Parameter space - Revision history

en>EmperorRyker: Grammatical error.

2014-10-06T21:20:29Z

Grammatical error.

← Older revision		Revision as of 23:20, 6 October 2014
Line 1:		Line 1:
	The '~~''Chow test'''~~ is ~~a [[statistical]]~~ and ~~[[econometric]] test of whether the coefficients in two [[linear regression]]s on different data sets are equal. The Chow test was invented by economist [[Gregory Chow]] in 1960~~. ~~In econometrics, the Chow test is most commonly used~~ in ~~[[time series analysis]] to test for the presence of a [[structural break]]. In [[program evaluation]], the Chow test is often used~~ to ~~determine whether the independent variables have different impacts on different subgroups of the population.~~		The author's title is Andera and she believes it sounds fairly great. Her family members lives in Alaska but her spouse desires them to move. He is an order clerk and it's something he truly enjoy. One of the very best things in the globe for him is doing ballet and he'll be starting something else alongside with it.<br><br>Here is my site; [http://chungmuroresidence.com/xe/reservation_branch2/152663 love psychic]

	~~{\| class="wikitable"~~
	\|-
	~~!structural break~~
	~~!program evaluation~~
	\|-
	\|-
	~~\|style="width:450px" valign="top" halign="center" \|~~
	~~[[File:Chow test structural break.png\|440px]]~~
	~~\|style="width:450px" valign="top" halign="center"\|~~
	~~[[File:Chow test substructures.png\|440px]]~~
	\|-
	~~\|valign="top"\|~~
	~~At <math> x=1~~.~~7 </math> there~~ is ~~a structural break, regression on the subintervals <math>[0,1.7]</math>~~ and ~~<math>[1~~.~~7,4] </math> delivers a better modelling than~~ the ~~combined regression(dashed) over~~ the ~~whole interval.~~
	~~\|valign="top"\|~~
	~~Comparison of 2 different programs (red, green) existing in a common data set, separate regressions~~ for ~~both programs deliver a better modelling than a combined regression (black).~~
	\|}

	~~Suppose that we model our data as~~

	~~:<math>~~
	~~y_t=a+bx_{1t} + cx_{2t} + \varepsilon.\,~~
	~~</math>~~

	~~If we split our data into two groups, then we have~~

	~~:<math>~~
	~~y_t=a_1+b_1x_{1t} + c_1x_{2t} + \varepsilon. \,</math>~~

	~~and~~

	~~:<math>~~
	~~y_t=a_2+b_2x_{1t} + c_2x_{2t} + \varepsilon. \,~~
	~~</math>~~

	~~The [[null hypothesis]] of the Chow test asserts that <math>a_1=a_2</math>, <math>b_1=b_2</math>, and <math>c_1=c_2</math>, and there~~ is ~~the assumption that the [[errors~~ and ~~residuals in statistics\|model errors]] <math> \varepsilon </math> are [[independent and identically distributed]] from a [[normal distribution]]~~ with ~~unknown [[variance]].~~

	Let <math>S_C</math> be the sum of squared [[errors and residuals in statistics\|residual]]s from the combined data, <math>S_1</math> be the sum of squared [[errors and residuals in statistics\|residual]]s from the first group, and <math>S_2</math> be the sum of squared [[errors and residuals in statistics\|residual]]s from the second group. ~~<math>N_1</math> and <math>N_2</math> are the number of observations in each group and~~ <~~math~~>k<~~/math~~> is ~~the total number of parameters (in this case, 3). Then the Chow test statistic is~~

	~~:<math>~~
	~~\frac{(S_C -(S_1+S_2))/(k)}{(S_1+S_2)/(N_1+N_2-2k)}.~~
	~~</math>~~

	~~The test statistic follows the [[F distribution]] with <math>k</math> and <math>N_1+N_2-2k</math> [[degrees of freedom (statistics)\|degrees of freedom]].~~

