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Learning, forecasting and structural breaks (replication data)
We provide a general methodology for forecasting in the presence of structural breaks induced by unpredictable changes to model parameters. Bayesian methods of learning and... -
An evaluation of the forecasts of the federal reserve: a pooled approach (rep...
The Federal Reserve Greenbook forecasts of real GDP, inflation and unemployment are analysed for the period 1974-1997. We consider whether these forecasts exhibit systematic... -
Permanent vs transitory components and economic fundamentals (replication data)
Any non-stationary series can be decomposed into permanent (or trend) and transitory (or cycle) components. Typically some atheoretic pre-filtering procedure is applied to... -
Censored latent effects autoregression, with an application to US unemploymen...
A model is proposed to describe observed asymmetries in postwar unemployment time series data. We assume that recession periods, when unemployment increases rapidly, correspond... -
Measuring predictability: theory and macroeconomic applications (replication ...
We propose a measure of predictability based on the ratio of the expected loss of a short-run forecast to the expected loss of a long-run forecast. This predictability measure... -
Forecasting exchange rates using feedforward and recurrent neural networks (r...
In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step... -
Forecasting in cointegrated systems (replication data)
We consider the implications for forecast accuracy of imposing unit roots and cointegrating restrictions in linear systems of I(1) variables in levels, differences, and...
