Seasonal Dummies Research Articles

Household's Food and Beverage Expenditure Patterns

We examined household's food expenditures in this study. The empirical work outlined here used quarterly data from 2003 Q1 to 2010 Q3. All variables are in log form and were obtained from the Korea National Statistical Office. The food items included cereals, dairy products, fruits, meat, vegetables, and alcoholic beverages. We applied the ordinary least squares method to a model consisting of household income and seasonal dummies. This is because household expenditures are ordinarily a function of income and have seasonal characteristics. The household's food consumption patterns also reflect the prevailing social and environmental circumstances. This study showed that the income coefficients of cereals, meat, dairy products, and alcoholic beverages tend to increase in the long-run, whereas those of vegetables and fruits decreased. The results also revealed that consumption of alcoholic beverages and meat was greatly affected by household income fluctuations, whereas those of vegetables and dairy products were not sensitive to income. The impulse response functions indicated that expenditures not only increased slowly before peaking one to eight quarters after the income shock but declined very slowly to pre-shock levels. The response of dairy products at the twelfth step was three times as large as that of the first step.

The Importance of Calculating the Potential Gross Domestic Product in the Context of the Taylor Rule

Taylor stated humorously that his rule was so easy that it could be written down on the back of a business card. The reality shows that the practical use of this type of rule implies accepting many assumptions about its final shape. The article mentions only the matter of influence of calculating the potential GDP and output gap on the empirical relevance of the Taylor rule. Two ways of calculating potential GDP were presented, i.e. the HP filter and linear trend of the current and the real GDP both seasonally adjusted (an additive model with seasonal dummies; TRAMO/SEATS procedure).

Metody analizy sezonowości stochastycznej w produkcji budowlano-montażowej

The article presents the influence of seasonal effects on time series. As an important case study, the models for deterministic and stochastic seasonal components have been discussed. A number of procedures to test the null hypothesis of seasonal integration have been described, including HEGY as well as Akdi-Dickey tests. The index of sold construction and assembly production in constant prices has been used for illustration. The authors have provided the evidence on the presence of seasonal unit roots and seasonal dummy variables in this time series.

Effect of Seasonality Treatment on the Forecasting Performance of Tourism Demand Models

This study provides a comprehensive comparison of the performance of the commonly used econometric and time-series models in forecasting seasonal tourism demand. The empirical study is carried out based on the demand for outbound leisure tourism by UK residents to seven destination countries: Australia, Canada, France, Greece, Italy, Spain and the USA. In the modelling exercise, the seasonality of the data is treated using the deterministic seasonal dummies, seasonal unit root test techniques and the unobservable component method. The empirical results suggest that no single forecasting technique is superior to the others in all situations. As far as overall forecast accuracy is concerned, the Johansen maximum likelihood error correction model outperforms the other models. The time-series models also show superior performance in dealing with seasonality. However, the time-varying parameter model performs relatively poorly in forecasting seasonal tourism demand. This empirical evidence suggests that the methods of seasonality treatment affect the forecasting performance of the models and that the pre-test for seasonal unit roots is necessary and can improve forecast accuracy.

Modeling and Forecasting Currency in Circulation in Sri Lanka

Currency in circulation is typically estimated either by specifying a currency demand equation based on the theory of transaction and portfolio demand for money or univariate time series models. The first approach works well with low frequency data but faces limitations with high frequency data series. Therefore, this paper proposes an alternative approach in modeling the high frequency data series by decomposing the trend, the seasonal, and the cyclical components. Three separate models were estimated with monthly, weekly and daily time series, assembling tools for forecasting trend, seasonal patterns and cycles in individual series separately. Trend and seasonal effects were identified by regressing on trend and seasonal dummies while cyclical dynamics were captured by allowing for ARMA effect in the regression disturbances. The sample period is 1 January 2000 to 31 August 2005 and data for 1 September 2005 to 31 January 2006 were used for post validity test. All three models fit the data well and provide very close forecasts during the post sample period. All three models clearly identify both inter-month and intramonths variations of currency in circulation. The models also identified that the Sinhala/Tamil New Year, elections, Christmas and the day prior to public and bank holidays have significant positive impact on demand for currency in Sri Lanka. This methodology may be used in forecasting currency in circulation with careful assessment of day to day current developments in the economy. DOI: 10.4038/ss.v36i1.1230 <em>Staff Studies </em>Volume 36 Numbers 1& 2 2006 p.37-72

