Examining the Ability of Core Inflation to Capture the Overall Trend of Total Inflation

This paper examines whether core inflation is able to predict the overall trend of total inflation using real-time data in a parametric and nonparametric framework. Specifically, two sample periods and five in-sample forecast horizons in two measures of inflation, which are the personal consumption expenditure and the consumer price index, are used in the exclusions-from core inflation persistence model. This paper finds that core inflation is only able to capture the overall trend of total inflation for the twelve-quarter in-sample forecast horizon using the consumer price index in both the parametric and nonparametric models in the longer sample period. The nonparametric model outperforms the parametric model for both data samples and for all five in-sample forecast horizons.Inflation Persistence, Real-Time Data, Monetary Policy, Nonparametrics, In-Sample Forecasting

Real-Time Data Revisions and the PCE Measure of Inflation

This paper tracks data revisions in the Personal Consumption Expenditure using the exclusions-from-core inflation persistence model. Keeping the number of observations the same, the regression parameters of earlier vintages of real-time data, beginning with vintage 1996:Q1, are tested for coincidence against the regression parameters of the last vintage of real-time data, used in this paper, which is vintage 2008:Q2 in a parametric and two nonparametric frameworks. The effects of data revisions are not detectable in the vast majority of cases in the parametric model, but the flexibility of the two nonparametric models is able to utilize the data revisions.Inflation Persistence, Real-Time Data, Monetary Policy, Nonparametrics, In-Sample Forecasting

A Local Examination for Persistence in Exclusions-from-Core Measures of Inflation Using Real-Time Data

Using parametric and nonparametric methods, inflation persistence is examined through the relationship between exclusions-from-core inflation and total inflation for two sample periods and in five in-sample forecast horizons ranging from one quarter to three years over fifty vintages of real-time data in two measures of inflation: personal consumption expenditure and the consumer price index. Unbiasedness is examined at the aggregate and local levels. A local nonparametric hypothesis test for unbiasedness is developed and proposed for testing the local conditional nonparametric regression estimates, which can be vastly different from the aggregated nonparametric model. This paper finds that the nonparametric model outperforms the parametric model for both data samples and for all five in-sample forecast horizons.Real-Time Data, Local Estimation, Nonparametrics, Inflation Persistence, Monetary Policy

Forecasting and tracking real-time data revisions in inflation persistence

This paper presents three local nonparametric forecasting methods that are able to utilize the isolated periods of revised real-time PCE and core PCE for 62 vintages within a historic framework with respect to the nonparametric exclusion-from-core inflation persistence model. The flexibility, provided by the kernel and window width, permits the incorporation of the forecasted value into the appropriate time frame. For instance, a low inflation measure can be included in other low inflation time periods in order to form more optimal forecasts by combining values that are similar in terms of metric distance as opposed to chronological time. The most efficient nonparametric forecasting method is the third model, which uses the flexibility of nonparametrics to its utmost by making forecasts conditional on the forecasted value.Inflation Persistence, Real-Time Data, Monetary Policy, Nonparametrics, Forecasting

Evaluating Exclusion-from-Core Measures of Inflation using Real-Time Data

Using parametric and nonparametric methods, inflation persistence is examined through the relationship between the exclusions-from-core measure of inflation and total inflation for two sample periods and five in-sample forecast horizons ranging from one to twelve quarters over fifty vintages of real-time data in two measures of inflation: personal consumption expenditure and the consumer price index. This paper finds that core inflation is only able to capture the overall trend of total inflation for the twelve-quarter in-sample forecast horizon using the consumer price index in both the parametric and nonparametric models in the longer sample period. The nonparametric model outperforms the parametric model for both data samples and for all five in-sample forecast horizons.Inflation Persistence, Real-Time Data, Monetary Policy, Nonparametrics, In-Sample Forecasting

A Local Examination for Persistence in Exclusions-from-Core Measures of Inflation Using Real-Time Data

Using parametric and nonparametric methods, inflation persistence is examined through the relationship between exclusions-from-core inflation and total inflation for two sample periods and in five in-sample forecast horizons ranging from one quarter to three years over fifty vintages of real-time data in two measures of inflation: personal consumption expenditure and the consumer price index. Unbiasedness is examined at the aggregate and local levels. A local nonparametric hypothesis test for unbiasedness is developed and proposed for testing the local conditional nonparametric regression estimates, which can be vastly different from the aggregated nonparametric model. This paper finds that the nonparametric model outperforms the parametric model for both data samples and for all five in-sample forecast horizons.Real-Time Data, Local Estimation, Nonparametrics, Inflation Persistence, Monetary Policy

A Poisson Regression Examination of the Relationship between Website Traffic and Search Engine Queries

A new area of research involves the use of Google data, which has been normalized and scaled to predict economic activity. This new source of data holds both many advantages as well as disadvantages, which are discussed through the use of daily and weekly data. Daily and weekly data are employed to show the effect of aggregation as it pertains to Google data, which can lead to contradictory findings. In this paper, Poisson regressions are used to explore the relationship between the online traffic to a specific website and the search volumes for certain keyword search queries, along with the rankings of that specific website for those queries. The purpose of this paper is to point out the benefits and the pitfalls of a potential new source of data that lacks transparency in regards to the original level data, which is due to the normalization and scaling procedures utilized by Google.Poisson Regression, Search Engine, Google Insights, Aggregation, Normalization Effects, Scaling Effects

Real-Time Data Revisions and the PCE Measure of Inflation

This paper tracks data revisions in the Personal Consumption Expenditure using the exclusions-from-core inflation persistence model. Keeping the number of observations the same, the regression parameters of earlier vintages of real-time data, beginning with vintage 1996:Q1, are tested for coincidence against the regression parameters of the last vintage of real-time data, used in this paper, which is vintage 2008:Q2 in a parametric and two nonparametric frameworks. The effects of data revisions are not detectable in the vast majority of cases in the parametric model, but the flexibility of the two nonparametric models is able to utilize the data revisions

Examining the Ability of Core Inflation to Capture the Overall Trend of Total Inflation

This paper examines whether core inflation is able to predict the overall trend of total inflation using real-time data in a parametric and nonparametric framework. Specifically, two sample periods and five in-sample forecast horizons in two measures of inflation, which are the personal consumption expenditure and the consumer price index, are used in the exclusions-from core inflation persistence model. This paper finds that core inflation is only able to capture the overall trend of total inflation for the twelve-quarter in-sample forecast horizon using the consumer price index in both the parametric and nonparametric models in the longer sample period. The nonparametric model outperforms the parametric model for both data samples and for all five in-sample forecast horizons