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Konjunkturprognoser och konjunkturpolitikk—Ekonomiska rådets årsbok 1992 (Business cycle forecasting and stabilization policies—Economic council yearbook 1992
578
Book
This point can, of course, be made for virtually any model and estimation procedure and is no criticism against time series models as such. Bayesian VAR models [Litterman (1986)] frequently give better forecasts than classically estimated ones. There are sections on Bayesian estimation, presenting the prior beliefs and distribution popularized by Litterman. It should, however, be noted that other prior distributions might be more suitable and give better forecasts than the normal prior used by Litterman [Kadiyala and Karlsson (1993)]. Turning to the last section, the chapter on VARMAX models makes the useful connection to dynamic simultaneous equations systems and the interpretation of a VARMAX model as the reduced form of a simultaneous equations system. Models with rational expectations are discussed, and it is demonstrated that they can be reduced to VARMAX models if the exogenous variables follow a VARMA process. The special problems connected with forecasting using VARMAX models are also discussed. A weak point in this chapter is the cursory discussion of exogeneity; no connection is made with Granger causality (discussed extensively elsewhere) and no indication is made of how one might test for exogeneity. The use of cointegrating restrictions has proved to be beneficial for the forecast performance of VAR models [Engle and Yoo (198’7); Edlund and Karlsson (1993)], especially for forecasts over longer lead times. The chapter on cointegration is a good introduction to the subject. It is, of course, impossible to cover all aspects of this rapidly developing topic in one short chapter. Additional useful references are the book of readings by Engle and Granger (1991) and the recent book by Banerjee et al. (1993). Sune Karlsson Stockholm School of Economics Stockholm, Sweden
References Banerjee, 1993,
A., J.J. Dolado, Co-Integration,
J.W. Galbraith and D. Hendry, Error Correction and the
reviews Econometric Analysis of Non-Stationary Data (Oxford University Press, Oxford). Dufour, J.-M., 1985, “Unbiasedness of predictions from estimated vector autoregressions”, Econometric Theory 1, 387-402. Edlund, P.-O. and S. Karlsson, 1993, “Forecasting the Swedish unemployment rate: VAR vs. transfer function modelling”, International Journal of Forecasting, 9, 6176. Engle, R.F. and B.S. Yoo, 1987, “Forecasting and testing in co-integrated systems”, Journal of Econometrics, 35, 143-159. Engle, R.F. and C.W.J. Granger, 1991, Long-Run Economic Relationships: Readings in Cointegration (Oxford University Press, Oxford). Kadiyala, K.R. and S. Karlsson, 1993, “Forecasting with generalized Bayesian vector autoregressions”, Journal of Forecasting, 12, 365-378. Litterman, R.B., 1986, “Forecasting with Bayesian vector autoregressions-five years of experience”, Journal of Business & Economic Statistics, 4, 25-38.
Konjunkturprognoser och konjunkturpolitikkEkonomiska RHdets Arsbok 1992 (Business Cycle Forecasting and Stabilization Policies-Economic Council Yearbook 1992 > (Allmanna Forlaget, Stockholm, 1993), pp. 116, ISBN 91-3812586-X, ISSN 1100-3413.
The aim of the Swedish Economic Council, which was set up in November 1987, is to narrow the gap between economic policy making and economic research. The Council attempts to do so by initiating and financing economic research, publishing research and arranging seminars at the Ministry of Finance on subjects that are particularly relevant for policy makers. The Council is chaired by Professor Torsten Persson. The present yearbook is the fifth to appear since 1988 and contains papers related to a 3 year research programme initiated by the Council on forecasting and stabilization policies. Most papers in the yearbook are related to forecasting, as are most of the projects in the programme. Nearly all publications from the programme are written in English and are available from the National Institute of Economic Research (NIER) in Stockholm. NIER is closely related to the Ministry of Finance and prepares forecasts for the Swedish economy mainly by use of
Rook reviews
urlformaii~ed forecasting methods. The use of structural econometric models is relatively limited by international comparison, The present recession has proved to be a problem for most forecasters. Both its depth and length have surprised many observers. The paper by James H. Stock and Mark W. Watson in this yearbook addresses this problem with reference to the USA and their forecasting experience with their own model for coincident and leading indicators. They show that the relationships in the model are unstable and that this instability is not only a recent phenomenon but rather quite common. The reason for this instability is mainly but not exclusively related to the financia1 variables in the model. They use seven leading indicators to forecast the growth rate of the US economy which is measured by an index of four coincident indicators. These variables are chosen by use of a stepwise regression procedure. Two of the leading indicators, the risk premium and the yield curve, are said to be the main source of instability in the model. The explanation behind this resuft is that the change in monetary policy from a neutraI to an expansionary policy during the recession, affected the leading indicators so that they signalled optimism even as late as the autumn of 1990 when the US economy went into recession. The important policy lesson is that the links between monetary policy and the real economy arc highly unstable according to