A comparison of univariate methods for forecasting container throughput volumes

In this paper, six univariate forecasting models for the container throughput volumes in Taiwan’s three major ports are presented. The six univariate models include the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey model, the hybrid grey model, and the SARIMA model. The purpose of this paper is to search for a model that can provide the most accurate prediction of container throughput. By applying monthly data to these models and comparing the prediction results based on mean absolute error, mean absolute percent error and root mean squared error, we find that in general the classical decomposition model appears to be the best model for forecasting container throughput with seasonal variations. The result of this study may be helpful for predicting the short-term variation in demand for the container throughput of other international ports.

The ripple effect of local house price movements in New Zealand

Utilising a selection of 10 urban area data sets in New Zealand for the period 1994–2004, we examine local house price co‐movements by using various house price indexing approaches, at a monthly level. Applying the Granger causality test based on a vector error correction model (VECM), where seasonality is considered by using seasonal dummy variables, we found in the long run that the ripple effect is most likely constrained within regions. There is little evidence to suggest that the ripple effect will spread nationally between main regional centres. The results support the theory that the ripple effect is likely to be caused by a region’s internal economic factors rather than migration and spatial arbitrage.

Extended complex error correction models for seasonal cointegration

In this paper, we extend the complex error correction model (ECM) of [Cubadda, G. (2001). Complex reduced rank models for seasonally cointegrated time series. Oxford Bulletin of Economics and Statistics, 63, 497–511] to models with two types of deterministic terms: (i) restricted seasonal dummies and constant; (ii) restricted seasonal dummies and unrestricted constant. These types of deterministic terms are most frequently adopted in the analysis of seasonal cointegration by many practitioners and researchers, because the other type–where all seasonal dummies and constant terms are unrestricted–may yield oscillating trends. We obtain the limiting distribution of the likelihood ratio (LR) test for the seasonal cointegrating (CI) rank in the extended models. We also provide asymptotic and finite critical values for the test.

Volatility Modeling, Seasonality and Risk-Return Relationship in GARCH-in-Mean Framework: The Case of Indian Stock and Commodity Markets

This paper is based on an empirical study of volatility, risk premium and seasonality in risk-return relation of the Indian stock and commodity markets. This investigation is conducted by means of the General Autoregressive Conditional Heteroscedasticity in the mean model (GARCH-in-Mean) introduced by Engle et al. (1987). A systematic approach to model volatility in returns is presented. Volatility clustering and asymmetric nature are examined for Indian stock and commodity markets. The risk-return relationship and seasonality in risk-return are also investigated through GARCH-in-Mean modeling in which seasonal dummies are used for return as well as volatility equation. The empirical work has been carried out on market index S&P CNX Nifty for a period of 18 years from January 1990 to December 2007. Gold prices from 22nd July 2005 to 20th February 2008 and Soybean from October 2004 - December 2007 are also considered. The stock and commodity markets returns show persistence as well as clustering and asymmetric properties. Risk-return relationship is positive though insignificant for Nifty and Soybean where as significant positive relationship is found in the case of Gold. Seasonality in risk and return is also found which suggests the asymmetric nature of return, i.e. negative correlation between return and its volatility.

Tourism in the Canary Islands: forecasting using several seasonal time series models

AbstractThis paper deals with the analysis of the number of tourists travelling to the Canary Islands by means of using different seasonal statistical models. Deterministic and stochastic seasonality is considered. For the latter case, we employ seasonal unit roots and seasonally fractionally integrated models. As a final approach, we also employ a model with possibly different orders of integration at zero and the seasonal frequencies. All these models are compared in terms of their forecasting ability in an out‐of‐sample experiment. The results in the paper show that a simple deterministic model with seasonal dummy variables and AR(1) disturbances produce better results than other approaches based on seasonal fractional and integer differentiation over short horizons. However, increasing the time horizon, the results cannot distinguish between the model based on seasonal dummies and another using fractional integration at zero and the seasonal frequencies. Copyright © 2008 John Wiley &amp; Sons, Ltd.

