ECONOMIC FORECASTING (original) (raw)
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Forecasting in cointegrated systems
Journal of Applied Econometrics, 1995
We consider the implications for forecast accuracy of imposing unit roots and cointegrating restrictions in linear systems of I( 1) variables in levels, differences, and cointegrated combinations. Asymptotic formulae are obtained for multi-step forecast error variances for each representation. Alternative measures of forecast accuracy are discussed. Finite sample behaviour in a bivariate model is studied by Monte Carlo using control variables. We also analyse the interaction between unit roots and cointegrating restrictions and intercepts in the DGP. Some of the issues are illustrated with an empirical example of forecasting the demand for M1 in the UK.
Are Macroeconomic Forecasts Informative? Cointegration Evidence from the ASA-NBER Surveys
1999
3 Other papers directly addressing this issue of integration and cointegration include Fischer (1989) for M1, and Lahiri and Chun (1989) for GNP, the price deflator and the unemployment 3 2 STUDIES OF FORECAST RATIONALITY 2.1 Previous Literature Survey data are generally viewed with suspicion by economists, even more so as the rational expectations approach to macroeconomics has come to dominate economic discourse. The skepticism is borne of desire to infer preferences from actions, rather than statements. Unfortunately, as most glaringly made obvious by the exchange rate literature, rational expectations measures of expectations have their own limitations (see Froot and Thaler, 1990). Consequently, macroeconomists have long resorted to various survey measures such as the Livingstone survey of inflationary expectations. Several recent studies have found that survey data do contain useful information about future events. 2 Typically, in assessing the rationality of these survey-based forecasts, the usual metrics have been used-mean error, root mean squared error, and mean absolute error. A good example of this approach is Zarnowitz and Braun (1993). Recently, Aggarwal, Mohanty and Song (1995) have assessed the unbiasedness and integration and cointegration characteristics of macroeconomic data and their respective forecasts, published by Money Market Services (MMS). However, they-like Liu and Maddala (1992) in their examination of MMS exchange rate forecasts-impose a unitary elasticity of forecasts with respect to actual series; in contrast, our modeling approach is more flexible, allowing for cointegration without necessarily imposing these constraints. Hence we can test for whether the restriction is rejected or not. 3 Moreover, all of these previous studies use inefficient tests for either rate. In their paper, Lahiri and Chun test for cointegration with unitary elasticity restrictions using a less efficient ADF test on a constructed regressor. 4 integration or cointegration, or for both. In contrast, we apply more powerful unit root tests corrected for small sample effects, and the Johansen and Juselius (1990) multivariate cointegration testing procedure which is more efficient than other testing procedures. In assessing the forecast characteristics, we also carefully distinguish between the initial unrevised series, and the subsequently revised, final series. Presumably, the forecasters are attempting to predict the former. Hence, comparisons of forecasts and actual revised series, as in many previous studies, is unlikely to provide an accurate picture of forecast rationality. 2.2 The ASA-NBER Survey of Forecasters Since 1968, the American Statistical Association (ASA) and the National Bureau of Economic Research (NBER) have jointly undertaken a survey of macroeconomic forecasters. Over time, the series surveyed as well as the respondents, have varied. However, taken together, these forecasts comprise the most extensive and longest uninterrupted set of series available. A detailed discussion of the coverage and characteristics of the ASA-NBER forecast surveys is provided by Zarnowitz and Braun (1993). The ASA-NBER database includes forecasts of industrial production, the GNP deflator, real GNP, housing starts, the CPI inflation rate, the 3 month treasury bill rate, the yield on corporate bonds, nominal after tax corporate profits, and the rate of unemployment. We obtained the median forecasts and the actual unrevised series from CITIBASE. The actual revised, or official, series are also retrieved from the same source. In general, the data begin in 1970 at the earliest, and in 1981 for several of the series. The data appendix contains detailed
ESSAYS IN FORECASTING ABSTRACT OF THE DISSERTATION Essays in Forecasting
2009
This dissertation comprises three essays in macroeconomic forecasting. The first essay discusses model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. Particular emphasis is placed on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error on the class of test statistics with limiting distributions that are functionals of Gaussian processes. Results of an empirical investigation of the marginal predictive content of money for income are also presented. The second essay outlines a number of approaches to the selection of factor proxies (ob-served variables that proxy unobserved estimated factors) using statistics based on large sample datasets. This approach to factor proxy selection is examined via a small Monte Carlo experiment and a set of prediction experiments, where evidence supporting our proposed methodology is presented. The third essay co...
Economic forecasting: editors’ introduction
Empirical Economics
The 2-day workshop, organized by Robert M. Kunst and Martin Wagner, drew much more attention than originally expected. This reflects the increased-respectively regained-importance of forecasting not only in practical terms but also as a research topic in the underlying scientific disciplines. Much of this growing interest may be also rooted in increased importance of forecasting in fields such as management science, marketing or supply chain management and may well be driven by methodological developments rooted in several disciplines that could be summarized under labels such as big data, machine learning and the like; the workshop itself had a narrower focus on macroeconomic forecasting. Even with the specific focus on macroeconomics, the papers span a wide portfolio of approaches and applications, ranging from statistical theory to data-driven research. As indicated in the beginning, some of the contributions in this special volume are not related to the workshop, as submission of manuscripts was open.
