Estimation Inference Specification by White (38 results)

hardcover. Condition: Very Good. number 22 of Econometric Society Monographs With very good dust jacket. Very Good hardcover with light shelfwear - NICE! Standard-sized.

Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Paperback. Condition: Very Good. No Jacket. Former library book; May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.15.

Condition: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.

Hardcover. Condition: Good. No Jacket. Condition : Good. Former-university library copy with associated library stamps etc. Hard cover, no jacket. 380pp. No highlighting or annotations to text. Please note the image is a stock photo - not the actual copy.

Condition: New.

Condition: New. SUPER FAST SHIPPING.

Condition: Good. Volume 22. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. 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,600grams, ISBN:0521574463.

Condition: New.

Paperback. Condition: new. Paperback. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New. In.

Paperback. Condition: New.

23 cm. original cloth, dustjacket. x,380 pp. references. index. "Econometric Society Monographs". -(very) good. 655g.

Condition: New. pp. 396.

Paperback. Condition: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.

Hardcover. Condition: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.

Paperback. Condition: new. Paperback. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: Brand New. reprint edition. 392 pages. 9.00x6.25x0.75 inches. In Stock.

Paperback. Condition: new. Paperback. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condition: New. SUPER FAST SHIPPING.

Condition: New.

Condition: New. In.

Condition: New.

Hardcover. Condition: new. Hardcover. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

hardcover. Condition: New. In shrink wrap. Looks like an interesting title!

hardcover. Condition: New. In shrink wrap. Looks like an interesting title!

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book examines the consequences of misspecifications from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference.

Hardcover. Condition: new. Hardcover. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condition: new. Hardcover. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models and gives the conditions under which parameters of interest can be consistently estimated despite misspecification and the consequences of misspecification for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.