A Distant-Early-Warning Model of Inflation Based on M1 Disequilibria

Armour, Jamie; Atta-Mensah, Joseph; Engert, Walter; Hendry, Scott

doi:10.2139/ssrn.48284

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…We chose to use this term spread series rather than the overnight rate on its own because the latter implies that real interest rates have been trending down over our sample. 15 This implies that stance of monetary policy was restrictive throughout the 1980s and easy after the early 1990s, which is at odds with the historical interpretation of events reported in Armour et al (1996). In contrast, the term spread appears to be more consistent with the historical record and also similar to other measures of the stance of Canadian monetary policy described in Fung and Yuan (1999).…”

Section: The Datamentioning

confidence: 93%

Consumption, housing collateral and the Canadian business cycle

Christensen

Corrigan

Mendicino

et al. 2016

Canadian J of Economics

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How important are collateral constraints for reproducing salient features of the data? To address this question, we estimate two nested versions of a New Keynesian model: one with collateralized household debt and the frictionless version of the same model. Both versions of the model are fit to Canadian data using Bayesian methods. We argue that the presence of collateral constraints improves the performance of the model in terms of overall goodness of fit. Housing collateral helps to generate a positive correlation between consumption and house prices. Moreover, housing collateral induced spillovers boosted consumption growth during the housing market boom‐bust cycles of the late 1980s and early 2000s.

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Section: The Datamentioning

confidence: 93%

Consumption, housing collateral and the Canadian business cycle

Christensen

Corrigan

Mendicino

et al. 2016

Canadian J of Economics

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“…The GDP gap is calculated as the deviation of actual, seasonally adjusted monthly real GDP from the economy's potential GDP for that same month. Potential GDP is estimated using the linear-quadratic trend of real GDP suggested by Armour et al (1996). The money supply measure used is seasonally adjusted M1.…”

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confidence: 99%

Neural network versus econometric models in forecasting inflation

Moshiri

Cameron

2000

J. Forecast.

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Arti®cial neural network modelling has recently attracted much attention as a new technique for estimation and forecasting in economics and ®nance. The chief advantages of this new approach are that such models can usually ®nd a solution for very complex problems, and that they are free from the assumption of linearity that is often adopted to make the traditional methods tractable. In this paper we compare the performance of BackPropagation Arti®cial Neural Network (BPN) models with the traditional econometric approaches to forecasting the in¯ation rate. Of the traditional econometric models we use a structural reduced-form model, an ARIMA model, a vector autoregressive model, and a Bayesian vector autoregression model. We compare each econometric model with a hybrid BPN model which uses the same set of variables. Dynamic forecasts are compared for three dierent horizons: one, three and twelve months ahead. Root mean squared errors and mean absolute errors are used to compare quality of forecasts. The results show the hybrid BPN models are able to forecast as well as all the traditional econometric methods, and to outperform them in some cases.

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“…En otro trabajo, Hendry (1995) estimó un modelo de vector autorregresivo de corrección de error (VECM) para la demanda de M1 en Canadá, y Armour et al (1996) y Engert y Hendry (1998) encontraron que este VECM producía buenos pronósticos de inflación de uno a dos años. 16 Igualmente, Hallman, Porter y Small (1991) estimaron una relación de largo plazo entre el dinero y los precios en los Estados Unidos.…”

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Explicación y predicción de la inflación en mercados emergentes: El caso de México

Bailliu¹,

Díaz²,

Kruger³

et al. 2003

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En este artículo se evalúa la precisión de los pronósticos generados por tres tipos de modelos para la inflación en México durante el período 1983-2001. Estos modelos son el margen sobre costo marginal (markup), la curva de Phillips clásica aumentada con tipo de cambio y el modelo de la brecha de dinero. Los modelos se comparan entre sí y contra el estándar mínimo del mejor modelo puro de series de tiempo disponible. Los resultados indican que el modelo del margen sobre costo marginal supera ampliamente a los demás mientras que el poder explicativo de la curva de Phillips clásica solamente supera al del modelo puro de series de tiempo si se incluye al tipo de cambio. El modelo de la brecha de dinero no produce buenos resultados, indicando que el paradigma de "dinero pasivo" es aplicable a México. El artículo concluye con una discusión del futuro de estos modelos en un ambiente de inflación baja y estable.

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A Distant-Early-Warning Model of Inflation Based on M1 Disequilibria

Cited by 10 publications

References 9 publications

Consumption, housing collateral and the Canadian business cycle

Consumption, housing collateral and the Canadian business cycle

Neural network versus econometric models in forecasting inflation

Explicación y predicción de la inflación en mercados emergentes: El caso de México

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