Special Issue on Economic Forecasts: Guest Editorial
Jahrbucher Fur Nationalokonomie Und Statistik, 2011
Forecasts guide decisions in all areas of economics and finance. Economic policy makers base their decisions on business cycle forecasts, investment decisions of firms are based on demand forecasts, and portfolio managers try to outperform the market based on financial market forecasts. Forecasts extract relevant information from the past and help to reduce the inherent uncertainty of the future. The recent years have witnessed a large increase in the use and publication of forecasts in different fields of economics and finance. The general progress in information and communication technology has increased the availability and ease of use of data and econometrical software packages, and the methodological progress has provided us with sophisticated forecasting procedures. The topic of this special issue of the Journal of Economics and Statistics is the theory and practise of forecasting and forecast evaluation. The purpose is to provide an overview of the state of the art of forecasting; a specific focus is on business cycle forecasts and forecasting in finance. The papers included in this volume deal with both methodological issues and empirical applications.
Macroeconomic Forecast Models – Concepts And Theoretical Notions
Romanian Statistical Review Supplement, 2017
The present age is characterized by a rapid system of change, thus increasing the need to know these changes with the anticipation of future developments. Even if, in anticipation of future developments with the help of the macroeconomic forecast, we are talking about relative knowledge, this knowledge allows both the creation of a set of solutions to adapt to the transformations that are required, as well as the reduction of possible risks. Thus, in this article, the authors intend to present some theoretical notions regarding the macroeconomic forecast and its role in determining the evolution of activities carried out in various economic or social sectors. At the same time, based on the theoretical notions, the authors will present the particularities of the use of the linear regression model in the analysis of the evolution of the macroeconomic indicators.
On the information provided by forecasting models
Technological Forecasting and Social Change, 1980
The Box-Jenkins approach to time series analysis, which is an efficient way of analyzing stationary time series, recommends differencing as a general method for transforming a nonstationary time series into a stationary one. This paper gives a methodological discussion of some other ways of transforming a nonstationary series, in particular removing linear trends. It is argued that in many cases removing trends is superior to differencing in several respects. For example, when the process generating the time series is an ARMA@,q) process added to a linear trend, differencing will produce an ARMA@,q + 1) process that violates the invertibility conditions and is therefore difficult to estimate. The discussion is extended to time series with seasonal patterns. PETER GARDENFORS is an Assistant Professor at the Department of Philosophy, University of Lund, Sweden. He is working on a research project on the methodology of forecasting sponsored by the Planning Division of the Research Institute of Swedish National Defense (FOA P). BENGT HANSSON holds a research position in decision theory with the Swedish Research Council for Humanities and Social Sciences. He also leads a project on "Efficient use of knowledge," sponsored by the Bank of Sweden Tercentenary Foundation. 'The main reference is Box and Jenkins 121. A good representation can also be found in Anderson [ 1]
Long-run forecasting in multicointegrated systems
Journal of Forecasting, 2004
We extend the analysis of Christoffersen and Diebold (1998) on long-run forecasting in cointegrated systems to multicointegrated systems. For the forecast evaluation we consider several loss functions, each of which has a particular interpretation in the context of stock-flow models where multicointegration typically occurs. A loss function based on a standard mean square forecast error (MSFE) criterion focuses on the forecast errors of the flow variables alone. Likewise, a loss function based on the triangular representation of cointegrated systems (suggested by Christoffersen and Diebold) considers forecast errors associated with changes in both stock (modelled through the cointegrating restrictions) and flow variables. We suggest a new loss function which is based on the triangular representation of multicointegrated systems which further penalizes deviations from the long-run relationship between the levels of stock and flow variables as well as changes in the flow variables. Among other things, we show that if one is concerned with all possible long-run relations between stock and flow variables, this new loss function entails high and increasing forecasting gains compared to both the standard MSFE criterion and Christoffersen and Diebold's criterion. The paper demonstrates the importance of carefully selecting loss functions in forecast evaluation of models involving stock and flow variables.
Journal of Forecasting, 2002
Conventional wisdom holds that restrictions on low-frequency dynamics among cointegrated variables should provide more accurate short-to medium-term forecasts than univariate techniques that contain no such information; even though, on standard accuracy measures, the information may not improve long-term forecasting. But inconclusive empirical evidence is complicated by confusion about an appropriate accuracy criterion and the role of integration and cointegration in forecasting accuracy. We evaluate the short-and medium-term forecasting accuracy of univariate Box-Jenkins type ARIMA techniques that imply only integration against multivariate cointegration models that contain both integration and cointegration for a system of five cointegrated Asian exchange rate time series. We use a rolling-window technique to make multiple out of sample forecasts from one to forty steps ahead. Relative forecasting accuracy for individual exchange rates appears to be sensitive to the behaviour of the exchange rate series and the forecast horizon length. Over short horizons, ARIMA model forecasts are more accurate for series with moving-average terms of order >1. ECMs perform better over medium-term time horizons for series with no moving average terms. The results suggest a need to distinguish between 'sequential' and 'synchronous' forecasting ability in such comparisons. 1 Three progressive stages in linear techniques are from univariate structures, such as the ARIMA models of Box-Jenkins, on to the multiple input, single output cases, such as ARMAX models, then through to VAR and VARMA models. 356 M. McCrae et al.