this model. Stock and Watson suggest that a statespace model with time varying parameters is the solution when generating leading indicators. In this model only the weight given to each leading indicator is allowed to vary over time while the lag-structure for each variable is fixed. Their new model which is briefly presented, rapidly adjusts the model to changes in relationships and improves the forecasts. Another-but not necessarily alternative-approach to short run forecasting which is represented in the yearbook is the use of business survey data. The NIER uses the Swedish quarterly survey quite intensively when making their forecasts and the reliability of these surveys and their possibte usefulness as leading indicators are of particular relevance. Firms in manufacturing in particular are asked about their current and planned/expected activity. Timo Terasvirta’s paper discusses how the Swedish survey could be
579
changed so that the answers could be more reliable. His recommendations are partty based on experience with a similar Finnish survey. Terasvirta’s main conclusion is that the respondents have difficulty in seasonally adjusting their answers and that simplifying and modifying the questionnaire could improve data reliability. In another joint research paper in the project (but not included in this yearbook) Markku Rahiala and Timo Terlsvirta use the Kalman-filter technique to estimate a state-space model partly based on survey data from both Sweden and Finland. They show that data from the surveys improve forecasts compared with a univariate model of manufacturing output. A similar conclusion is reached by Reinhold Bergstrom using a different model on Swedish data. The survey data on expected or planned output can improve a model which forecasts manufacturing output even if the improvement is moderate. Recent research using data from a similar Norwegian survey shows that although the survey data do not support the rational expectation hypothesis in most cases, the data contain information useful for forecasters. Thus survey data could be a supplement to short run forecasting models and their use should be of interest to model builders. A. Cappeien Central Bureau of Statistics
Peter Clifton, Hai Nguyen and Susan Nutt, 1991, Market Research
Using Forecasting in Business,
(Butterworth and Heinemann, Stoneham, MA), 294 pp., paperback, US$29.95, ISBN 0-75060153-1. This book is an interesting attempt to meld market analysis, market research and forecasting with a very practical point of view in mind. It is quite strong in presenting information from a practical perspective to a marketer or sales person. However, its treatment of many areas of market research or forecasting is not at a depth sufficient to provide the reader with the level of understanding needed to perform forecasting or market research. It would be good background
Book
This point can, of course, be made for virtually any model and estimation procedure and is no criticism against time series models as such. Bayesian VAR models [Litterman (1986)] frequently give better forecasts than classically estimated ones. There are sections on Bayesian estimation, presenting the prior beliefs and distribution popularized by Litterman. It should, however, be noted that other prior distributions might be more suitable and give better forecasts than the normal prior used by Litterman [Kadiyala and Karlsson (1993)]. Turning to the last section, the chapter on VARMAX models makes the useful connection to dynamic simultaneous equations systems and the interpretation of a VARMAX model as the reduced form of a simultaneous equations system. Models with rational expectations are discussed, and it is demonstrated that they can be reduced to VARMAX models if the exogenous variables follow a VARMA process. The special problems connected with forecasting using VARMAX models are also discussed. A weak point in this chapter is the cursory discussion of exogeneity; no connection is made with Granger causality (discussed extensively elsewhere) and no indication is made of how one might test for exogeneity. The use of cointegrating restrictions has proved to be beneficial for the forecast performance of VAR models [Engle and Yoo (198’7); Edlund and Karlsson (1993)], especially for forecasts over longer lead times. The chapter on cointegration is a good introduction to the subject. It is, of course, impossible to cover all aspects of this rapidly developing topic in one short chapter. Additional useful references are the book of readings by Engle and Granger (1991) and the recent book by Banerjee et al. (1993). Sune Karlsson Stockholm School of Economics Stockholm, Sweden
References Banerjee, 1993,
A., J.J. Dolado, Co-Integration,
J.W. Galbraith and D. Hendry, Error Correction and the
reviews Econometric Analysis of Non-Stationary Data (Oxford University Press, Oxford). Dufour, J.-M., 1985, “Unbiasedness of predictions from estimated vector autoregressions”, Econometric Theory 1, 387-402. Edlund, P.-O. and S. Karlsson, 1993, “Forecasting the Swedish unemployment rate: VAR vs. transfer function modelling”, International Journal of Forecasting, 9, 6176. Engle, R.F. and B.S. Yoo, 1987, “Forecasting and testing in co-integrated systems”, Journal of Econometrics, 35, 143-159. Engle, R.F. and C.W.J. Granger, 1991, Long-Run Economic Relationships: Readings in Cointegration (Oxford University Press, Oxford). Kadiyala, K.R. and S. Karlsson, 1993, “Forecasting with generalized Bayesian vector autoregressions”, Journal of Forecasting, 12, 365-378. Litterman, R.B., 1986, “Forecasting with Bayesian vector autoregressions-five years of experience”, Journal of Business & Economic Statistics, 4, 25-38.
