Masking of volatility by seasonal adjustment methods

We report that the X-12 ARIMA and TRAMO–SEATS seasonal adjustment methods consistently underestimate the variability of the differenced seasonally adjusted series. We show that underestimation is due to a non-zero estimation error in estimating the seasonal component at each time period, which is the result of the use of low order seasonal filter in X12-ARIMA for estimating the seasonal component. Hence, we propose the use of high order seasonal filter for estimating the seasonal component, which helps reducing the estimation error noticeably, helps amending the underestimation problem, and helps improving the forecasting accuracy of the series. In TRAMO–SEATS, Airline model is found to deliver the best seasonal filter among other ARIMA models.     

An application of a new seasonal unit root test to inflation

In this paper, we apply the modified seasonal unit root test with seasonal level shifts at unknown time proposed by Popp (2007) to the G7 inflation rate. We also study the power properties of this test and generate critical values for a range of different break points and sample sizes. We find that there is a non-seasonal unit root in Canada's inflation rate, a semi-annual unit root in Germany's inflation rate, and no seasonal unit root at the annual frequency for any of the G7 countries.     

Modelling movie attendance with seasonality: evidence from China

High-frequency data improves the timeliness of movie attendance forecasts, but also results in the issue of seasonality. The main objective of this article is to build and test a novel movie attendance model that considers seasonality. Based on the Bass model, we combine an intertemporal demand shift pattern and the binary elements of seasonality – weekends and holidays – and propose a model called DISBM. We chose a sample of 58 movies released in China in 2013 to evaluate our proposal. The empirical results suggest that DISBM has better performance than other seasonal models. We demonstrate that the intertemporal demand shift results in weekend fluctuations, while the extra demand causes the seasonal holiday effect. The intent of this study is to better understand various movie attendance diffusions given different seasonal effects, in order to develop corresponding marketing strategies.     

Seasonal asymmetries in wholesale–retail cost pass-through

This article investigates the extent of seasonal asymmetries in wholesale to retail cost pass-through in the Canadian apple market. We model nonlinearity in cost pass-through in a panel two-regime error correction model. The model employs weekly store-level retail matching wholesale price data for a major US retail chain. Our results reveal distinct seasonal asymmetries in cost pass-through. Retail prices adjust faster during the fall indicating significantly higher pass-through in response to a change in input composition and seasonal expansion of alternative marketing channels. This input composition effect on cost pass-through highlights the general importance of time-variant market conditions and their respective determinants in explaining cost pass-through dynamics in commodity markets.     

Price clustering in Australian water markets

The finding of clustering in financial prices on particular digits is common across a broad range of financial markets. This article explores whether price clustering is also present in the case of the weekly market for seasonal water in rural Victoria, Australia. We find a similar degree of clustering in the seasonal water market. This suggests that the trading activities of the market produce characteristics that are similar to more sophisticated and deeper financial markets.     

The treatment of seasonality in error correction models as unobserved component: a case study for an Austrian consumption function

This paper deals with an alternative approach to treating seasonality in error correction models for consumption with a parsimonious parameterization as proposed by Harvey and Scott. We introduce an unobserved seasonal component into an error correction model for Austrian consumer expenditures on nondurables and services and compare the results with different approaches. The use of stochastic seasonal results in a definite improvement of the estimated model. First version received: October 1997/Final version received: May 2000     

Modelling changes in seasonality in Indian manufacturing production: an application of the STAR model

This paper attempts to identify the factors responsible for changes in seasonal patterns in manufacturing production in India. This is done in the framework of the Smooth Transition Autoregressive (STAR) models. We find that the variations in seasonal patterns in manufacturing production are driven by the rate of growth of rainfall. The gradual changes in the structure of the economy have not yet had any discernible effect on the seasonality, although the overall dynamics of this variable have been affected.     

