Add to ORKGAutoregressive models are used routinely in forecasting and often lead to better performance than more complicated models. However, empirical evidence is also suggesting that the autoregressive representations of many macroeconomic and financial time series are likely to be subject to structural breaks. This paper develops a theoretical framework for the analysis of small-sample properties of forecasts from general autoregressive models under a structural break. Our approach is quite general and allows for unit roots both pre- and post-break. We derive finite-sample results for the mean squared forecast error of one-step-ahead forecasts, both conditionally and unconditionally and present numerical results for different types of break specif...
This paper considers the problem of forecasting under continuous and discrete structural breaks and ...
This dissertation covers topics in estimation and forecasting under structural breaks, intime-series...
When the assumption of constant parameters fails, the in-sample fit of a model may be a poor guide t...
Autoregressive models are used routinely in forecasting and often lead to better performance than mo...
This paper compares the forecasting performance of models that have been proposed for forecasting in...
Mean square forecast error loss implies a bias–variance trade-off that suggests that structural brea...
This paper compares the forecasting performance of different models which have been proposed for for...
A structural break is viewed as a permanent change in the parameter vector of a model. Using taxonom...
A structural break is viewed as a permanent change in the parameter vector of a model. Using taxonom...
This paper compares the forecasting performance of different models which have been proposed for for...
Abstract: This paper compares the forecasting performance of different models which have been propos...
Recent evidence suggests that many economic time series are subject to structural breaks. In the pre...
Empirical evidence suggests that many macroeconomic and financial time-series are subject to occasio...
This paper revisits the least squares estimator of the linear regression with a structural break. We...
Includes bibliographical references. Title from coverAvailable from British Library Document Supply ...
This paper considers the problem of forecasting under continuous and discrete structural breaks and ...
This dissertation covers topics in estimation and forecasting under structural breaks, intime-series...
When the assumption of constant parameters fails, the in-sample fit of a model may be a poor guide t...
Autoregressive models are used routinely in forecasting and often lead to better performance than mo...
This paper compares the forecasting performance of models that have been proposed for forecasting in...
Mean square forecast error loss implies a bias–variance trade-off that suggests that structural brea...
This paper compares the forecasting performance of different models which have been proposed for for...
A structural break is viewed as a permanent change in the parameter vector of a model. Using taxonom...
A structural break is viewed as a permanent change in the parameter vector of a model. Using taxonom...
This paper compares the forecasting performance of different models which have been proposed for for...
Abstract: This paper compares the forecasting performance of different models which have been propos...
Recent evidence suggests that many economic time series are subject to structural breaks. In the pre...
Empirical evidence suggests that many macroeconomic and financial time-series are subject to occasio...
This paper revisits the least squares estimator of the linear regression with a structural break. We...
Includes bibliographical references. Title from coverAvailable from British Library Document Supply ...
This paper considers the problem of forecasting under continuous and discrete structural breaks and ...
This dissertation covers topics in estimation and forecasting under structural breaks, intime-series...
When the assumption of constant parameters fails, the in-sample fit of a model may be a poor guide t...