Konjunkturprognoser och konjunkturpolitikkEkonomiska RHdets Arsbok 1992 (Business Cycle Forecasting and Stabilization Policies-Economic Council Yearbook 1992 > (Allmanna Forlaget, Stockholm, 1993), pp. 116, ISBN 91-3812586-X, ISSN 1100-3413.
The aim of the Swedish Economic Council, which was set up in November 1987, is to narrow the gap between economic policy making and economic research. The Council attempts to do so by initiating and financing economic research, publishing research and arranging seminars at the Ministry of Finance on subjects that are particularly relevant for policy makers. The Council is chaired by Professor Torsten Persson. The present yearbook is the fifth to appear since 1988 and contains papers related to a 3 year research programme initiated by the Council on forecasting and stabilization policies. Most papers in the yearbook are related to forecasting, as are most of the projects in the programme. Nearly all publications from the programme are written in English and are available from the National Institute of Economic Research (NIER) in Stockholm. NIER is closely related to the Ministry of Finance and prepares forecasts for the Swedish economy mainly by use of
Rook reviews
urlformaii~ed forecasting methods. The use of structural econometric models is relatively limited by international comparison, The present recession has proved to be a problem for most forecasters. Both its depth and length have surprised many observers. The paper by James H. Stock and Mark W. Watson in this yearbook addresses this problem with reference to the USA and their forecasting experience with their own model for coincident and leading indicators. They show that the relationships in the model are unstable and that this instability is not only a recent phenomenon but rather quite common. The reason for this instability is mainly but not exclusively related to the financia1 variables in the model. They use seven leading indicators to forecast the growth rate of the US economy which is measured by an index of four coincident indicators. These variables are chosen by use of a stepwise regression procedure. Two of the leading indicators, the risk premium and the yield curve, are said to be the main source of instability in the model. The explanation behind this resuft is that the change in monetary policy from a neutraI to an expansionary policy during the recession, affected the leading indicators so that they signalled optimism even as late as the autumn of 1990 when the US economy went into recession. The important policy lesson is that the links between monetary policy and the real economy arc highly unstable according to this model. Stock and Watson suggest that a statespace model with time varying parameters is the solution when generating leading indicators. In this model only the weight given to each leading indicator is allowed to vary over time while the lag-structure for each variable is fixed. Their new model which is briefly presented, rapidly adjusts the model to changes in relationships and improves the forecasts. Another-but not necessarily alternative-approach to short run forecasting which is represented in the yearbook is the use of business survey data. The NIER uses the Swedish quarterly survey quite intensively when making their forecasts and the reliability of these surveys and their possibte usefulness as leading indicators are of particular relevance. Firms in manufacturing in particular are asked about their current and planned/expected activity. Timo Terasvirta’s paper discusses how the Swedish survey could be
579
changed so that the answers could be more reliable. His recommendations are partty based on experience with a similar Finnish survey. Terasvirta’s main conclusion is that the respondents have difficulty in seasonally adjusting their answers and that simplifying and modifying the questionnaire could improve data reliability. In another joint research paper in the project (but not included in this yearbook) Markku Rahiala and Timo Terlsvirta use the Kalman-filter technique to estimate a state-space model partly based on survey data from both Sweden and Finland. They show that data from the surveys improve forecasts compared with a univariate model of manufacturing output. A similar conclusion is reached by Reinhold Bergstrom using a different model on Swedish data. The survey data on expected or planned output can improve a model which forecasts manufacturing output even if the improvement is moderate. Recent research using data from a similar Norwegian survey shows that although the survey data do not support the rational expectation hypothesis in most cases, the data contain information useful for forecasters. Thus survey data could be a supplement to short run forecasting models and their use should be of interest to model builders. A. Cappeien Central Bureau of Statistics
Peter Clifton, Hai Nguyen and Susan Nutt, 1991, Market Research
Using Forecasting in Business,
(Butterworth and Heinemann, Stoneham, MA), 294 pp., paperback, US$29.95, ISBN 0-75060153-1. This book is an interesting attempt to meld market analysis, market research and forecasting with a very practical point of view in mind. It is quite strong in presenting information from a practical perspective to a marketer or sales person. However, its treatment of many areas of market research or forecasting is not at a depth sufficient to provide the reader with the level of understanding needed to perform forecasting or market research. It would be good background