E37 - Forecasting and Simulation: Models and Applications
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A Consistent Bootstrap Test for Conditional Density Functions with Time-Dependent Data
This paper describes a new test for evaluating conditional density functions that remains valid when the data are time-dependent and that is therefore applicable to forecasting problems. We show that the test statistic is asymptotically distributed standard normal under the null hypothesis, and diverges to infinity when the null hypothesis is false. -
Evaluating Factor Models: An Application to Forecasting Inflation in Canada
This paper evaluates the forecasting performance of factor models for Canadian inflation. This type of model was introduced and examined by Stock and Watson (1999a), who have shown that it is quite promising for forecasting U.S. inflation. -
On the Nature and the Stability of the Canadian Phillips Curve
This paper empirically determines why, during the 1990s, inflation in Canada was consistently more stable than predicted by the fixed-coefficients Phillips curve. A time-varying-coefficient model, where all the parameters adjust simultaneously, shows that the behaviour of expectations was probably a major contributing factor. -
The U.S. Capacity Utilization Rate: A New Estimation Approach
The recent strengh of the U.S. economy and historically low rates of inflation have sparked considerable debate among economists and Federal Reserve officials. In order to better explain the recent behaviour of inflation, some observers have raised the concept of a non-accelerating inflation capacity utilization rate (NAICU). In this study, the author presents a new […] -
Indicator Models of Core Inflation for Canada
When there is uncertainty about estimates of the margin of unused capacity in the economy, examining a range of inflation indicators may help in assessing the balance of risks regarding the outlook for inflation. This paper tests a wide range of observable variables for their leading-indicator properties with respect to core inflation, including: commodity prices, […] -
Forecasting GDP Growth Using Artificial Neural Networks
Financial and monetary variables have long been known to contain useful leading information regarding economic activity. In this paper, the authors wish to determine whether the forecasting performance of such variables can be improved using neural network models. The main findings are that, at the 1-quarter forecasting horizon, neural networks yield no significant forecast improvements. […] -
A Modified P*-Model of Inflation Based on M1
This paper examines the performance of M1 in an indicator-model of inflation over time horizons as long as 16 quarters into the future. -
A Distant-Early-Warning Model of Inflation Based on M1 Disequilibria
A vector error-correction model (VECM) that forecasts inflation between the current quarter and eight quarters ahead is found to provide significant leading information about inflation. The model focusses on the effects of deviations of M1 from its long-run demand but also includes, among other things, the influence of the exchange rate, a simple measure of the output gap and past prices.
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