Quarterly Time-Series Forecasting With Neural Networks

Forecasting of time series that have seasonal and other variations remains an important problem for forecasters. This paper presents a neural network (NN) approach to forecasting quarterly time series. With a large data set of 756 quarterly time series from the M3 forecasting competition, we conduct a comprehensive investigation of the effectiveness of several data preprocessing and modeling approaches. We consider two data preprocessing methods and 48 NN models with different possible combinations of lagged observations, seasonal dummy variables, trigonometric variables, and time index as inputs to the NN. Both parametric and nonparametric statistical analyses are performed to identify the best models under different circumstances and categorize similar models. Results indicate that simpler models, in general, outperform more complex models. In addition, data preprocessing especially with deseasonalization and detrending is very helpful in improving NN performance. Practical guidelines are also provided.

Effect of neglected deterministic seasonality on unit root tests

Whenever deterministic seasonality is ignored, the distribution of the Dickey-Fuller test is shifted to the left, with lower dispersion at the same time. When accounting for serial correlation, the distortions become less predictable. A Monte Carlo study confirms that the (augmented) Dickey-Fuller test without seasonal dummies is oversized and has little power at the same time, due to the need of lag augmentation. The effect of neglecting seasonal deterministics on the KPSS test for stationarity depends on the way the long-run variance is estimated.

Investigating the seasonal structure in the German economy using fractional integration with structural breaks

PurposeThe purpose of the paper is to examine the seasonal structure in the German monetary aggregate M1 and output by means of fractional integration techniques.Design/methodology/approachThe authors use a version of the tests of Robinson that permits testing seasonal I (d) models with the possibility of incorporating seasonal dummy variables and structural breaks.FindingsThe results show that there is a strong degree of persistence in their behaviour, especially at the long run or zero frequency, with orders of integration ranging between 1.25 and 1.50.Originality/valueThe main innovation in this work is the use of a new time series approach to the case of seasonality.

A Study on the Impact of SARS on the Forecast of Visitor Arrivals to China

ABSTRACT The objective of this study is to build a model to forecast the visitor arrivals to China and to examine the impact caused by the SARS incident. Seasonal ARIMA models and seasonal dummy variables are created to build the forecast models, and we use intervention analysis to process the outliers resulting from unexpected incidents. The results reveal that the SARS incident had a considerable impact on the international visitor arrivals to China and caused the figures to drop by at least 42%. The seasonal ARIMA models are also used to forecast the visitor arrivals to China up to the end of 2007.

The Nature of Seasonality in Spanish Tourism Time Series

This paper deals with the analysis of seasonality in the context of tourism time series. The authors present a general testing procedure that permits them to consider the cases of deterministic and/or stochastic (with integer and fractional differentiation) seasonality in a unified treatment. The procedure is applied to four Spanish tourism time series: the total (foreign and domestic) number of tourists, the number of domestic tourists, the number of nights spent in hotel accommodation by tourists, and the number of nights spent in hotel accommodation by domestic tourists. The results show that the series can be well described in terms of seasonally fractionally integrated models, with the orders of integration ranging between 0.4 and 0.6 in the case of white noise disturbances, and values slightly smaller with autocorrelated disturbances. Thus the standard practice of taking seasonal dummies (deterministic seasonality) or integer differentiation (seasonal unit roots) may lead to erroneous conclusions about the stochastic behaviour of the series. Moreover, the series seem to be mean reverting, implying that shocks affecting them disappear in the long run though at a very slow hyperbolic rate.

Modeling Seasonality in Tourism Forecasting

Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, ARIMA14 and ARIMA1. ARIMA14 is used for modeling stochastic nonstationary seasonality and requires first and fourth differences to achieve stationarity. ARIMA1 considers the series only in first differences, and seasonality is modeled with a constant and three seasonal dummies. The selection of either model depends on the nature of seasonality. Conventional unit root tests determine the nature of seasonality and the order of integration and, therefore, the series’ choice of forecasting model. To determine whether the test correctly identifies the forecasting model for tourism demand, out-of-sample forecasting performance of ARIMA1 and ARIMA14 is compared with HEGY unit root model selection method. Comparing forecasting performance of both models with HEGY unit root model selection shows that the outcome of HEGY test procedure may not be useful in the selection of a univariate time-series model for quarterly tourism demand series.