	~~==References==~~
	*{{cite journal \| doi=10.2307/1910133 \| first1=Gregory C.\|last1=Chow \| title=Tests of Equality Between Sets of Coefficients in Two Linear Regressions \| jstor=1910133 \| journal=Econometrica \| year=1960 \| volume=28 \| pages=591–605 \| issue=3}}
	*{{cite book \|last=Doran \|first=Howard E. \|year=1989 \|title=Applied Regression Analysis in Econometrics \|publisher=CRC Press \|isbn=0-8247-8049-3 \|page=146 }} ([http://books.google.de/books?id=JHzik5hkrIMC&pg=PA146&dq=Chow-Test+regression&lr=&as_brr=3&sig=ACfU3U1T_tmXj-sQbqDVHutF3RRPLFQTqg restricted online version (Google Books)])
	*{{cite book \|last=Dougherty \|first=Christopher \|year=2007 \|title=Introduction to Econometrics \|publisher=Oxford University Press \|isbn=0-19-928096-7 \|page=194 }} ([http://books.google.de/books?id=h0MTRO_3jYEC&pg=PA194&vq=chow+test&source=gbs_search_r&cad=1_1&sig=ACfU3U2ZXf7-rPaXyqzwpfAaEDtpqoy5MA restricted online version (Google Books)])

	~~==External links==~~
	*[http://www.stata.com/support/faqs/stat/chow.html Computing the Chow statistic], [http://www.stata.com/support/faqs/stat/chow2.html Chow and Wald tests], [http://www.stata.com/support/faqs/stat/chow3.html Chow tests]: Series of FAQ explanations from the [~~[Stata]] Corporation at~~ http://~~www.stata~~.com/~~support~~/~~faqs~~/

	~~[[Category:Econometrics]]~~
	~~[[Category:Time series analysis]]~~
	~~[[Category:Statistical tests]]~~
	~~[[Category:Regression diagnostics]~~]

en>Rjwilmsi: Journal cites, added 1 DOI using AWB (9904)

2014-02-03T21:54:48Z

Journal cites, added 1 DOI using AWB (9904)

← Older revision		Revision as of 23:54, 3 February 2014
Line 1:		Line 1:
	~~I would like~~ to ~~introduce myself~~ to ~~you, I am Andrew~~ [~~http~~:~~//www~~.~~khuplaza~~.~~com~~/~~dent~~/~~14869889 love psychic]~~ and ~~best psychic readings (~~[~~http:~~/~~/www~~.~~onbizin~~.co.kr/xe/~~?document_srl~~=~~320614 onbizin~~.co.kr]~~) my wife doesn't like it at all~~. ~~It's not a common factor but what I like performing~~ is ~~to climb but I don't have~~ the ~~time lately~~. ~~Ohio~~ is ~~exactly where his home is and his family members enjoys it~~. ~~I am an invoicing officer~~ and ~~I'll be promoted quickly.~~<br><br>~~Here is my blog post~~ ... ~~phone psychic;~~ [http://si.~~dgmensa~~.~~org~~/xe/~~index~~.~~php~~?~~document_srl~~=~~48014~~&~~mid~~=~~c0102 Full Piece~~ of ~~writing~~],		The '''Chow test''' is a [[statistical]] and [[econometric]] test of whether the coefficients in two [[linear regression]]s on different data sets are equal. The Chow test was invented by economist [[Gregory Chow]] in 1960. In econometrics, the Chow test is most commonly used in [[time series analysis]] to test for the presence of a [[structural break]]. In [[program evaluation]], the Chow test is often used to determine whether the independent variables have different impacts on different subgroups of the population.