Visibility,Institutional Preferences and Agency Considerations

We show that market frictions and agency considerations are important concerns when institutional investors make portfolio allocation decisions. For a sample of widely followed firms, institutional holdings increase with increases in visibility as measured by the number of analysts following the firm. We also report a significant seasonal pattern in institutional holdings consistent with the gamesmanship hypothesis, which asserts that institutions rebalance their portfolios in response to agency considerations. Finally, we find that excess returns are highly seasonal with performance, deteriorating when the following by financial analysts increases. "Followed" firms actually exhibit inferior market performance over the 1981-1996 sample period.     

Testing stochastic income convergence in seasonal heterogeneous panels

In this paper we introduce a seasonal version of the Solow–Swan growth model and acquire an empirical income convergence equation. We take this equation as a basis to investigate whether income convergence exists in an OECD sample. To do this, we propose the test statistics under various asymptotic properties for some of the seasonal frequencies in the context of nonstationary heterogeneous panels. Critical values and moments of our statistics are generated and their finite sample performances are examined via Monte Carlo simulations.     

The nature of Austrian macroeconomic time series unit root tests

It is still an open question in economic and econometric modelling whether the non-stationarity in a time series is captured by detrending or by differencing. We test thirrteen Austrian macroenconomic time series for difference versus trend stationarity using informal methods and formal procedures developed by Dickey-Fuller and Phillips-Perron. To eliminate the effects of seasonal adjustment on the tests we apply a third procedure to the unadjusted data, recently developed by Hylleberg-Engle-Granger-Yoo. Independent of the seasonal adjustment the empirical results indicate that these series are integrated of order 1.     

On the (mis)specification of seasonality and its consequences: An empirical investigation with US data

It is well known that mis-specification of a trend leads to spurious cycles in detrended data (see, e.g., Nelson and Kang (1981)). Seasonal-adjustment procedures make assumptions, either implicitly or explicitly, about roots on the unit circle both at the zero and seasonal frequencies. Consequently, seasonal-adjustment procedures may produce spurious seasonal variation and other statistically undesirable effects. In this paper we document for a large class of widely used US quarterly macroeconomic series the effects of competing seasonal-adjustment procedures on the univariate time-series properties of the adjusted series. We also investigate which procedures are most appropriate given the properties of the data. Overall, we find very significant differences and evidence that several U.S. macroeconomic time series contain a mixture of deterministic and stochastic seasonal components.     

Seasonality,price discrimination,and welfare

We discuss a potential limitation to a widely accepted result, namely that an output increase is a necessary condition for welfare to increase with price discrimination. We use a theoretical model to show that the existence of seasonal demand fluctuations may allow for a simultaneous reduction in average output and increase in average welfare. We also discuss a number of extensions of our basic model.     

我国外贸出口与实际产出关系的季节单整与协整分析

本文根据Lamfalussy模型运用季节误差修正模型和季节单整与协整方法,利用1984年1季度至2004年4季度未经季节调整数据研究了我国的实际产出和出口之间的关系.实证分析表明:尽管出口对实际产出有显著的促进作用,但强度却不大,出口依存度每提高1%,实际产出增长率仅提高0.078%,其原因是我国季度实际产出和出口之间不存在(零频率上)长期协整关系,只存在π频率上短期季节协整关系.本文结果有助于消除人们对于人民币升值造成出口减少影响经济增长的顾虑.     

季节平稳过程间的虚假回归

本文推导了当数据生成过程是独立的季节平稳过程情形下,OLS参数估计及检验统计量的极限分布。发现序列中的自相关性会导致虚假回归现象的发生。     

Calendar anomalies in cash and stock index futures: International evidence

This paper examines calendar anomalies (day-of-the-week and monthly seasonal effects) in cash and stock index futures returns. We consider daily data from FTSE100 (UK), FTSE/ASE-20 (Greece), S&P500 (US) and Nasdaq100 (US) spot and future indexes over the period 2004–2011. We employ a Regime-Switching specification which allows us to distinguish between different regimes corresponding to high and low volatile periods. The results show differences in the seasonal patterns in cash and futures indexes due to the existence of basis risk. Calendar effects are also conditioned to the market situation. During a low volatile situation these calendar effects tend to be positive, but these effects turn negative if the market is under a high volatile period. These findings are recommended to financial risk managers dealing with futures markets.     