Recursive demeaning and deterministic seasonality

In this paper, a mean adjustment scheme for unit root tests in the presence of deterministic seasonality is discussed. The Cauchy estimator for autoregressive processes provides some advantages in the application to unit root tests. In particular, it allows for asymptotically standard normal tests and does not require any tabulation of the critical values. The approach can also be employed for testing seasonal unit root. In both cases, a special scheme of mean adjustment based on recursive coefficients, so-called recursive mean adjustment, is essential to maintain the martingale property of regressors. However, the straightforward recursive estimation of seasonal dummies in the case of deterministic seasonal effects leads to a strong positive bias of the estimated autoregressive parameter and therefore to invalid tests. This paper shows how to overcome this problem and to use the Cauchy estimator for unit root testing in the presence of deterministic seasonality.

Seasonality in stock returns: evidence from an emerging market

This study investigates the nature of seasonality in the monthly stock returns derived from a general index of the Kuwait Stock Exchange. A structural time series model incorporating stochastic dummies reveals that seasonality is present but it is deterministic as implied by the constancy of the monthly seasonal factors over the sample period. Two conventional models that incorporate deterministic seasonal dummies corroborate these results. Moreover, seasonality is found to take the form of a July effect, as opposed to the better-recognized January effect. This finding is attributed to the ‘summer holiday effect’.

Modeling Energy Commodity Futures

The article analyzes the price dynamics of two important commodity futures prices? crude oil and natural gas. Some of the latest models of commodity prices are subjected to empirical testing here? specifically the two-factor model of Schwartz-Smith [2000], which nests other important models developed earlier. The two-factor model includes a mean-reverting short-term deviation and uncertain equilibrium level to which prices gravitate. The two factors are not directly observable but can be estimated from futures prices. This is the base case model in this study. The model parameters of the Schwartz-Smith two-factor model are estimated out of traded futures on natural gas and crude oil, using the fixed maturity format the author creates for futures prices. Analysis of the variance structure of natural gas prices suggests the presence of seasonality. Seasonal adjustment is performed and the model is estimated on the deseasonalized data. The effect of seasonality is studied using both the fixed-maturity price series and the original price series for all the models dealing with seasonality to judge whether the model implications and parameters change across the data series. The seasonally adjusted series produce a better fit to the two-factor model, as indicated by various goodness-of-fit metrics computed. Model-based seasonality approaches are developed as well, among which seasonal dummies and a three-factor model with a stochastic seasonality component of log spot prices. The modeled-in seasonality specifications explain futures prices for natural gas better than the base case of the two-factor model, with the three-factor model producing an especially good description of the prices at a certain point of time, but predicting log prices up to three years out in the future with larger errors than alternative models. The author runs a race between the various parameterized and non-parameterized versions of models with seasonality to test both their in-sample and out-of-sample prediction ability, gauged by the pricing errors they generate. The volatility functions model, based on principal components extraction from daily data, with seasonality in short-term volatility, seems to have the best forecasting ability, followed by the two-factor model on Kendall-type deseasonalized data and the seasonal dummies specification. The Bayesian information criterion (BIC) applied to all the parameterized models singles out the three-factor model, followed by the two-factor model on Kendall-type deseasonalized data of fixed maturity. The three-factor model, however, produces less accurate parameter estimates and larger prediction errors than the other models.

Deterministic seasonality versus seasonal fractional integration

We propose in this article the use of a testing procedure due to Robinson (J. Amer. Statist. Assoc. 89 (1994) 1420) for testing deterministic seasonality versus seasonal fractional integration. A new statistic, based on the score principle, is developed to simultaneously test both the order of integration of the seasonal component and the need of seasonal dummies. Both tests have standard null and local limit distributions. However, finite-sample critical values of the tests are computed, and experiments based on Monte Carlo show that the sizes of the asymptotic tests are too large, these larger sizes being also associated with some superior rejection frequencies compared with the finite-sample-based tests. Using quarterly data for real consumption and income in Canada, the UK and Japan, the results show that both variables are seasonally fractionally integrated for the three countries without need of deterministic seasonal dummies. We also find evidence that the series may be seasonally fractionally cointegrated.