			{\| class="wikitable"
			\|-
			!structural break
			!program evaluation
			\|-
			\|-
			\|style="width:450px" valign="top" halign="center" \|
			[[File:Chow test structural break.png\|440px]]
			\|style="width:450px" valign="top" halign="center"\|
			[[File:Chow test substructures.png\|440px]]
			\|-
			\|valign="top"\|
			At <math> x=1.7 </math> there is a structural break, regression on the subintervals <math>[0,1.7]</math> and <math>[1.7,4] </math> delivers a better modelling than the combined regression(dashed) over the whole interval.
			\|valign="top"\|
			Comparison of 2 different programs (red, green) existing in a common data set, separate regressions for both programs deliver a better modelling than a combined regression (black).
			\|}

			Suppose that we model our data as

			:<math>
			y_t=a+bx_{1t} + cx_{2t} + \varepsilon.\,
			</math>

			If we split our data into two groups, then we have

			:<math>
			y_t=a_1+b_1x_{1t} + c_1x_{2t} + \varepsilon. \,</math>

			and

			:<math>
			y_t=a_2+b_2x_{1t} + c_2x_{2t} + \varepsilon. \,
			</math>

			The [[null hypothesis]] of the Chow test asserts that <math>a_1=a_2</math>, <math>b_1=b_2</math>, and <math>c_1=c_2</math>, and there is the assumption that the [[errors and residuals in statistics\|model errors]] <math> \varepsilon </math> are [[independent and identically distributed]] from a [[normal distribution]] with unknown [[variance]].

			Let <math>S_C</math> be the sum of squared [[errors and residuals in statistics\|residual]]s from the combined data, <math>S_1</math> be the sum of squared [[errors and residuals in statistics\|residual]]s from the first group, and <math>S_2</math> be the sum of squared [[errors and residuals in statistics\|residual]]s from the second group. <math>N_1</math> and <math>N_2</math> are the number of observations in each group and <math>k</math> is the total number of parameters (in this case, 3). Then the Chow test statistic is

			:<math>
			\frac{(S_C -(S_1+S_2))/(k)}{(S_1+S_2)/(N_1+N_2-2k)}.
			</math>

			The test statistic follows the [[F distribution]] with <math>k</math> and <math>N_1+N_2-2k</math> [[degrees of freedom (statistics)\|degrees of freedom]].

			==References==
			*{{cite journal \| doi=10.2307/1910133 \| first1=Gregory C.\|last1=Chow \| title=Tests of Equality Between Sets of Coefficients in Two Linear Regressions \| jstor=1910133 \| journal=Econometrica \| year=1960 \| volume=28 \| pages=591–605 \| issue=3}}
			*{{cite book \|last=Doran \|first=Howard E. \|year=1989 \|title=Applied Regression Analysis in Econometrics \|publisher=CRC Press \|isbn=0-8247-8049-3 \|page=146 }} ([http://books.google.de/books?id=JHzik5hkrIMC&pg=PA146&dq=Chow-Test+regression&lr=&as_brr=3&sig=ACfU3U1T_tmXj-sQbqDVHutF3RRPLFQTqg restricted online version (Google Books)])
			*{{cite book \|last=Dougherty \|first=Christopher \|year=2007 \|title=Introduction to Econometrics \|publisher=Oxford University Press \|isbn=0-19-928096-7 \|page=194 }} ([http://books.google.de/books?id=h0MTRO_3jYEC&pg=PA194&vq=chow+test&source=gbs_search_r&cad=1_1&sig=ACfU3U2ZXf7-rPaXyqzwpfAaEDtpqoy5MA restricted online version (Google Books)])

			==External links==
			*[http://www.stata.com/support/faqs/stat/chow.html Computing the Chow statistic], [http://www.stata.com/support/faqs/stat/chow2.html Chow and Wald tests], [http://www.stata.com/support/faqs/stat/chow3.html Chow tests]: Series of FAQ explanations from the [[Stata]] Corporation at http://www.stata.com/support/faqs/

			[[Category:Econometrics]]
			[[Category:Time series analysis]]
			[[Category:Statistical tests]]
			[[Category:Regression diagnostics]]

en>Helpful Pixie Bot: ISBNs (Build KE)

2012-05-11T14:50:05Z

ISBNs (Build KE)

New page

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