A C Harvey
Biographic Data
| ID | 5729652 |
|---|---|
| NAME | A C Harvey |
| GIVEN NAMES | A C |
| FAMILY NAME | Harvey |
| SIGNATURE | HARVEY A C |
| VERIFIED | No |
| TOTAL WORKS | 16 |
| TOTAL CITATIONS | 5 |
| AUTHOR COUNT | 16 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1976 |
| LATEST PUBLICATION YEAR | 1993 |
| H-INDEX | 1 |
Detrending, stylized facts and the business cycle
The stylized facts of macroeconomic time series can be presented by fitting structural time series models. Within this framework, we analyse the consequences of the widely used detrending technique popularised by Hodrick and Prescott (1980). It is shown that mechanical detrending based on the Hodrick–Prescott filter can lead investigators to report spurious cyclical behaviour, and this point is illustrated with empirical examples. Structural time…
[Time Series Models for Count or Qualitative Observations]
Time Series Models for Count or Qualitative Observations
Time series sometimes consist of count data in which the number of events occurring in a given time interval is recorded. Such data are necessarily nonnegative integers, and an assumption of a Poisson or negative binomial distribution is often appropriate. This article sets ups a model in which the level of the process generating the observations changes over time. A recursion analogous to the Kalman filter is used to construct the likelihood fun…
Advanced Econometrics
Stochastic Trends in Dynamic Regression Models
Journal Article Stochastic Trends in Dynamic Regression Models: An Application to the Employment-Output Equation Get access A. C. Harvey, A. C. Harvey London School of Economics and National Institute of Economic and Social Research Search for other works by this author on: Oxford Academic Google Scholar S. G. B. Henry, S. G. B. Henry London School of Economics and National Institute of Economic and Social Research Search for other works by this …
Trends and Cycles in Macroeconomic Time Series
Two structural time series models for annual observations are constructed in terms of trend, cycle, and irregular components. The models are then estimated via the Kalman filter using data on five U.S. macroeconomic time series. The results provide some interesting insights into the dynamic structure of the series, particularly with respect to cyclical behavior. At the same time, they illustrate the development of a model selection strategy for s…
An Introduction to National Income Accounting
Econometrics
Forecasting Economic Time Series With Structural and Box-Jenkins Models
The basic structural model is a univariate time series model consisting of a slowly changing trend component, a slowly changing seasonal component, and a random irregular component. It is part of a class of models that have a number of advantages over the seasonal ARIMA models adopted by Box and Jenkins (1976). This article reports the results of an exercise in which the basic structural model was estimated for six U.K. macroeconomic time series …
[Forecasting Economic Time Series with Structural and Box-Jenkins Models
A. C. Harvey, P. H. J. Todd, [Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study]: Response, Journal of Business & Economic Statistics, Vol. 1, No. 4 (Oct., 1983), pp. 313-315
Time Series Models
The Econometric Analysis of Time Series
Journal Article The Econometric Analysis of Time Series Get access The Econometric Analysis of Time Series. By A. C. Harvey. (Oxford: Philip Allan, 1981. Pp. xi + 384. £17.50.) Grayham E. Mizon Grayham E. Mizon University of Southampton Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 93, Issue 369, 1 March 1983, Pages 254–257, https://doi.org/10.2307/2232203 Published: 01 March 1983
The econometric analysis of time series
The Algebra of Econometrics
Comparison of Box-Jenkins and Bonn Monetary Model Prediction Performance
Estimating Regression Models with Multiplicative Heteroscedasticity
The Econometric Analysis of Time Series
Journal Article The Econometric Analysis of Time Series Get access The Econometric Analysis of Time Series. By A. C. Harvey. (Oxford: Philip Allan, 1981. Pp. xi + 384. £17.50.) Grayham E. Mizon Grayham E. Mizon University of Southampton Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 93, Issue 369, 1 March 1983, Pages 254–257, https://doi.org/10.2307/2232203 Published: 01 March 1983
Estimating Regression Models with Multiplicative Heteroscedasticity
The Algebra of Econometrics
Comparison of Box-Jenkins and Bonn Monetary Model Prediction Performance
The econometric analysis of time series
Forecasting Economic Time Series With Structural and Box-Jenkins Models
The basic structural model is a univariate time series model consisting of a slowly changing trend component, a slowly changing seasonal component, and a random irregular component. It is part of a class of models that have a number of advantages over the seasonal ARIMA models adopted by Box and Jenkins (1976). This article reports the results of an exercise in which the basic structural model was estimated for six U.K. macroeconomic time series …
[Forecasting Economic Time Series with Structural and Box-Jenkins Models
A. C. Harvey, P. H. J. Todd, [Forecasting Economic Time Series with Structural and Box-Jenkins Models: A Case Study]: Response, Journal of Business & Economic Statistics, Vol. 1, No. 4 (Oct., 1983), pp. 313-315
Time Series Models
The Econometric Analysis of Time Series
Journal Article The Econometric Analysis of Time Series Get access The Econometric Analysis of Time Series. By A. C. Harvey. (Oxford: Philip Allan, 1981. Pp. xi + 384. £17.50.) Grayham E. Mizon Grayham E. Mizon University of Southampton Search for other works by this author on: Oxford Academic Google Scholar The Economic Journal, Volume 93, Issue 369, 1 March 1983, Pages 254–257, https://doi.org/10.2307/2232203 Published: 01 March 1983
Trends and Cycles in Macroeconomic Time Series
Two structural time series models for annual observations are constructed in terms of trend, cycle, and irregular components. The models are then estimated via the Kalman filter using data on five U.S. macroeconomic time series. The results provide some interesting insights into the dynamic structure of the series, particularly with respect to cyclical behavior. At the same time, they illustrate the development of a model selection strategy for s…
An Introduction to National Income Accounting
Econometrics
Stochastic Trends in Dynamic Regression Models
Journal Article Stochastic Trends in Dynamic Regression Models: An Application to the Employment-Output Equation Get access A. C. Harvey, A. C. Harvey London School of Economics and National Institute of Economic and Social Research Search for other works by this author on: Oxford Academic Google Scholar S. G. B. Henry, S. G. B. Henry London School of Economics and National Institute of Economic and Social Research Search for other works by this …
Advanced Econometrics
[Time Series Models for Count or Qualitative Observations]
Time Series Models for Count or Qualitative Observations
Time series sometimes consist of count data in which the number of events occurring in a given time interval is recorded. Such data are necessarily nonnegative integers, and an assumption of a Poisson or negative binomial distribution is often appropriate. This article sets ups a model in which the level of the process generating the observations changes over time. A recursion analogous to the Kalman filter is used to construct the likelihood fun…
Detrending, stylized facts and the business cycle
The stylized facts of macroeconomic time series can be presented by fitting structural time series models. Within this framework, we analyse the consequences of the widely used detrending technique popularised by Hodrick and Prescott (1980). It is shown that mechanical detrending based on the Hodrick–Prescott filter can lead investigators to report spurious cyclical behaviour, and this point is illustrated with empirical examples. Structural time…
Econometrics (13 works) · Economics (11 works) · Mathematics (9 works) · Statistics (7 works) · Time series (7 works) · Computer Science (6 works) · Autoregressive integrated moving average (4 works) · Monetary Policy and Economic Impact (4 works) · Box–Jenkins (3 works) · Complex Systems and Time Series Analysis (3 works)