nep-for New Economics Papers
on Forecasting
Issue of 2011‒02‒19
nine papers chosen by
Rob J Hyndman
Monash University

  1. Mathematical Models and Economic Forecasting: Some Uses and Mis-Uses of Mathematicsin Economics By David F. Hendry
  2. Forecasting Brazilian Inflation Using a Large Data Set By Francisco Marcos Rodrigues Figueiredo
  3. FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure By Cecilia Frale; Libero Monteforte
  4. Forecasting the term structure of the Euro Market using Principal Component Analysis By Dauwe, Alexander; Moura, Marcelo L.
  5. Out-Of-Sample Comparisons of Overfit Models By Calhoun, Gray
  6. The sensitivity of the Scaled Model of Error with respect to the choice of the correlation parameters: A simulation study By Rebecca Graziani; Nico Keilman
  7. Estimation and evaluation of DSGE models: progress and challenges By Frank Schorfheide
  8. How Rational are the Expected Inflation Rate in Australia? By Paradiso, Antonio; Rao, B. Bhaskara
  9. A Solution to Overoptimistic Forecasts and Fiscal Procyclicality: The Structural Budget Institutions Pioneered by Chile By Frankel, Jeffrey

  1. By: David F. Hendry
    Abstract: We consider three ‘cases studies’ of the uses and mis-uses of mathematics in economics and econometrics. The first concerns economic forecasting, where a mathematical analysis is essential, and is independent of the specific forecasting model and how the process being forecast behaves. The second concerns model selection with more candidate variables than the number of observations. Again, an understanding of the properties of extended general-to-specific procedures is impossible without advanced mathematical analysis. The third concerns inter-temporal optimization and the formation of ‘rational expectations’, where misleading results follow from present mathematical approaches for realistic economies. The appropriate mathematics remains to be developed, and may end ‘problem specific’ rather than generic.
    Keywords: Economic forecasting, structural breaks, model selections, expectations, impulse-indicator saturation, mathematical analyses
    JEL: C02 C22
    Date: 2011
  2. By: Francisco Marcos Rodrigues Figueiredo
    Abstract: The objective of this paper is to verify if exploiting the large data set available to the Central Bank of Brazil, makes it possible to obtain forecast models that are serious competitors to models typically used by the monetary authorities for forecasting inflation. Some empirical issues such as the optimal number of variables to extract the factors are also addressed. I find that the best performance of the data rich models is usually for 6-step ahead forecasts. Furthermore, the factor model with targeted predictors presents the best results among other data-rich approaches, whereas PLS forecasts show a relative poor performance.
    Date: 2010–12
  3. By: Cecilia Frale (MEF-Ministry of the Economy and Finance-Italy, Treasury Department); Libero Monteforte (Bank of Italy and MEF-Ministry of the Economy and Finance-Italy, Treasury Department)
    Abstract: In this paper a dynamic factor model with mixed frequency is proposed (FaMIDAS), where the past observations of high frequency indicators are used following the MIDAS approach. This structure is able to represent with richer dynamics the information content of the economic indicators and produces smoothed factors and forecasts. In addition, the Kalman filter is applied, which is particularly suited for dealing with unbalanced data set and revisions in the preliminary data. In the empirical application for the Italian quarterly GDP the short-term forecasting performance is evaluated against other mixed frequency models in a pseudo-real time experiment, also allowing for pooled forecast from factor models.
    Keywords: mixed frequency models, dynamic factor models, MIDAS,forecasting.
    JEL: E32 E37 C53
    Date: 2011–01
  4. By: Dauwe, Alexander; Moura, Marcelo L.
    Date: 2011–10
  5. By: Calhoun, Gray
    Abstract: This paper uses dimension asymptotics to study why overfit linear regression models should be compared out-of-sample; we let the number of predictors used by the larger model increase with the number of observations so that their ratio remains uniformly positive. Under this limit theory, the naive Diebold-Mariano-West out-of-sample test can test hypotheses about a key quantity for evaluating forecasting models---a time series analogue to the generalization error---as long as the out-of-sample period is small relative to the total sample size. Moreover, tests that are designed to reject if the larger model is true, such as the usual in-sample Wald and LM tests and also Clark and McCracken's (2001, 2005a), McCracken's (2007) and Clark and West's (2006, 2007) out-of-sample statistics, will choose the larger model too often when the smaller model is more accurate.
