Isbn: 9781412906562 - multiple time series models: 148 (quantitative applications in the social sciences) (20 resultados)

Condición: Very Good. 1st Edition. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good. …

Paperback. Condición: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Paperback. Condición: Very Good. Reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. This book focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. It also reviews arguments for and against using multi-equation time series models. …

Paperback. Condición: Very Good. Reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. This book focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. It also reviews arguments for and against using multi-equation time series models. …

Condición: New. pp. 120 Index 1st Edition.

Paperback. Condición: New. 1st. Many analyses of time series data involve multiple, related variables.  Multiple Time Series Models presents many specification choices and special challenges.  This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression.  The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned.  Specification, estimation, and inference using these models is discussed.  The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available.Key FeaturesOffers a detailed comparison of different time series methods and approaches. Includes a self-contained introduction to vector autoregression modeling. Situates multiple time series modeling as a natural extension of commonly taught statistical models. …

Condición: New. pp. 120.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Paperback. Condición: new. New Copy. Customer Service Guaranteed.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: Brand New. 1st edition. 120 pages. 8.50x5.50x0.25 inches. In Stock.

paperback. Condición: New. In shrink wrap. Looks like an interesting title.

Paperback. Condición: New. 1st. Many analyses of time series data involve multiple, related variables.  Multiple Time Series Models presents many specification choices and special challenges.  This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression.  The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned.  Specification, estimation, and inference using these models is discussed.  The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available.Key FeaturesOffers a detailed comparison of different time series methods and approaches. Includes a self-contained introduction to vector autoregression modeling. Situates multiple time series modeling as a natural extension of commonly taught statistical models. …

Condición: New. pp. 120 Illus. This item is printed on demand.

Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many analyses of time series data involve multiple, related variables. Multiple Time Series Models presents many specification choices and special challenges. This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression.The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. Specification, estimation, and inference using these modelsis discussed. The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available.Key Features- Offers a detailed comparison of different time series methods and approaches. - Includes a self-contained introduction to vector autoregression modeling. - Situates multiple time series modeling as a natural extension of commonly taught statistical models.…

Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. This book focuses on vector autoregression (VAR) models as a generalization of the other approaches me.…