Nije dostupno na hrvatskom jeziku.
Kenneth D. West
- 19 March 2009
- WORKING PAPER SERIES - No. 1030Details
- Abstract
- We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the bench-mark to all the alternative models simultaneously rather than sequentially, and do not require re-estimation of models as part of a bootstrap procedure. Both procedures adjust MSPE differences in accordance with Clark and West (2007); one procedure then examines the maximum t-statistic, the other computes a chi-squared statistic. Our simulations examine the proposed procedures and two existing procedures that do not adjust the MSPE differences: a chi-squared statistic, and White’s (2000) reality check. In these simulations, the two statistics that adjust MSPE differences have most accurate size, and the procedure that looks at the maximum t-statistic has best power. We illustrate, our procedures by comparing forecasts of different models for U.S. inflation.
- JEL Code
- C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
- 1 August 2003
- WORKING PAPER SERIES - No. 248Details
- Abstract
- Standard economic models hold that exchange rates are influenced by fundamental variables such as relative money supplies, outputs, inflation rates and interest rates. Nonetheless, it has been well documented that such variables little help predict changes in floating exchange rates -- that is, exchange rates follow a random walk. We show that the data do exhibit a related link suggested by standard models
- JEL Code
- F31 : International Economics→International Finance→Foreign Exchange
F37 : International Economics→International Finance→International Finance Forecasting and Simulation: Models and Applications
G15 : Financial Economics→General Financial Markets→International Financial Markets
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates