Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts

Simulation methods are used to measure the expected differentials between the Mean Square Errors of the forecasts from models based on temporally disaggregated versus aggregated data. This allows for novel comparisons including long-order ARMA models, such as those expected with weekly data, under realistic conditions where the parameter values have to be estimated. The ambivalence of past empirical evidence on the benefits of disaggregation is addressed by analyzing four different economic time series for which relatively large sample sizes are available. Because of this, a sufficient number of predictions can be considered to obtain conclusive results from out-of-sample forecasting contests. The validity of the conventional method for inferring the order of the aggregated models is revised.Data Aggregation, Efficient Forecasting, Research Methods/ Statistical Methods,

Measuring Progress on the Control of Porcine Reproductive and Respiratory Syndrome (PRRS) at a Regional Level: The Minnesota N212 Regional Control Project (Rcp) as a Working Example.

Due to the highly transmissible nature of porcine reproductive and respiratory syndrome (PRRS), implementation of regional programs to control the disease may be critical. Because PRRS is not reported in the US, numerous voluntary regional control projects (RCPs) have been established. However, the effect of RCPs on PRRS control has not been assessed yet. This study aims to quantify the extent to which RCPs contribute to PRRS control by proposing a methodological framework to evaluate the progress of RCPs. Information collected between July 2012 and June 2015 from the Minnesota Voluntary Regional PRRS Elimination Project (RCP-N212) was used. Demography of premises (e.g. composition of farms with sows = SS and without sows = NSS) was assessed by a repeated analysis of variance. By using general linear mixed-effects models, active participation of farms enrolled in the RCP-N212, defined as the decision to share (or not to share) PRRS status, was evaluated and used as a predictor, along with other variables, to assess the PRRS trend over time. Additionally, spatial and temporal patterns of farmers' participation and the disease dynamics were investigated. The number of farms enrolled in RCP-N212 and its geographical coverage increased, but the proportion of SS and NSS did not vary significantly over time. A significant increasing (p<0.001) trend in farmers' decision to share PRRS status was observed, but with NSS producers less willing to report and a large variability between counties. The incidence of PRRS significantly (p<0.001) decreased, showing a negative correlation between degree of participation and occurrence of PRRS (p<0.001) and a positive correlation with farm density at the county level (p = 0.02). Despite a noted decrease in PRRS, significant spatio-temporal patterns of incidence of the disease over 3-weeks and 3-kms during the entire study period were identified. This study established a systematic approach to quantify the effect of RCPs on PRRS control. Despite an increase in number of farms enrolled in the RCP-N212, active participation is not ensured. By evaluating the effect of participation on the occurrence of PRRS, the value of sharing information among producers may be demonstrated, in turn justifying the existence of RCPs

THE PROCESS FOLLOWED BY PPP DATA. ON THE PROPERTIES OF LINEARITY TESTS

Recent research has reported the lack of correct size in stationarity test for PPP deviations within a linear framework. However, theoretically well motivated nonlinear models, such as the ESTAR, appear to parsimoniously fit the PPP data and provide an explanation for the PPP ¿puzzle¿. Employing Monte Carlo experiments we analyze the size and power of the nonlinear tests against a variety of nonstationary hypotheses. We also fit the ESTAR model to data from high inflation economies. Our results provide further support for ESTAR specification.ESTAR, Real Exchange Rate, Size, Linearity Test.

FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure

In this paper a dynamic factor model with mixed frequency is proposed (FaMIDAS), where the past observations of high frequency indicators are used following the MIDAS approach. This structure is able to represent with richer dynamics the information content of the economic indicators and produces smoothed factors and forecasts. In addition, it is particularly suited for real time forecast as it reduces the problem of the unbalanced data set and of the revisions in preliminary data. In the empirical application we specify and estimate a FaMIDAS to forecast Italian quarterly GDP. The short-term forecasting performance is evaluated against other mixed frequency models in a pseudo-real time experiment, also allowing for pooled forecast from factor models.Mixed frequency models, Dynamic factor Models, MIDAS, Forecasting

ARE US GASOLINE PRICE ADJUSTMENTS ASYMMETRIC?

We use the LSE-Hendry general to specific approach to analyse if US gasoline price adjustments are asymmetric with respect to changes in crude oil prices. Furthermore, we modify some weaknesses in the earlier works by Boreinstein, Cameron and Gilbert (1997) and Bachmeier and Griffin (2003) and shows that if the price adjustment equations are properly specified and estimated, alternative specifications and temporal aggregation of data do not affect the results. Monthly US data are used to show that alternative specifications give equally good results and there is no asymmetry in the US gasoline price adjustments.Asymmetric price adjustments, Market power, General to specific approach, Error correction models and Gasoline and crude oil prices

Measuring the Sustainability of Cities: A Survey-Based Analysis of the Use of Local Indicators

We analyze 17 studies of the use of sustainable development indicators (SDI) in an urban setting. The analysis reveals a lack of consensus not only on the conceptual framework and the approach favored, but also on the selection and optimal number of indicators. First, by performing different classifications and categorizations of SDI we identify problems inherent in territorial practices that use SDI. Second, we argue that the lack of consensus in several steps of the creation of SDI stems notably from the ambiguity in the definitions of sustainable development, objectives for the use of such indicators, the selection method and the accessibility of qualitative and quantitative data. Third, we propose a selection strategy for SDI through which we demonstrate the need to adopt a parsimonious list of SDI covering the sustainable development components and their constituent categories as broadly as possible while minimizing the number of indicators retained. Nous analysons 17 études traitant de l’utilisation d’indicateurs de développement durable (IDD) en milieu urbain pour différents pays, provinces ou états occidentaux. 188 IDD différents sont recensés dans ces études dont 135 (72 %) ne sont utilisés qu’une ou deux fois. L’analyse de ces études révèle ainsi un faible consensus non seulement au niveau du cadre conceptuel ou de l’approche préconisée, mais aussi en ce qui concerne la sélection et le nombre d’indicateurs optimal. Premièrement, différents classements et catégorisations des IDD recensés nous permettent d’observer et d’identifier les problèmes inhérents aux pratiques territoriales ayant recours aux IDD. Deuxièmement, nous argumentons que l’absence de consensus à plusieurs étapes de la création des IDD émergent entre autres de l’ambiguïté occasionnée par la définition du développement durable, des objectifs visées par l’utilisation de tels indicateurs, de la méthode de sélection préconisée et de l’accessibilité des données qualitatives et quantitatives en cette matière. Troisièmement, nous proposons une stratégie de sélection des IDD (que nous appelons SuBSeleC) où nous démontrons la nécessité d’adoption d’une liste parcimonieuse d’IDD couvrant le plus largement possible les volets du développement durable et des catégories qui les composent tout en minimisant le nombre d’indicateurs retenus. Le résultat est une liste concise et moins redondante d’indicateurs moins sectoriels et plus intégrateurs ayant l’avantage d’englober les dimensions intégrées du développement durable.Cities, Indicators, Sustainable Development, Environment, Local Governance., Villes, indicateurs, développement durable, environnement, gouvernance locale.