nep-for New Economics Papers
on Forecasting
Issue of 2009‒07‒11
nine papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Levels of log Variables in Vector Autoregressions By Gunnar Bardsen; Helmut Luetkepohl
  2. Forecasting with Spatial Panel Data By Baltagi, Badi H.; Bresson, Georges; Pirotte, Alain
  3. Can the Fed Predict the State of the Economy? By Tara M. Sinclair; Fred Joutz; Herman O. Stekler
  4. Forecasting with Factor-augmented Error Correction Models By Igor Masten; Massimiliano Marcellino; Anindya Banerjeey
  5. Introducing the Euro-STING: Short-Term Indicator of Euro Area Growth By Camacho, Maximo; Pérez-Quirós, Gabriel
  6. Real-time density forecasts from VARs with stochastic volatility By Todd E. Clark
  7. Measuring Inflationary Pressure in Bangladesh: The P-Star Approach By Mustafa. K. Mujeri
  8. Strict and Flexible Inflation Forecast Targets: An Empirical Investigation By Glenn Otto; Graham Voss
  9. On the forecasting of lease expense in firm valuation By Jennergren, L. Peter

  1. By: Gunnar Bardsen; Helmut Luetkepohl
    Abstract: Sometimes forecasts of the original variable are of interest although a variable appears in logarithms (logs) in a system of time series. In that case converting the forecast for the log of the variable to a naive forecast of the original variable by simply applying the exponential transformation is not optimal theoretically. A simple expression for the optimal forecast under normality assumptions is derived. Despite its theoretical advantages the optimal forecast is shown to be inferior to the naive forecast if specification and estimation uncertainty are taken into account. Hence, in practice using the exponential of the log forecast is preferable to using the optimal forecast.
    Keywords: Vector autoregressive model, cointegration, forecast root mean square error
    JEL: C32
    Date: 2009
  2. By: Baltagi, Badi H. (Syracuse University); Bresson, Georges (University of Paris 2); Pirotte, Alain (University of Paris 2)
    Abstract: This paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or ignoring heterogeneity due to the individual effects, using Monte Carlo experiments. In addition, we check the performance of these forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous rather than homogeneous panel data models.
    Keywords: forecasting, BLUP, panel data, spatial dependence, heterogeneity
    JEL: C33
    Date: 2009–06
  3. By: Tara M. Sinclair (Department of Economics The George Washington University); Fred Joutz (Department of Economics The George Washington University); Herman O. Stekler (Department of Economics The George Washington University)
    Abstract: Recent research has documented that the Federal Reserve produces systematic errors in forecasting inflation, real GDP growth, and the unemployment rate, even though these forecasts are unbiased. We show that these systematic errors reveal that the Fed is “surprised” by real and inflationary cycles. Using a modified Mincer-Zarnowitz regression, we show that the Fed knows the state of the economy for the current quarter, but cannot predict it one quarter ahead.
    Keywords: Forecast Evaluation; Federal Reserve; Systematic Errors; Recessions
    JEL: C53 E37 E52 E58
    Date: 2009–06
  4. By: Igor Masten; Massimiliano Marcellino; Anindya Banerjeey
    Abstract: As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter's specification in di¤erences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that relative to the FAVAR, FECM generally o¤ers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.
    Keywords: Forecasting, Dynamic Factor Models, Error Correction Models, Cointegration, Factor-augmented Error Correction Models, FAVAR
    Date: 2009–06–25
  5. By: Camacho, Maximo; Pérez-Quirós, Gabriel
    Abstract: We set out a model to compute short-term forecasts of the euro area GDP growth in real-time. To allow for forecast evaluation, we construct a real-time data set that changes for each vintage date and includes the exact information that was available at the time of each forecast. With this data set, we show that our simple factor model algorithm, which uses a clear, easy-to-replicate methodology, is able to forecast the euro area GDP growth as well as professional forecasters who can combine the best forecasting tools with the possibility of incorporating their own judgement. In this context, we provide examples showing how data revisions and data availability affect point forecasts and forecast uncertainty.
    Keywords: Business cycle; Forecasting; Time Series
    JEL: C22 E27 E32
    Date: 2009–06
  6. By: Todd E. Clark
    Abstract: Central banks and other forecasters have become increasingly interested in various aspects of density forecasts. However, recent sharp changes in macroeconomic volatility such as the Great Moderation and the more recent sharp rise in volatility associated with greater variation in energy prices and the deep global recession pose significant challenges to density forecasting. Accordingly, this paper examines, with real-time data, density forecasts of U.S. GDP growth, unemployment, inflation, and the federal funds rate from VAR models with stochastic volatility. The model of interest extends the steady state prior BVAR of Villani (2009) to include stochastic volatility, because, as found in some prior work and this paper, incorporating informative priors on the steady states of the model variables often improves the accuracy of point forecasts. The evidence presented in the paper shows that adding stochastic volatility to the BVAR with a steady state prior materially improves the real-time accuracy of point and density forecasts.
    Date: 2009
  7. By: Mustafa. K. Mujeri
    Abstract: The paper estimates the P* model for Bangladesh economy and test its forecasting ability through generating recursive forecasts. The empirical result shows that the model performs relatively well and contains additional information regarding future rates of inflation. The price and output gap models fare consistently better then the velocity gap model which brings out the importance of non-monetary factors in explaining inflation dynamics in Bangladesh. The P* model can have wide applications in policy analysis. With financial sector liberalization and reforms, it is likely that the scope for the P* model to play a more proactive role would be ramified in Bangladesh. [BB WP no.0901]
    Keywords: inflation; Bangladesh; P* approach; forecasts; velocity gap model; price gap model; output gap model; financial sector; monetary targeting policy.
    Date: 2009
  8. By: Glenn Otto (University of New South Wales, Australia); Graham Voss (University of Victoria, Canada, Hong Kong Institute for Monetary Research)
    Abstract: We examine whether standard theoretical models of inflation forecast targeting are consistent with the observed behaviour of the central banks of Australia, Canada, and the United States. The target criteria from these models restrict the conditionally expected paths of variables targeted by the central bank, in particular inflation and the output gap. We estimate various moment conditions, providing a description of monetary policy for each central bank under different maintained hypotheses. We then test whether these estimated conditions satisfy the predictions of models of optimal monetary policy. The overall objective is to examine the extent to which and the manner in which these central banks successfully balance inflation and output objectives over the near term. For all three countries, we obtain reasonable estimates for both the strict and flexible inflation forecast targeting models, though with some qualifications. Most notably, for Australia and the United States there are predictable deviations from forecasted targets, which is not consistent with models of inflation targeting. In contrast, the results for Canada lend considerable support to simple models of flexible inflation forecast targeting.
    JEL: E31 E58
    Date: 2009–05
  9. By: Jennergren, L. Peter (Department of Accounting)
    Abstract: The forecasting of lease expense in valuation models like the discounted cash flow model and the discounted dividends model is more complex than the forecasting of non-lease cash operating expense. The reason is that lease expense depends on past real growth and inflation, due to the long lives of the leased assets, whereas non-lease cash operating expense depends only on this year's nominal sales revenue. For that reason, the recommendation is to capitalize operating leases, since that facilitates correct forecasting of lease expense. Naive forecasting (as if lease expense depends only on current nominal sales revenue) can have a noticeable impact on the calculated value of the equity, if the company is a heavy user of leased equipment and has experienced rapid real growth in recent years. An illustrative example is used throughout. This paper extends Jennergren 2008a and Jennergren 2008b.
    Keywords: Leasing; valuation; accounting data; discounted free cash flow; discounted dividends
    Date: 2009–06–29

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