T T ln(ych) = E[ln(ych)|xch] + μch = xchβ+ μch (1)

where the error term μ follows a normal distribution with mean zero and constant variance, μ~N(0, σ<sup>2</sup> ). This assumption is later relaxed. The set of possible explanatory variables are those common to both training and target data sources, as in Table A.1 and Table A.2 in the Appendix.

We deviate from Newhouse and Vyas (2019) by only including the most recent CES round (2011/12) as training data and excluding the previous rounds in 2004/05 and 2009/10. That paper showed that including a linear time trend substantially improved the accuracy of the prediction when predicting poverty rates in 2004/05 using data from 2009/10 and 2011/12. This suggested that a linear time trend would also give accurate estimates for a projection three years ahead, from 2011/12 to 2014/15. However, validation tests undertaken with the data used in this paper indicated that including a linear time trend, in a model estimated using data from 2009/10 and 2011/12, greatly overpredicted poverty in 2004/05. This is due to a key difference between the data used in this paper and the one used by Newhouse and Vyas (2019), namely the availability of data on some service expenditure items in the latter (see Appendix for further details). Because the real value of these expenditures grew substantially over time, they moderated the estimated impact of the time trend variable and generated a more accurate back-cast of poverty in 2004/05. Because the data considered in this study do not contain data on any expenditure items, relying on a linear time trend to nowcast poverty becomes riskier. This issue is exacerbated by the fact that the prediction from 2011/12 to 2017/18 spans seven years, which is much longer than the three-year gap when projecting from 2011/12 to 2014/15. We therefore assume that the coefficients remain unchanged between 2011/12 and 2017/18. We recognize that this likely understates the extent to which poverty has changed, because it holds the estimated coefficients from 2011 constant, including the intercept.

T Similar to Newhouse and Vyas (2019), the xch vector in equation 1 consists of an intercept as well as household and district level demographic variables, labor market indicators as well as districtlevel rainfall shocks. We include several additional variables, not present in the data used by Newhouse and Vyas (2019) to compensate for the absence of the service expenditure variables in the SCS Health 2017/18. These additional variables include characteristics of the household head such as gender, marital status, and, as explained in the Appendix, the type of cooking fuel.

T In order to choose the explanatory variables to be included in the xch vector, we consider two shrinkage or regularization methods: the least absolute shrinkage selection operator (LASSO) regression method and the Stepwise regression algorithm. Both methods reduce the number of predictors to be included in the final specification of the model, with the aim of reducing the variance of the projections at the cost of a negligible increase in the bias of the coefficients. The LASSO algorithm (Tibshirani 1996), solves the residual error minimization problem of the linear T model in a manner that only a subset xch of all the xch potential variables are chosen in the final model used for projections. On the other hand, there are several ways to carry out stepwise regressions. The forward selection starts with no variables and tests each additional variable using 7