Abstract: Traditional techniques for analyzing time series are based on the notion of stationarity of phenomena under study, but in reality most economic and financial series do not verify this hypothesis, which implies the implementation of specific tools for the detection of such behavior. In this paper, we study nonstationary non-seasonal time series tests in a non-exhaustive manner. We formalize the problem of nonstationary processes with numerical simulations and take stock of their statistical characteristics. The theoretical aspects of some of the most common unit root tests will be discussed. We detail the specification of the tests, showing the advantages and disadvantages of each. The empirical study focuses on the application of these tests to the exchange rate (USD/TND) and the Consumer Price Index (CPI) in Tunisia, in order to compare the Power of these tests with the characteristics of the series.
Abstract: The US Consumer Price Indices (CPIs) measures
hundreds of items in the US economy. Many social programs
and government benefits index to the CPIs. The purpose of
this project is to modernize an existing process. This paper will
show the development of a small, visual, software product that
documents the Economic Price Adjustment (EPA) for longterm
contracts. The existing workbook does not provide the
flexibility to calculate EPAs where the base-month and the
option-month are different. Nor does the workbook provide
automated error checking. The small, visual, software product
provides the additional flexibility and error checking. This
paper presents the feedback to project.
Abstract: This paper examines the effect of the volatility of oil
prices on food price in South Africa using monthly data covering the
period 2002:01 to 2014:09. Food price is measured by the South
African consumer price index for food while oil price is proxied by
the Brent crude oil. The study employs the GARCH-in-mean VAR
model, which allows the investigation of the effect of a negative and
positive shock in oil price volatility on food price. The model also
allows the oil price uncertainty to be measured as the conditional
standard deviation of a one-step-ahead forecast error of the change in
oil price. The results show that oil price uncertainty has a positive
and significant effect on food price in South Africa. The responses of
food price to a positive and negative oil price shocks is asymmetric.
Abstract: GMDH algorithm can well describe the internal structure of objects. In the process of modeling, automatic screening of model structure and variables ensure the convergence rate.This paper studied a new GMDH model based on polynomial spline stimation. The polynomial spline function was used to instead of the transfer function of GMDH to characterize the relationship between the input variables and output variables. It has proved that the algorithm has the optimal convergence rate under some conditions. The empirical results show that the algorithm can well forecast Consumer Price Index (CPI).
Abstract: In this paper bi-annual time series data on unemployment rates (from the Labour Force Survey) are expanded to quarterly rates and linked to quarterly unemployment rates (from the Quarterly Labour Force Survey). The resultant linked series and the consumer price index (CPI) series are examined using Johansen’s cointegration approach and vector error correction modeling. The study finds that both the series are integrated of order one and are cointegrated. A statistically significant co-integrating relationship is found to exist between the time series of unemployment rates and the CPI. Given this significant relationship, the study models this relationship using Vector Error Correction Models (VECM), one with a restriction on the deterministic term and the other with no restriction.
A formal statistical confirmation of the existence of a unique linear and lagged relationship between inflation and unemployment for the period between September 2000 and June 2011 is presented. For the given period, the CPI was found to be an unbiased predictor of the unemployment rate. This relationship can be explored further for the development of appropriate forecasting models incorporating other study variables.