Ñº¸±â ¹«°ü ȸ±Í,
»ï´Ü°èÃÖ¼ÒÁ¦°ö, GMM , FIML µî¿¡ ÀÇÇÑ ¼±Çü ¹× ºñ¼±Çü ½Ã½ºÅÛ ¹æÁ¤½Äµé¿¡ ´ëÇÑ ÃßÁ¤ ±â¹ýÀ» Á¦°øÇÕ´Ï´Ù.
½Ã½ºÅÛÀº Ⱦ´Ü ¹æÁ¤½Ä Á¦ÇÑ(cross equation restrictions) °ú ¼ø¼¿¡ °ü°è
¾øÀÌ ÀÚ±âȸ±Í ¿ÀÂ÷¸¦ Æ÷ÇÔÇÕ´Ï´Ù.
º¤ÅÍ ÀÚ±âȸ±Í/¿ÀÂ÷ ¼öÁ¤ ¸ðÇü
º¤ÅÍ ÀÚ±â ȸ±Í(Vector Autoregression)¿Í
º¤ÅÍ ¿ÀÂ÷ ¼öÁ¤ ¸ðÇü(Vector Error Correction models)Àº EView¸¦ ÀÌ¿ëÇØ
½±°Ô ÇÒ ¼ö ÀÖ½À´Ï´Ù. ÀÏ´Ü ÃßÁ¤Çϸé, VAR ¶Ç´Â VEC¿¡ ´ëÇÑ ÀÓÆÞ½º ¹ÝÀÀ ÇÔ¼ö¿Í ºÐ»ê ºÐÇØ¸¦ ½ÃÇèÇØ
º¼ ¼ö ÀÖ½À´Ï´Ù. VAR ÀÓÆÞ½º ¹ÝÀÀ ÇÔ¼ö¿Í ºÐÇØ´Â ºÐ¼®ÀûÀ¸·Î ¶Ç´Â Monte Carlo ¹æ¹ý¿¡ ÀÇÇØ
°è»êµÈ Ç¥ÁØ ¿ÀÂ÷·Î Ư¡À» ÀÌ·ç¸ç, ´Ù¾çÇÑ ±×·¡ÇÈ ¹× »êÃâµÈ Ç¥ ÇüÅ·Πµð½ºÇ÷¹ÀÌÇÒ ¼ö ÀÖ½À´Ï´Ù.
ÅëÇÕ °ü°è ¶Ç´Â Á¶Á¤ °è¼ö¿¡ ´ëÇÑ ¼±Çü Á¦¾àÀ» Áְųª °ËÁ¤ÇØ
º¼ ¼ö ÀÖ½À´Ï´Ù. EViewÀÇ VARs ¿ª½Ã ´Ü±â(Sims 1986) ¶Ç´Â Àå±â(Blanchard and
Quah 1989) Á¦¾àÀ» µÒÀ¸·Î½á ±¸Á¶Àû ÀμöºÐÇØ¸¦ ÃßÁ¤ÇÒ ¼ö ÀÖ½À´Ï´Ù. EViews¿¡ ÀÇÇØ Á¦ÃâµÈ
LF Åë°è·®À» ÀÌ¿ëÇÏ¿© Over-identifying Á¦¾àÀ» °ËÁ¤ÇØ ºÒ ¼ö ÀÖ½À´Ï´Ù.
VARs´Â ÃßÁ¤ÇÑ ±Ô°Ý(specifications)ÀÇ ±¸Á¶¸¦
½ÃÇèÇØ º¼ ¼ö ÀÖµµ·Ï ´Ù¾çÇÑ º¸±â ¿É¼ÇÀ» Á¦°øÇÕ´Ï´Ù. With a few clicks of the mouse,
you can display the inverse roots of the characteristic
AR polynomial, perform Granger causality and joint lag exclusion
tests, evaluate various lag length criteria, view correlograms
and autocorrelations, or perform various multivariate residual
based diagnostics.
Panel Data Analysis
and Pooled Time Series-Cross Section
EViews features a wide variety
of tools designed to facilitate working with panel or pooled/time
series-cross section data. Unbalanced or balanced data sets
with unlimited length time and/or cross-sections are easily
analyzed. In addition to ordinary linear and non-linear
least-squares, equation estimation methods include 2SLS/IV
and Generalized 2SLS/IV, which can be used to estimate complex
dynamic panel data estimation including Anderson-Hsiao and
Arellano-Bond types of estimators.
All of these methods allow both
time and cross-section fixed and random effect specifications.
For random effects models, quadratic unbiased estimators
of component variances include Swamy-Arora, Wallace-Hussain
and Wansbeek-Kapteyn.
Also supported are AR specifications,
weighted least squares, and seemingly unrelated regression.
Coefficients on specific variables (including AR terms)
can be constrained to be identical, or allowed to differ
across the cross-section.
