Imposing cointegration on a forecasting system, if cointegration is present, is believed to improve long-horizon forecasts. Contrary to this belief, at long horizons nothing is lost by ignoring cointegration when the forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. Our results highlight a potentially important deficiency of standard forecast accuracy measures—they fail to value the maintenance of cointegrating relationships among variables—and we suggest alternatives that explicitly do so.
Imposing cointegration on a forecasting system, if cointegration is present, is believed to improve long-horizon forecasts. Contrary to this belief, at long horizons nothing is lost by ignoring cointegration when the forecasts are evaluated using standard mutivariate forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. Our results highlight a potentially important deficiency of standard forecast accuracy measures-they fail to value the maintenance of cointegrating relationships among variables-and we suggest alternatives tht explicitly do so.
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.
Research activity in the IMF emphasizes the links between the organization's policy and operational concerns. The main objectives of research is IMF staff understanding of policy and operational issues relevant to the institution, and to improve the analytical quality of the work prepared for management and the Executive Board and the advice provided to member countries. The scope of research in the IMF is defined by the purposes and functions of the institution. In order to foster innovation and ensure quality control, the IMF makes much of its research available outside the institution and encourages staff to interact with academia and other research organizations through conferences, seminars, and occasional joint research projects. The visiting scholar’s program has also enhanced the quality of research done in the IMF. This program brings in leading members of the economics profession from around the world to assist in the preparation of papers for the Executive Board and to conduct research on IMF-related issues.
The time-series properties of real exchange rates, on a number of definitions, for 22 industrial countries during 1979-95 were used to re-examine whether PPP holds. It is shown that if real exchange rates reverted to a constant mean slowly, say by five percent a month, then at standard levels of significance we should expect 11 of the 22 series examined to yield evidence of mean reversion and to reject that hypothesis of a unit root. Using models that imply a constant unconditional mean or trend-stationary productivity changes, we find that only one of the 22 real exchange rates shows evidence against unit roots. This low rate of rejection of unit roots in real exchange rates can be construed as evidence against PPP.
Section headings in this handbook include: 'Forecasting Methodology; 'Forecasting Models'; 'Forecasting with Different Data Structures'; and 'Applications of Forecasting Methods.'.
This text on economic forecasting asks why some practices seem to work empirically despite a lack of formal support from theory. After reviewing the conventional approach to forecasting, it looks at the implications for causal modelling, presents forecast errors and delineates sources of failure.
