Does money matter in inflation forecasting?.

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation

Evidence and Ideology in Macroeconomics: The Case of Investment Cycles

The paper reports the principal findings of a long term research project on the description and explanation of business cycles. The research strongly confirmed the older view that business cycles have large systematic components that take the form of investment cycles. These quasi-periodic movements can be represented as low order, stochastic, dynamic processes with complex eigenvalues. Specifically, there is a fixed investment cycle of about 8 years and an inventory cycle of about 4 years. Maximum entropy spectral analysis was employed for the description of the cycles and continuous time econometrics for the explanatory models. The central explanatory mechanism is the second order accelerator, which incorporates adjustment costs both in relation to the capital stock and the rate of investment. By means of parametric resonance it was possible to show, both theoretically and empirically how cycles aggregate from the micro to the macro level. The same mathematical tool was also used to explain the international convergence of cycles. I argue that the theory of investment cycles was abandoned for ideological, not for evidential reasons. Methodological issues are also discussed

Does money matter in inflation forecasting?

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.Forecasting ; Inflation (Finance) ; Monetary theory

Nonlinear prediction of Malaysian exchange rate with monetary fundamentals

This paper compares one-step-ahead out-of-sample predictions on Malaysian Ringgit-US Dollar exchange rate using the generalized regression neural network for a range of forecasting horizons from 1991M3 to 2008M8. We find that the monetary fundamentals are significant in explaining the dynamics of Malaysian exchange rate in a longer forecast horizon as the performance of monetary exchange rate models outperformed the random walk benchmark model. The results also revealed that Malaysian exchange rate market provides profitable short-term arbitrage opportunities with lagged observations, and the integration of autoregressive terms into the monetary exchange rate models enhanced the out-of-sample forecasting performance.Autoregressive, monetary model, neural network, random walk

Survey of Research on Financial Sector Modeling within DSGE Models: What Central Banks Can Learn from It

This survey gives insight into the ongoing research in financial frictions modeling. The recent financial turmoil has fueled interest in operationalizing financial frictions concepts and introducing them into tools for policy makers. The rapid growth of the literature on these issues is the motivation for our review of the presented approaches. The empirical facts that motivate the inclusion of financial frictions are surveyed. This survey provides a description of the basic approaches for introducing financial frictions into dynamic stochastic general equilibrium models. The significance and empirical identification of the financial accelerator effect is then discussed. The role of financial frictions models in CNB monetary and macroprudential policy is also described. It is concluded that given the heterogeneity of the approaches to financial frictions it is beneficial for the conduct of monetary policy to focus on the development of satellite approaches. The role of financial frictions in DSGE models for macroprudential policy is also discussed, as these models can be used to generate stress-testing scenarios. It can be concluded that DSGE models with financial frictions could complement current stress-testing practice, but are not able to replace stress tests.DSGE models, financial accelerator, financial frictions.

Improving Monetary Policy Models

If macroeconomic models are to be useful in policy-making, where uncertainty is pervasive, the models must be treated as probability models, whether formally or informally. Use of explicit probability models allows us to learn systematically from past mistakes, to integrate model-based uncertainty with uncertain subjective judgment, and to bind data-bassed forecasting together with theory-based projection of policy effects. Yet in the last few decades policy models at central banks have steadily shed any claims to being believable probability models of the data to which they are fit. Here we describe the current state of policy modeling, suggest some reasons why we have reached this state, and assess some promising directions for future progress.

An Empirical Investigation of the Lucas Hypothesis: the Yield Curve and on Linearity in the Money-Output Relationship

Existing evidence about the effectiveness of money growth to stimulate economic activity has been criticized from different perspectives. In addition, high correlation between money and output is not helpful to detect the direction of causality. From a policy perspective, in fact, positive correlation may arise from two opposite policy conducts: either the monetary authority sets the supply of money to influence future output fluctuations, or the central bank controls money growth as a reaction to the recent evolution of macro variables. In this work the relationship between money and output is analysed within a non linear framework that ascribes a primary role to expectations. In particular, we find evidence that the Lucas (1973) hypothesis, that exists an inverse correlation between the variance of nominal shocks and the magnitude of output response to nominal shocks, is supported by data evidence when the yield curve is either flat or downward sloping. We also provide evidence suggesting that the Friedman (1977) hypothesis, that the variability of inflation exerts a negative effect on the natural level of output, holds when a positive risk premium is incorporated in an upward sloping term structure of interest rates.Term Structure, Kalman Filtering, Expectations, Output Growth.

Incorporating prediction and estimation risk in point-in-time credit portfolio models

In this paper we focus on the analysis of the effect of prediction and estimation risk on the loss distribution, risk measures and economic capital. When variables for the determination of probability of default and loss distribution have to be predicted because they are not available at the time the prediction is made, the prediction is prone to errors. The model parameters for the estimation of probability of default or asset correlation are not available, and usually have to be estimated using historical data. The incorporation of prediction and estimation risk generally leads to broader loss distributions and therefore to rising values of risk parameters such as Value at Risk or Expected Shortfall. The level of economic capital required may be strongly underestimated if prediction and estimation risk are ignored. --probability of default,PD,credit risk,default correlation,asset correlation,point in time,value at risk,estimation risk

Bayesian Methods in Nonlinear Time Series

This paper reviews the analysis of the threshold autoregressive, smooth threshold autoregressive, and Markov switching autoregressive models from the Bayesian perspective. For each model we start by describing a baseline model and discussing possible extensions and applications. Then we review the choice of prior, inference, tests against the linear hypothesis, and conclude with models selection. A short discussion of recent progress in incorporating regime changes into theoretical macroeconomic models concludes our survey.Threshold, Smooth Threshold, Markov-switching