Journal ArticleDOI
An Application of Vector Time Series Techniques to Macroeconomic Forecasting
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TLDR
A wide variety of time series techniques are now used for generating forecasts of economic variables, with each technique attempting to summarize and exploit whatever regularities exist in a given data set.Abstract:
A wide variety of time series techniques are now used for generating forecasts of economic variables, with each technique attempting to summarize and exploit whatever regularities exist in a given data set. It appears that many researchers arbitrarily choose one of these techniques. The purpose of this article is to provide an example for which the choice of time series technique appears important; merely choosing arbitrarily among available techniques may lead to suboptimal results.read more
Citations
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Journal ArticleDOI
The sensitivity of VAR forecasts to alternative lag structures
Rik Hafer,Richard G. Sheehan +1 more
TL;DR: In this article, the sensitivity of forecasts from a VAR model using different lag structures is examined, using simple ad hoc rules as well as statistical criteria, such as mean square error and Bayesian rules, and the results indicate that the accuracy of VAR forecasts varies dramatically across alternative lag structures.
Journal ArticleDOI
The impact of the hotel room tax: an interrupted time series approach.
TL;DR: In this paper, the authors employ interrupted time series analysis to estimate the impact of hotel room tax on real net hotel revenue in New York City, showing that New York had the highest hotel room occupancy tax in the U.S. at 19.25 percent plus $2.
Posted Content
Specifying vector autoregressions for macroeconomic forecasting
TL;DR: A quasi-Bayesian approach is viewed as a flexible tool for constructing a filter which optimally extracts information about the future from a set of macroeconomic data for forecasting macroeconomic variables.
Journal ArticleDOI
Asymptotic and bootstrap prediction regions for vector autoregression
TL;DR: In this paper, small sample properties of asymptotic and bootstrap prediction regions for VAR models are evaluated and compared and Monte Carlo simulations reveal that the bootstrap region based on the percentile- t method outperforms its asymptic and other bootstrap alternatives in small samples.
Journal ArticleDOI
Modeling and forecasting hospital patient movements: Univariate and multiple time series approaches
TL;DR: In this article, the authors proposed to model and forecast monthly patient movements in individual hospitals by means of the Box-Jenkins univariate and Tiao-Box multiple time series approaches; and they tried to determine the choice between the univariate method and the multivariate approach in the case of hospital patient data.
References
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Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
Journal ArticleDOI
Macroeconomics and reality
TL;DR: In this article, the authors argue that the style in which their builders construct claims for a connection between these models and reality is inappropriate, to the point at which claims for identification in these models cannot be taken seriously.
ReportDOI
Forecasting and conditional projection using realistic prior distributions
TL;DR: This paper developed a forecasting procedure based on a Bayesian method for estimating vector autoregressions, which is applied to 10 macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations.
Journal ArticleDOI
Autoregressive modelling and money-income causality detection
TL;DR: In this paper, a step-wise procedure based on Granger's concept of causality and Abaike's final prediction error criterion is suggested as a practical means to identify the order of lags of each variable in a multivariate autoregressive process.