In addition, panel analysis
includes 1) LR-type testing for omitted or redundant regressors
in panel and pool equations specified by list; 2) Redundant
fixed effects testing for panel and pool equations estimated
by ordinary linear and nonlinear least squares; and 3) Hausman
random effects testing evaluates the restriction that the
random effects are uncorrelated with the explanatory variables.
State-Space Models
The state-space object allows estimation
of a wide variety of single- and multi-equation dynamic
time-series models using the Kalman Filter algorithm. Among
other things, you can use the state-space object to estimate
random and time-varying coefficient models and ML ARMA specifications.
Sophisticated procs and views
give you access to powerful filtering and smoothing tools
so that you can view or generate one-step ahead, filtered,
or smoothed signals, states, or errors. EViews' built-in
forecasting procedures also provide easy-to-use tools for
in- and out-of-sample forecasting using n-step ahead or smoothed values.
User-Defined Maximum
Likelihood Estimation
EViews features an object
(the LogL) for handling user-specified maximum likelihood
estimation problems. Simply use standard EViews expressions
to describe the log likelihood contribution of each observation
in your sample, and EViews will do the rest.
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Specification Evaluation and Diagnostics
Once an equation or system is estimated,
you can use EViews to perform a wide array of specification
evaluation and diagnostic tests.
These tests include Wald tests
of linear and nonlinear coefficient restrictions, likelihood
ratio and F-tests for omitted variables, Lagrange multiplier
tests for serial correlation and ARCH, White heteroskedasticity
tests, Ramsey RESET tests, and Chow forecast and breakpoint
tests.
Additional tests exist for specific
models. As with other object views, all hypothesis tests
can be generated by a simple menu selection from an equation
or system window.
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Forecasting
and Simulation
In EViews, you need not concern
yourself with the complexities of making forecasts. You
can concentrate on the substance of the forecasting problem.
For single equation models, just select a menu item and
EViews will compute a static or dynamic forecast with optional
forecast standard errors and a graph of the 95 percent forecast
confidence. Successful forecasting equations can be saved
in your workfile or stored in an EViews database.
Simultaneous Equation
Solution and Simulation
The model object, which is used
for simultaneous equation simulation and solution, provides
the features most commonly requested by model builders.
Variable dependencies and the block
structure of the model¡¯s equations are displayed with a
simple mouse click. Reference equations by name and the
model is updated automatically whenever the equation is
re-estimated. You can even use the model to manage multiple
solution scenarios for comparing simulation results under
various sets of assumptions.
The EViews model object makes it
easy to perform non-stochastic or stochastic simulation
using either Gauss-Seidel or Newton solvers. Built-in views
and procedures display simulation results in graphical or
tabular form. Forward solution (currently unavailable with
stochastic solution) allows you to solve for model consistent
expectations. EViews provides sophisticated add factor support,
including equation normalization. You can even solve simple
control problems where the values for an exogenous control
variable are found so that an endogenous variable achieves
a user specified target.

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Data Management
Powerful modeling tools are only
useful if you can easily access your data. EViews provides
the widest range of data management tools available in any
econometric software.
Extensive Function
Library
EViews contains an extensive library
of functions for working with and transforming your data.
In addition to standard mathematical and trigonometric functions,
EViews provides functions for computing descriptive statistics,
by-group statistics, specialized date and time series data
functions, functions for working with a variety of statistical
distributions and date and string handling.
Sophisticated Expression
Handling
EViews¡¯ powerful tools for expression handling mean that you can
use expressions virtually anywhere you would use a series.
You don't have to create new variables to work with the
logarithm of Y, the moving average of W, or the ratio of
X to Y (or any other valid expression). Instead, you can
use the expression in computing descriptive statistics,
as part of an equation or model specification, or in constructing
graphs.
When you forecast using an equation
with an expression for the dependent variable, EViews will
(if possible) allow you to forecast the underlying dependent
variable and will adjust the estimated confidence interval
accordingly. For example, if the dependent variable is specified
as LOG(G), you can elect to forecast either the log or the
level of G, and to compute the appropriate, possibly asymmetric,
confidence interval.
Links, Formulas and
Values Maps
Links allow you to create series
that link to data contained in other workfiles or workfile
pages. Links allow you to combine data at different frequencies,
or match merge in data from a summary page into an individual
page such that the data is dynamically updated whenever
the underlying data change. Similarly, within a workfile,
formulas can be assigned to data series so that the data
series are automatically recalculated whenever the underlying
data is modified.
Value labels (e.g., "High",
"Med", "Low", corresponding to 2, 1,
0) may be applied to numeric or alpha series so that categorical
data can be displayed with meaningful labels. Built-in functions
allow you to work with either the underlying or the mapped
values when performing calculations.
Data types
EViews can handle complex data
structures, including irregular dated data, cross-section
data with observation identifiers, and dated and undated
panel data. In addition to numerical data, an EViews workfile
can also contain alphanumeric (character string) data and
series containing dates, which can be further manipulated
using an extensive library of functions.
In addition EViews provides a wide
range of tools for manipulating your data. Included is the
ability to combine series by complex match merge criteria.