The distribution of index futures realised volatility under seasonality and microstructure noise

Previous research documents that the distribution of realised volatility appears approximately log-normal. However, formal tests reject normality fairly convincingly, which may indicate intrinsic features in the intraday data series, namely, the presence of seasonal intraday patterns and microstructure noise. Because many models are based on a normality assumption, this must be verified in order to validate the results. We find departures from normality due to the seasonal and noise components of intraday data, such that, after controlling for both features, the volatility estimates follow a log-normal distribution. Our results reveal that failing to account for these market imperfections can have important implications for analyses of volatility transmission and for investment and hedging decisions.     

Splines and the proportion of the seasonal period as a season index

In this paper a seasonal model is proposed to deal with heterogeneous seasonal patterns, in which neither the length of the seasonal period nor the magnitude of the seasonal effects remains the same over time. In these settings, there is a need for parsimony and flexibility. To this end, the seasonal effect at a season is defined as a function of the proportion of the length of the seasonal period elapsed up to this season, and the seasonal pattern is modelled by means of evolving splines. The methodology is illustrated for weekly Canary tomato exports.     

Forecasting the barbados money supply

The purpose of this paper is to provide an adequate forecasting method for the money supply in the Barbadian economy. This would assist the Central Bank in making decisions on monetary intervention. The performance of ARIMA and vector autoregressive forecasting models are investigated along with combinations of these models. The results of this study suggest that there are reasonable options available for obtaining reliable forecasts of the Barbados money supply. Our findings indicate that seasonal factors and interest rate effects should be comprehended within the forecasting model. We accomplished this through a combination forecasting procedure in which seasonal effects are captured by an ARIMA model and interest rates are introduced through a vector autoregressive forecasting model as exogenous variables.     

An analysis of irrigated agricultural outcomes under the prior appropriation doctrine: hypotheses and applications

This article measures irrigated agricultural outcomes under the prior appropriation doctrine by developing a model of farmers’ land allocations in response to water supply change. The modelling approach considers the institutional factors of water rights and permits the inclusive determination of water and land allocations. We utilize farm-level data of irrigated agriculture in Water District #1 in Idaho to examine the predictions from our theoretical model. The water rights priority date is consolidated at the farm level and used to differentiate farmers’ responses. We test a set of hypotheses that relate to water supply and crop revenue. Our results suggest that the water rights priority has a profound impact on agricultural outcomes, indicating strong institutional effects and weak influence of market-based approaches. Farmers respond to both long-term and seasonal water supply change and variation, and they respond to seasonal water supply forecasts in varied ways depending on their water rights portfolios, thus signalling a disproportionate distribution of potential impacts of water supply change.     

Comparison of Stochastic and Spline Models for Temperature‐based Derivatives in China

In this paper, we propose modelling the seasonal variation of temperature with a stochastic process to achieve normality of residuals. We conduct a heuristic comparison of the new stochastic seasonal variation model with three established empirical temperature and pricing models: the model of Alaton et al., the continuous autoregressive model and the spline model. The test criteria are residual normality, the Akaike information criterion, relative errors, and stability of price behaviour. The objective of the paper is to find the most suitable model for the application of temperature‐based derivatives in China. Therefore, 30 years of daily average temperature data from 12 cities in mainland China are applied. The results show that the stochastic seasonal variation model dominates the other three models by providing a more precise fitting of the temperature process. Furthermore, the spline model displays inconsistencies when it is applied to Chinese temperature data. This model has the smallest relative errors, but the worst results for normality of residuals.