C - Mathematical and Quantitative Methods
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GAUSS™ Programs for the Estimation of State-Space Models with ARCH Errors: A User's Guide
State-space models have long been popular in explaining the evolution of various economic variables. This is mainly because they generally have more economic content than do others in their class of parsimonious models (for example, VARs). Yet, in spite of their advantages, use of these models until recently was limited by the assumption that all […] -
The Employment Costs of Downward Nominal-Wage Rigidity
In this paper, we use firm-level wage and employment data to address whether there is evidence of downward nominal-wage rigidity, and whether that rigidity is associated with a reduction in employment. We describe an estimation bias that can result when estimating reduced-form wage and employment equations and suggest a way of controlling for that bias. […] -
Pricing Interest Rate Derivatives in a Non-Parametric Two-Factor Term-Structure Model
Diffusion functions in term-structure models are measures of uncertainty about future price movements and are directly related to the risk associated with holding financial securities. Correct specification of diffusion functions is crucial in pricing options and other derivative securities. In contrast to the standard parametric two-factor models, we propose a non-parametric two-factor term-structure model that […] -
Estimating One-Factor Models of Short-Term Interest Rates
There currently exists in the literature several continuous-time one-factor models for short-term interest rates. This paper considers a wide range of these models that are nested into one general model. These models are approximated using both a discrete-time model and a model that accounts for aggregation effects over time, and are estimated by both the […] -
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. […] -
Forecasting Inflation with the M1-VECM: Part Two
A central bank's main concern is the general direction of future inflation, and not transitory fluctuations of the inflation rate. As a result, this paper is concerned with forecasting a simple measure of the trend of inflation, the eight-quarter CPI-inflation rate. The primary objective is to improve the M1-based vector-error-correction model (VECM) developed by Hendry […] -
A Discussion of the Reliability of Results Obtained with Long-Run Identifying Restrictions
In a recent article, Faust and Leeper (1997) discuss reasons why inference from structural VARs identified with long-run restrictions may not be reliable. In this paper, the authors argue that there are reasons to believe that Faust and Leeper's arguments are not devastating in practice. First, simulation exercises suggest that this approach does well when […] -
Canadian Policy Analysis Model: CPAM
This paper documents the structure and properties of the Canadian Policy Analysis Model (CPAM). CPAM is designed to provide a reasonably complete representation of the Canadian macro economy. -
Menu Costs, Relative Prices, and Inflation: Evidence for Canada
The menu-cost models of price adjustment developed by Ball and Mankiw (1994;1995) predict that short-run movements in inflation should be positively related to the skewness and the variance of the distribution of disaggregated relative-price shocks in each period. We test these predictions on Canadian data using the distribution of changes in disaggregated producer prices to measure the skewness and standard deviation of relative-price shocks.