Nonlinear Methods Econometrics (24 results)

Hardcover. Condition: Good. No Jacket. Missing dust jacket; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Condition: good. A good reading copy. May contain markings or be a withdrawn library copy.

Hardcover. Condition: Bon. Ancien livre de bibliothèque. Traces d'usure sur la couverture. Edition 1972. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Signs of wear on the cover. Edition 1972. Ammareal gives back up to 15% of this item's net price to charity organizations.

Hardcover. Condition: Good. No Jacket. 1st Edition. Condition : Good. Hard cover, no jacket. Former university library copy with associated markings. 280pp. No highlighting or annotations to text. Pages age toned. Covered in non removable protective laminate. Photo on request.

Condition: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Clean from markings. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:

Gebundene Ausgabe. Condition: Gut. 292 Seiten ex Library Book aus einer wissenschafltichen Bibliothek Sprache: Englisch Gewicht in Gramm: 550.

Condition: Good. Volume 192. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. Clean from markings. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,400grams, ISBN:3540108386.

Gebundene Ausgabe. Condition: Gut. 292 Seiten Exemplar aus einer wissenchaftlichen Bibliothek Sprache: Englisch Gewicht in Gramm: 550.

23 x 16. 280 Seiten. Hardcover. Ordnungsgemäß aus einer Universitäts-Bibliothek ausgesondert (Stempel, Rückenschild). Gut erhaltenes Exemplar. Sprache: Englisch Gewicht in Gramm: 800.

Softcover. Condition: Sehr gut. Berlin, Springer 1981 gr.8°. 198 p. Pbck. Lecture Notes in Economics and Mathematical Systems, 192.- Throughout slightly browned.

Condition: Fair. Volume 77. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Clean from markings. In fair condition, suitable as a study copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,650grams, ISBN:

Bierens, Herman J., 1943-. Robust methods and asymptotic theory in nonlinear econometrics. Berlin ; New York: Springer-Verlag, 1981, ix, 198pp., PAPERBACK, very good BUT with a library gift donation stamp inside front cover and previous owner's initials on cover, but book was never part of the library, just a gift. Lecture notes in economics and mathematical systems, 192. 9783540108382 ISBN 0387108386.

Condition: New. In.

Paperback. Condition: New.

Condition: New. pp. 216.

Paperback. Condition: Brand New. 207 pages. German language. 9.53x6.54x0.55 inches. In Stock.

Hardcover. Condition: Gut. 280 S. Economics Numerical Optimization Least Squares Theory Confidence Interval Maximum Likelhood Estimation Heteroscedasticity Cobb- Douglas Type Function Guter Zustand/ Good Ex-Library. With figures. Cover shows mild wear. ha1062947 Sprache: Englisch Gewicht in Gramm: 640.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the data. The estimation methods involved are nonlinear least squares estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non linear weighted robust M-estimation (NLWRME) for the regression case and nonlinear two-stage least squares estimation (NL2SLSE) and a new method called minimum information estimation (MIE) for the case of structural equations. The asymptotic properties of the NLLSE and the two robust M-estimation methods are derived from further elaborations of results of Jennrich. Special attention is payed to the comparison of the asymptotic efficiency of NLLSE and NLRME. It is shown that if the tails of the error distribution are fatter than those of the normal distribution NLRME is more efficient than NLLSE. The NLWRME method is appropriate if the distributions of both the errors and the regressors have fat tails. This study also improves and extends the NL2SLSE theory of Amemiya. The method involved is a variant of the instrumental variables method, requiring at least as many instrumental variables as parameters to be estimated. The new MIE method requires less instrumental variables. Asymptotic normality can be derived by employing only one instrumental variable and consistency can even be proved with out using any instrumental variables at all.

Condition: Gut. Zustand: Gut | Seiten: 292 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the data. The estimation methods involved are nonlinear least squares estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non linear weighted robust M-estimation (NLWRME) for the regression case and nonlinear two-stage least squares estimation (NL2SLSE) and a new method called minimum information estimation (MIE) for the case of structural equations. The asymptotic properties of the NLLSE and the two robust M-estimation methods are derived from further elaborations of results of Jennrich. Special attention is payed to the comparison of the asymptotic efficiency of NLLSE and NLRME. It is shown that if the tails of the error distribution are fatter than those of the normal distribution NLRME is more efficient than NLLSE. The NLWRME method is appropriate if the distributions of both the errors and the regressors have fat tails. This study also improves and extends the NL2SLSE theory of Amemiya. The method involved is a variant of the instrumental variables method, requiring at least as many instrumental variables as parameters to be estimated. The new MIE method requires less instrumental variables. Asymptotic normality can be derived by employing only one instrumental variable and consistency can even be proved with out using any instrumental variables at all. 216 pp. Englisch.

Condition: New. Print on Demand pp. 216 67:B&W 6.69 x 9.61 in or 244 x 170 mm (Pinched Crown) Perfect Bound on White w/Gloss Lam.

Condition: New. PRINT ON DEMAND pp. 216.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the dat.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the data. The estimation methods involved are nonlinear least squares estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non linear weighted robust M-estimation (NLWRME) for the regression case and nonlinear two-stage least squares estimation (NL2SLSE) and a new method called minimum information estimation (MIE) for the case of structural equations. The asymptotic properties of the NLLSE and the two robust M-estimation methods are derived from further elaborations of results of Jennrich. Special attention is payed to the comparison of the asymptotic efficiency of NLLSE and NLRME. It is shown that if the tails of the error distribution are fatter than those of the normal distribution NLRME is more efficient than NLLSE. The NLWRME method is appropriate if the distributions of both the errors and the regressors have fat tails. This study also improves and extends the NL2SLSE theory of Amemiya. The method involved is a variant of the instrumental variables method, requiring at least as many instrumental variables as parameters to be estimated. The new MIE method requires less instrumental variables. Asymptotic normality can be derived by employing only one instrumental variable and consistency can even be proved with out using any instrumental variables at all.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 216 pp. Englisch.