Any measure of unobserved inflation uncertainty relies on specific assumptions which are most likely not fulfilled completely. This calls into question whether an individual measure delivers a reliable signal. To reduce idiosyncratic measurement error, we propose using common information...

We study how millions of granular and weekly household scanner data combined with machine learning can help to improve the real-time nowcast of German inflation. Our nowcasting exercise targets three hierarchy levels of inflation: individual products, product groups, and headline inflation. At...

In this paper we argue that future inflation in an economy depends on the way people perceive current inflation, their inflation sentiment. We construct some simple measures of inflation sentiment which capture whether price acceleration is shared by many components of the CPI basket. In a...

We examine the accuracy of survey-based expectations of the Chilean exchange rate relative to the US dollar. Our out-of-sample analysis reveals that survey-based forecasts outperform the Driftless Random Walk (DRW) in terms of Mean Squared Prediction Error at several forecasting horizons. This...

Based on time series data on inflation rates in Nigeria from 1960 to 2016, we model and forecast inflation using ARMA, ARIMA and GARCH models. Our diagnostic tests such as the ADF tests indicate that NINF time series data is essentially I (1), although it is generally I (0) at 10% level of...

This research uses annual time series data on CPI in Japan from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the X series is I (1). The study presents the ARIMA (1, 1, 0) model for predicting CPI in Japan. The diagnostic tests...

This research uses annual time series data on CPI in the UK from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the K series is I (2). The study presents the ARIMA (1, 2, 1) model for predicting CPI in the UK. The diagnostic...

This research uses annual time series data on CPI in Norway from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the N series is I (2). The study presents the ARIMA (2, 2, 2) model for predicting CPI in Norway. The diagnostic...

This research uses annual time series data on CPI in Australia from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the A series is I (1). The study presents the ARIMA (1, 1, 0) model for predicting CPI in Australia. The...

This research uses annual time series data on CPI in Singapore from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the S series is I (1). The study presents the ARIMA (1, 1, 2) model for predicting CPI in Singapore. The...