Essays in Econometrics: Spectral Analysis, Seasonality, Nonlinearity, Methodology, and Forecasting - Vol. 1

Essays in Econometrics: Spectral Analysis, Seasonality, Nonlinearity, Methodology, and Forecasting - Vol. 1

Essays in Econometrics: Spectral Analysis, Seasonality, Nonlinearity, Methodology, and Forecasting - Vol. 1

Essays in Econometrics: Spectral Analysis, Seasonality, Nonlinearity, Methodology, and Forecasting - Vol. 1

Synopsis

Vol. I: This book, and its companion volume, present a collection of papers by Clive W.J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in spectral analysis, seasonality, nonlinearity, methodology, and forecasting. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors. Vol. II: This book, and its companion volume in the Econometric Society Monographs series (ESM number 32), present a collection of papers by Clive W.J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in causality, integration and cointegration, and long memory. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

Excerpt

At the beginning of the twentieth century, there was very little fundamental theory of time series analysis and surely very few economic time series data. Autoregressive models and moving average models were introduced more or less simultaneously and independently by the British statistician Yule (1921, 1926, 1927) and the Russian statistician Slutsky (1927). the mathematical foundations of stationary stochastic processes were developed by Wold (1938), Kolmogorov (1933, 1941a, 1941b), Khintchine (1934), and Mann and Wald (1943).Thus, modern time series analysis is a mere eight decades old. Clive W. J. Granger has been working in the field for nearly half of its young life. His ideas and insights have had a fundamental impact on statistics, econometrics, and dynamic economic theory.

Granger summarized his research activity in a recent et Interview (Phillips 1997), which appears as the first reprint in this volume, by saying, “I plant a lot of seeds, a few of them come up, and most of them do not.” Many of the seeds that he planted now stand tall and majestic like the Torrey Pines along the California coastline just north of the University of California, San Diego, campus in La Jolla, where he has been an economics faculty member since 1974. Phillips notes in the et Interview that “It is now virtually impossible to do empirical work in time series econometrics without using some of his [Granger's] methods or being influenced by some of his ideas.” Indeed, applied time series econometricians come across at least one of his path-breaking ideas almost on a daily basis. For example, many of his contributions in the areas of spectral analysis, long memory, causality, forecasting, spurious regression, and cointegration are seminal. His influence on the profession continues with no apparent signs of abatement.

Spectral methods

In his et Interview, Granger explains that early in his career he was confronted with many applied statistical issues from various disciplines . . .

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