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Author: Evan F. Koenig Publisher: ISBN: Category : Economic forecasting Languages : en Pages : 44
Book Description
We distinguish between three different ways of using real-time data to estimate forecasting equations and argue that the most frequently used approach should generally be avoided. The point is illustrated with a model that uses monthly observations of industrial production, employment, and retail sales to predict real GDP growth. When the model is estimated using our preferred method, its out-of-sample forecasting performance is clearly superior to that obtained using conventional estimation, and compares favorably with that of the Blue-Chip consensus.
Author: Evan F. Koenig Publisher: ISBN: Category : Economic forecasting Languages : en Pages : 44
Book Description
We distinguish between three different ways of using real-time data to estimate forecasting equations and argue that the most frequently used approach should generally be avoided. The point is illustrated with a model that uses monthly observations of industrial production, employment, and retail sales to predict real GDP growth. When the model is estimated using our preferred method, its out-of-sample forecasting performance is clearly superior to that obtained using conventional estimation, and compares favorably with that of the Blue-Chip consensus.
Author: Publisher: ISBN: Category : Languages : en Pages :
Book Description
The U.S. Federal Reserve Board presents the full text of an article entitled "The Use and Abuse of Real-Time Data in Economic Forecasting," by Evan F. Koenig, Sheila Dolmas, and Jeremy Piger. The article discusses different ways of using real-time data to estimate forecasting equations.
Author: Eric Ghysels Publisher: Oxford University Press ISBN: 0190622032 Category : Business & Economics Languages : en Pages : 608
Book Description
Economic forecasting is a key ingredient of decision making both in the public and in the private sector. Because economic outcomes are the result of a vast, complex, dynamic and stochastic system, forecasting is very difficult and forecast errors are unavoidable. Because forecast precision and reliability can be enhanced by the use of proper econometric models and methods, this innovative book provides an overview of both theory and applications. Undergraduate and graduate students learning basic and advanced forecasting techniques will be able to build from strong foundations, and researchers in public and private institutions will have access to the most recent tools and insights. Readers will gain from the frequent examples that enhance understanding of how to apply techniques, first by using stylized settings and then by real data applications--focusing on macroeconomic and financial topics. This is first and foremost a book aimed at applying time series methods to solve real-world forecasting problems. Applied Economic Forecasting using Time Series Methods starts with a brief review of basic regression analysis with a focus on specific regression topics relevant for forecasting, such as model specification errors, dynamic models and their predictive properties as well as forecast evaluation and combination. Several chapters cover univariate time series models, vector autoregressive models, cointegration and error correction models, and Bayesian methods for estimating vector autoregressive models. A collection of special topics chapters study Threshold and Smooth Transition Autoregressive (TAR and STAR) models, Markov switching regime models, state space models and the Kalman filter, mixed frequency data models, nowcasting, forecasting using large datasets and, finally, volatility models. There are plenty of practical applications in the book and both EViews and R code are available online at authors' website.
Author: Haim Y. Bleikh Publisher: CRC Press ISBN: 1317010175 Category : Business & Economics Languages : en Pages : 148
Book Description
In Time Series Analysis and Adjustment the authors explain how the last four decades have brought dramatic changes in the way researchers analyze economic and financial data on behalf of economic and financial institutions and provide statistics to whomsoever requires them. Such analysis has long involved what is known as econometrics, but time series analysis is a different approach driven more by data than economic theory and focused on modelling. An understanding of time series and the application and understanding of related time series adjustment procedures is essential in areas such as risk management, business cycle analysis, and forecasting. Dealing with economic data involves grappling with things like varying numbers of working and trading days in different months and movable national holidays. Special attention has to be given to such things. However, the main problem in time series analysis is randomness. In real-life, data patterns are usually unclear, and the challenge is to uncover hidden patterns in the data and then to generate accurate forecasts. The case studies in this book demonstrate that time series adjustment methods can be efficaciously applied and utilized, for both analysis and forecasting, but they must be used in the context of reasoned statistical and economic judgment. The authors believe this is the first published study to really deal with this issue of context.
Author: Michael P. Clements Publisher: OUP USA ISBN: 0195398645 Category : Business & Economics Languages : en Pages : 732
Book Description
Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Author: Graham Elliott Publisher: Elsevier ISBN: 0444627413 Category : Business & Economics Languages : en Pages : 1386
Book Description
The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. Focuses on innovation in economic forecasting via industry applications Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications Makes details about economic forecasting accessible to scholars in fields outside economics
Author: Andrés Fernández Martin Publisher: ISBN: Category : Languages : en Pages :
Book Description
In this paper, we empirically assess the extent to which early release inefficiency and definitional change affect prediction precision. In particular, we carry out a series of ex-ante prediction experiments in order to examine: the marginal predictive content of the revision process, the trade-offs associated with predicting different releases of a variable, the importance of particular forms of definitional change which we call "definitional breaks", and the rationality of early releases of economic variables. An important feature of our rationality tests is that they are based solely on the examination of ex-ante predictions, rather than being based on in-sample regression analysis, as are many tests in the extant literature. Our findings point to the importance of making real-time datasets available to forecasters, as the revision process has marginal predictive content, and because predictive accuracy increases when multiple releases of data are used when specifying and estimating prediction models. We also present new evidence that early releases of money are rational, whereas prices and output are irrational. Moreover, we find that regardless of which release of our price variable one specifies as the "target" variable to be predicted, using only "first release" data in model estimation and prediction construction yields mean square forecast error (MSFE) "best" predictions. On the other hand, models estimated and implemented using "latest available release & " data are MSFE-best for predicting all releases of money. We argue that these contradictory finding are due to the relevance of definitional breaks in the data generating processes of the variables that we examine. In an empirical analysis, we examine the real-time predictive content of money for income, and we find that vector autoregressions with money do not perform significantly worse than autoregressions, when predicting output during the last 20 years. -- bias ; efficiency ; generically comprehensive tests ; rationality ; preliminary ; final ; real-time data
Author: Evan F. Koenig
Author: Evan F. Koenig
Author: 
Author: Eric Ghysels
Author: Tom Whiston
Author: Haim Y. Bleikh
Author: Michael P. Clements
Author: Thomas C. Stark
Author: Giles Keating
Author: Graham Elliott
Author: Andrés Fernández Martin