Forecasting Inflation in Developing Nations: The Case of Pakistan

This study attempts to outline the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Pakistan’s inflation. A framework for ARIMA forecasting is drawn up. On the basis of insample and out-of-sample forecast it can be concluded that the model has sufficient predictive powers and the findings are well in line with those of other studies. Further, in this study, the main focus is to forecast the monthly inflation on short-term basis, for this purpose, different ARIMA models are used and the candid model is proposed. On the basis of various diagnostic and selection & evaluation criteria the best and accurate model is selected for the short term forecasting of inflation.Forecasting inflation; ARIMA

Forecasting Inflation Through a Bottom-Up Approach: The Portuguese Case

The aim of this paper is to assess inflation forecasting acurracy over the short-term horizon using Consumer Price Index (CPI) disaggregated data. That is, aggregating forecasts is compared with aggregate forecasting. In particular, three questions are addressed: i) one should bottom-up or not, ii) how bottom one should go and iii) how one should model at the bottom. In contrast with the literature, di erent levels of data dis-aggregation are allowed, namely a higher disaggregation level than the one considered up to now. Moreover, both univariate and multivariate models are considered, such as SARIMA and SARIMAX models with dynamic common factors. An out-of-sample forecast comparison (up to twelve months ahead) is done using Portuguese CPI dataset. Aggregating the forecasts seems to be better than aggregate forecasting up to a five-months ahead horizon. Moreover, this improvement increases with the disaggregation level and the multivariate modelling outperforms the univariate one in the very short-run.

Relation between higher order comoments and dependence structure of equity portfolio

We study a relation between higher order comoments and dependence structure of equity portfolio in the US and UK by relying on a simple portfolio approach where equity portfolios are sorted on the higher order comoments. We find that beta and coskewness are positively related with a copula correlation, whereas cokurtosis is negatively related with it. We also find that beta positively associates with an asymmetric tail dependence whilst coskewness negatively associates with it. Furthermore, two extreme equity portfolios sorted on the higher order comoments are closely correlated and their dependence structure is strongly time varying and nonlinear. Backtesting results of value-at-risk and expected shortfall demonstrate the importance of dynamic modeling of asymmetric tail dependence in the risk management of extreme events

Non-Linearities And Fractional Integration In The Us Unemployment Rate

This paper proposes a model of the US unemployment rate which accounts for both its asymmetry and its long memory. Our approach introduces fractional integration and nonlinearities simultaneously into the same framework, using a Lagrange Multiplier procedure with a standard null limit distribution. The empirical results suggest that the US unemployment rate can be specified in terms of a fractionally integrated process, which interacts with some non-linear functions of labour demand variables such as real oil prices and real interest rates. We also find evidence of a long-memory component. Our results are consistent with a hysteresis model with path dependency rather than a NAIRU model with an underlying unemployment equilibrium rate, thereby giving support to more activist stabilisation policies. However, any suitable model should also include business cycle asymmetries, with implications for both forecasting and policy-making