    Keywords: Generalization Error; Forecasting; ModelSelection; t-test; Dimension Asymptotics
    JEL: C01 C12 C22 C52 C53
    Date: 2011–02–10
  6. By: Rebecca Graziani; Nico Keilman
    Abstract: In this paper we investigate the sensitivity of stochastic population forecasts produced by means of the Scaled Model of Error with respect to the choice of the correlation parameters. In particular, we evaluate the impact that a change in the specification of the correlation of the age-specific fertility forecast error increments across time and age and of the correlation of the age-specific mortality forecast error increments across time, age and sex has on the forecasts of the Total Fertility Rate and of the Male and Female Life Expectancies respectively. In our opinion a sensitivity analysis of this kind is extremely useful, since up to now the relevance and the impact of the choice of the Scaled Model of Error input parameters has not be discussed in detail. Such analysis will provide users with a better understanding of the model itself.
    Keywords: population forecasts, Scaled Model of Error, sensitivity analysis
    Date: 2011–01
  7. By: Frank Schorfheide
    Abstract: Estimated dynamic stochastic equilibrium (DSGE) models are now widely used for empirical research in macroeconomics as well as for quantitative policy analysis and forecasting at central banks around the world. This paper reviews recent advances in the estimation and evaluation of DSGE models, discusses current challenges, and provides avenues for future research.
    Keywords: Econometric models ; Stochastic analysis
    Date: 2011
  8. By: Paradiso, Antonio; Rao, B. Bhaskara
    Abstract: This paper uses the methodology of Pearce (1979) and Bhagestani and Noori (2008) to show that the expected rate of inflation by the market participants in Australia is more rational than the household survey forecasts by the Melbourne Institute.
    Keywords: ARIMA Forecasts; Expected Inflation Rate; Survey data; Australia.
    JEL: C12 C2 E3
    Date: 2011–02–08
  9. By: Frankel, Jeffrey (Harvard Kennedy School)
    Abstract: Historically, many countries have suffered a pattern of procyclical fiscal policy: spending too much in booms and then forced to cut back in recessions, thereby exacerbating the business cycle. This problem has especially plagued Latin American commodity-producers. Since 2000, fiscal policy in Chile has been governed by a structural budget rule that has succeeded in implementing countercyclical fiscal policy. The key innovation is that the two most important estimates of the structural versus cyclical components of the budget - trend output and the 10-year price of copper - are made by expert panels and thus insulated from the political process. Chile's fiscal institutions could usefully be emulated everywhere, but especially in other commodity-exporting countries. This paper finds statistical support for a series of hypotheses regarding forecasts by official agencies that have responsibility for formulating the budget. 1) Official forecasts of budgets and GDP in a 33-country sample are overly optimistic on average. 2) The bias toward over-optimism is stronger the longer the horizon 3) The bias is greater among European governments that are politically subject to the budget rules in the Stability and Growth Pact (SGP). 4) The bias is greater at the extremes of the business cycle, particularly in booms. 5) In most countries, the real growth rate is the key macroeconomic input for budget forecasting. In Chile it is the price of copper. 6) Real copper prices mean-revert in the long run, but this is not always readily perceived. 7) Chile's official forecasts are not overly optimistic on average. 8) Chile has apparently avoided the problem of official forecasts that unrealistically extrapolate in boom times. The conclusion: official forecasts, if not insulated from politics, tend to be overly optimistic, and the problem can be worse when the government is formally subject to budget rules. The key innovation that has allowed Chile in general to achieve countercyclical fiscal policy, and in particular to run surpluses in booms, is not just a structural budget rule in itself, but a regime that entrusts to panels of independent experts the responsibility for estimating the extent to which contemporaneous copper prices and GDP have departed from their long-run trends.
    JEL: E62 F41 H50 O54 Q33
    Date: 2011–02

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