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.

Long Run And Cyclical Dynamics In The Us Stock Market

This paper examines the long-run dynamics and the cyclical structure of the US stock market using fractional integration techniques. We implement a version of the tests of Robinson (1994a), which enables one to consider unit roots with possibly fractional orders of integration both at the zero (long-run) and the cyclical frequencies. We examine the following series: inflation, real risk-free rate, real stock returns, equity premium and price/dividend ratio, annually from 1871 to 1993. When focusing exclusively on the long-run or zero frequency, the estimated order of integration varies considerably, but nonstationarity is found only for the price/dividend ratio. When the cyclical component is also taken into account, the series appear to be stationary but to exhibit long memory with respect to both components in almost all cases. The exception is the price/dividend ratio, whose order of integration is higher than 0.5 but smaller than 1 for the long-run frequency, and is between 0 and 0.5 for the cyclical component. Also, mean reversion occurs in all cases. Finally, we use six different criteria to compare the forecasting performance of the fractional (at both zero and cyclical frequencies) models with others based on fractional and integer differentiation only at the zero frequency. The results show that the former outperform the others in a number of cases

FORECASTING EXCHANGE RATE :A Uni-variate out of sample Approach

In this paper we tried to build univariate model to forecast exchange rate of Indian Rupee in terms of different currencies like SDR, USD, GBP, Euro and JPY. Paper uses Box-Jenkins Methodology of building ARIMA model. Sample data for the paper was taken from March 1992 to June 2004, out of which data till December 2002 were used to build the model while remaining data points were used to do out of sample forecasting and check the forecasting ability of the model. All the data were collected from Indiastat database. Result of the paper shows that ARIMA models provides a better forecasting of exchange rates than simple auto- regressive models or moving average models. We were able to build model for all the currencies, except USD, which shows the relative efficiency of the USD currency market.Exchange rate forecasting, univariate analysis, ARIMA, Box- Jenkins methodology, out of sample approach

Robust Covariance Matrix Estimation with Data-Dependent VAR Prewhitening Order

This paper analyzes the performance of heteroskedasticity-and-autocorrelation-consistent (HAC) covariance matrix estimators in which the residuals are prewhitened using a vector autoregressive (VAR) filter. We highlight the pitfalls of using an arbitrarily fixed lag order for the VAR filter, and we demonstrate the benefits of using a model selection criterion (either AIC or BIC) to determine its lag structure. Furthermore, once data-dependent VAR prewhitening has been utilized, we find negligible or even counter-productive effects of applying standard kernel-based methods to the prewhitened residuals; that is, the performance of the prewhitened kernel estimator is virtually indistinguishable from that of the VARHAC estimator.

Testing the Marshall-Lerner condition in Kenya

In this paper we examine the Marshall-Lerner (ML) condition for the Kenyan economy. In particular, we use quarterly data on the log of real exchange rates, export-import ratio and relative (US) income for the time period 1996q1 – 2011q4, and employ techniques based on the concept of long memory or long-range dependence. Specifically, we use fractional integration and cointegration methods, which are more general than standard approaches based exclusively on integer degrees of differentiation. The results indicate that there exists a well-defined cointegrating relationship linking the balance of payments to the real exchange rate and relative income, and that the ML condition is satisfied in the long run although the convergence process is relatively slow. They also imply that a moderate depreciation of the Kenyan shilling may have a stabilizing influence on the balance of payments through the current account without the need for high interest rates.This study is partly funded by the Ministry of Education of Spain (ECO2011-2014 ECON Y FINANZAS, Spain) and from a Jeronimo de Ayanz project of the Government of Navarra