This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or exponential down-weighting. However, these studies...

We investigate the long-term macroeconomic effects of climate change across 48 U.S. states over the period 1963.2016 using a novel econometric strategy which links deviations of temperature and precipitation (weather) from their long-term moving-average historical norms (climate) to various...

We investigate the long-term macroeconomic effects of climate change across 48 U.S. states over the period 1963.2016 using a novel econometric strategy which links deviations of temperature and precipitation (weather) from their long-term moving-average historical norms (climate) to various...

We investigate the long-term macroeconomic effects of climate change across 48 U.S. states over the period 1963 2016 using a novel econometric strategy which links deviations of temperature and precipitation (weather) from their long-term moving-average historical norms (climate) to various...

Empirical evidence suggests that many macroeconomic and financial time series are subject to occasional structural breaks. In this paper we present analytical results quantifying the effects of such breaks on the correlation between the forecast and the realization and on the ability to forecast...

Autoregressive 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...

This paper considers the problem of forecasting under continuous and discrete structural breaks and proposes weighting observations to obtain optimal forecasts in the MSFE sense. We derive optimal weights for continuous and discrete break processes. Under continuous breaks, our approach recovers...

Autoregressive 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...