We study the autocorrelation structure and the spectral density function of aggregates from a discrete-time process. The underlying discrete-time process is assumed to be a stationary AutoRegressive Fractionally Integrated Moving-Average (ARFIMA) process, after suitable number of differencing if necessary. We derive closed-form expressions for the limiting autocorrelation function and the norma...

With the increasing competition in the telecommunications industry, the operators try their best to increase telecom income via various measures, one of which is to set an amount of income as a goal to make the encouragement. Since accurate forecast of income plays an important role in income target setting, this paper builds a time series Autoregressive Integrated Moving Average Model (ARIMA) ...

We examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to two years....

This paper develops a general asymptotic theory for the estimation of strictly stationary and ergodic time series models. Under simple conditions that are straightforward to check, we establish the strong consistency, the rate of strong convergence and the asymptotic normality of a general class of estimators that includes LSE, MLE, and some M-type estimators. As an application, we verify the a...

Abstract Evapotranspiration (ET) is an important process in the hydrological cycle and needs to be accurately quantified for proper irrigation scheduling and optimal water resources systems operation. The time variant characteristics of ET necessitate the need for forecasting ET. In this paper, two techniques, namely a seasonal ARIMA model and Winter's exponential smoothing model, have been inv...

and Applied Analysis 3 is the order of regular differences and φ(B) and θ(B) are, respectively, defined as follows φ (B) = 1 − φ 1 B − φ 2 B 2 − ⋅ ⋅ ⋅ − φ p B p θ (B) = 1 − θ 1 B − θ 2 B 2 − ⋅ ⋅ ⋅ − θ q B q . (5) Random errors, ε t , are assumed to be independently and identically distributed with a mean of zero and a constant variance of σ, and the roots of φ(x) = 0 and θ(x) = 0 all lie outsid...

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic volatility process. We develop a Monte Carlo maximum likelihood method to obtain efficient estimates of...

The paper presents an analysis of the seasonality of Italian daily electricity prices. Since the correct detection of the nature, stochastic or probabilistic, of the seasonality is crucial in ARIMA modeling, a test that allows such detection is presented. The application of this test to the Italian daily prices in the years 2008-11 has pointed to the presence of deterministic seasonality in the...

This article analyses the school performance of migrants dependent on peer groups in different international schooling environments. Using data from the international OECD PISA test, we consider social interaction within and between groups of natives and migrants. Results based on social multipliers (Glaeser et al. 2000, 2003) suggest that both native-tonative and migrant-to-migrant peer effect...

We establish the rate of growth of the length of long strange intervals in an innnite moving average process whose coeecients are regularly varying at innnity. We compute the limiting distribution of the appropriately normalized length of such intervals. The rate of growth of the length of long strange intervals turns out to change dramatically once the exponent of regular variation of the coee...