Workfile (dataset) procedures for changing the structure
of your data include: join, append, subset, resize,
sort, and reshape (stack and unstack). |
File Import and Export
EViews provides extensive read/write
support for foreign formats, including Excel, ASCII/Text,
SPSS, SAS (transport), Stata, Html, Microsoft Access,
Gauss Dataset, Rats, WinGive/PC Give, TSP, Aremos (.tsd),
dBase, Lotus and Binary. Access to SAS native format files,
version 8 or earlier, is also available if a SAS ODBC driver
is installed on the system (which must be purchased separately
through SAS).
EViews Databases
EViews has built-in database features.
An EViews database is a collection of EViews objects maintained
in a single file on disk. It need not be loaded into memory
in order to access an object inside it, and the objects
in the database are not restricted to being of a single
frequency or range. EViews databases support powerful query
features which can be used to search through the database
for a particular series or select a set of series with a
common property.
Series contained in EViews databases may be accessed and used by
EViews procedures without being fetched into workfiles.
Automatic search capabilities allow you to specify a list
of databases to be searched when a series you need cannot
be found in the current workfile.
Enterprise Edition Support
for ODBC, FAMETM, DRIBase, Haver
Analytics Databases, EcoWin, DataStream, FactSet and Moody's
Economy.com.
As part of the EViews Enterprise
Edition (an extra cost option over EViews Standard Edition),
support is provided for access to data contained in relational
databases (via ODBC drivers) and to databases in a variety
of proprietary formats used by commercial data and database
vendors. Open Database Connectivity (ODBC) is a standard
supported by many relational database systems including
Oracle, Microsoft SQL Server and IBM DB2. EViews allows
you to read or write entire tables from ODBC databases,
or to create a new workfile from the results of a SQL query.
For time series data, EViews also supports access to FAMETM
databases, both local and server based, Global Insight's
DRIBase databases, and Haver Analytics DLX databases. For
time series databases, the same, easy to use, EViews database
interface is available no matter what the source of the
data.
New to version 6, EViews supports
direct access to EcoWin, DataStream, FactSet, and Moody's
Economy.com databases.
Frequency Conversion
When you import data from a database
or from another workfile, they are automatically converted
to the frequency of your current project. EViews has many
options for frequency conversion, and includes support for
the conversion of daily, weekly, or irregular-frequency
data. Series may be assigned a preferred conversion method,
allowing you to use different methods for different series
without having to specify the conversion method every time
a series is accessed. |
Graphics
EViews supports a wide range
of graph types including line graphs, bar graphs, filled
area graphs, pie charts, scatter diagrams, mixed line-bar
graphs, high-low graphs, scatter plots and boxplots. A variety
of options give you control over line types, color, border
characteristics, headings, shading and scaling, including
logarithmic scaling and dual scale graphs. Legends are automatically
created and you can add labels in any scalable Windows font
anywhere on your graph. Any number of graphs can be combined
in a single graph for presentation.
Customizing a graph is as simple
as dragging graphic elements around the screen. Want to
change the characteristics of a legend or a text label?
Just click on it and your options are immediately presented
in easy to understand dialogs.

You can easily incorporate your
customized graphs into other applications using copy-and-paste
or by writing the graph to a file. Formats supported include
Windows metafiles and PostScript files.
Windows On-Line Help
Need help? EViews provides a full
Windows-style help system with index and search capabilities.
In addition, the entire EViews User¡¯s Guide and EViews
Command and Programming Reference are provided in Adobe
PDF format (along with Adobe Acrobat Reader). Both manuals
are extensively hypertext linked, making it easy to find
the information you need. Heavily commented example programs
(and sample data files) are indexed to provide easy access
to an array of expertly written EViews programs.
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A Powerful Programming Language
Point-and-click
is fine, but you feel more comfortable entering commands.
Besides, you need programming tools and capabilities. Well,
EViews is really two programs in one. In addition to its
state-of-the-art windowing interface, EViews includes a
powerful command language that allows access to all menu
items.
Modeled loosely
after the BASIC programming language but with new object-oriented
extensions and matrix handling capabilities. EViews allows
you to enter individual commands for immediate or batch
execution. Your programs can make use of advanced capabilities
such as looping and condition branching, as well as subroutine
and macro processing. Matrix primitives, from simple multiplication
and inversion, to more advanced procedures for Kronecker
products, eigenvector solution, and singular value decomposition,
provide you with the tools you need for solving your most
complex problems.
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| CPU: |
Pentium. |
| Operating
System: |
Windows
98 (SE), Windows NT 4.0, Windows Me, Windows 2000,
Windows XP, Windows Vista. |
| Memory: |
Windows 98, Me or NT 4.0ÀÇ
°æ¿ì 32MB ÀÌ»ó
Windows
2000, Windows XP, Windows VistaÀÇ °æ¿ì 64MBÀÌ»ó |
| Disk
Space: |
200MB
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| Monitor: |
VGA,
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| Hardware: |
CD-ROM. |
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