1.

Record Nr.

UNINA9910828315103321

Titolo

Business cycles, indicators, and forecasting / / edited by James H. Stock and Mark W. Watson

Pubbl/distr/stampa

Chicago, : University of Chicago Press, c1993

ISBN

1-281-43112-5

9786611431129

0-226-77474-0

Edizione

[1st ed.]

Descrizione fisica

1 online resource (350 p.)

Collana

Studies in business cycles ; ; v. 28

Altri autori (Persone)

StockJames H

WatsonMark W

Disciplina

338.5/42

Soggetti

Economic forecasting

Economic indicators

Business cycles

Economic forecasting - United States

Economic indicators - United States

Business cycles - United States

United States Economic conditions Congresses

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and indexes.

Nota di contenuto

Front matter -- Relation of the Directors to the Work and Publications of the National Bureau of Economic Research -- Contents -- Acknowledgments -- Introduction -- 1. Twenty-two Years of the NBERASA Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance -- 2. A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience -- 3. Estimating Event Probabilities from Macroeconometric Models Using Stochastic Simulation -- 4. A Nine-Variable Probabilistic Macroeconomic Forecasting Model -- 5. Why Does the Paper-Bill Spread Predict Real Economic Activity? -- 6. Further Evidence on Business- Cycle Duration Dependence -- 7. A Dynamic Index Model for Large Cross Sections -- 8. Modeling Nonlinearity over the Business Cycle -- Contributors -- Author Index -- Subject Index



Sommario/riassunto

The inability of forecasters to predict accurately the 1990-1991 recession emphasizes the need for better ways for charting the course of the economy. In this volume, leading economists examine forecasting techniques developed over the past ten years, compare their performance to traditional econometric models, and discuss new methods for forecasting and time series analysis.