Fast and Robust Rank Aggregation against Model Misspecification

In rank aggregation, preferences from different users are summarized into a total order under the homogeneous data assumption. Thus, model misspecification arises and rank aggregation methods take some noise models into account. However, they all rely on certain noise model assumptions and cannot handle agnostic noises in the real world. In this paper, we propose CoarsenRank, which rectifies the underlying data distribution directly and aligns it to the homogeneous data assumption without involving any noise model. To this end, we define a neighborhood of the data distribution over which Bayesian inference of CoarsenRank is performed, and therefore the resultant posterior enjoys robustness against model misspecification. Further, we derive a tractable closed-form solution for CoarsenRank making it computationally efficient. Experiments on real-world datasets show that CoarsenRank is fast and robust, achieving consistent improvement over baseline methods

Forecasting with multivariate temporal aggregation:the case of promotional modelling

Demand forecasting is central to decision making and operations in organisations. As the volume of forecasts increases, for example due to an increased product customisation that leads to more SKUs being traded, or a reduction in the length of the forecasting cycle, there is a pressing need for reliable automated forecasting. Conventionally, companies rely on a statistical baseline forecast that captures only past demand patterns, which is subsequently adjusted by human experts to incorporate additional information such as promotions. Although there is evidence that such process adds value to forecasting, it is questionable how much it can scale up, due to the human element. Instead, in the literature it has been proposed to enhance the baseline forecasts with external well-structured information, such as the promotional plan of the company, and let experts focus on the less structured information, thus reducing their workload and allowing them to focus where they can add most value. This change in forecasting support systems requires reliable multivariate forecasting models that can be automated, accurate and robust. This paper proposes an extension of the recently proposed Muliple Aggregation Prediction Algorithm (MAPA), which uses temporal aggregation to improve upon the established exponential smoothing family of methods. MAPA is attractive as it has been found to increase both the accuracy and robustness of exponential smoothing. The extended multivariate MAPA is evaluated against established benchmarks in modelling a number of heavily promoted products and is found to perform well in terms of forecast bias and accuracy. Furthermore, we demonstrate that modelling time series using multiple temporal aggregation levels makes the final forecast robust to model misspecification

On structure, family and parameter estimation of hierarchical Archimedean copulas

Research on structure determination and parameter estimation of hierarchical Archimedean copulas (HACs) has so far mostly focused on the case in which all appearing Archimedean copulas belong to the same Archimedean family. The present work addresses this issue and proposes a new approach for estimating HACs that involve different Archimedean families. It is based on employing goodness-of-fit test statistics directly into HAC estimation. The approach is summarized in a simple algorithm, its theoretical justification is given and its applicability is illustrated by several experiments, which include estimation of HACs involving up to five different Archimedean families.Comment: 63 pages, one attachment in attachment.pd

Aggregate loans to the euro area private sector

This paper provides new evidence on the behaviour of euro area aggregate loans to the private sector. Using a sample covering the last twenty years, a cointegrating vector linking the real stock of loans to a small set of domestic macroeconomic variables is found. Besides real GDP and prices, this set includes a new measure of the cost of loans obtained as a weighted average of bank lending rates. The results are overall encouraging, though the recursive estimates of the long-run parameters suggest that in 2000 some disturbances, probably of a temporary nature, affected the system. The study then addresses the issue of the leading indicator properties of loans. It finds that the deviations of the real stock of loans from the equilibrium level implied by the model seem to contain information on future changes in inflation, though not on its level. JEL Classification: C32, C51cointegration, credit, euro area, loans

Export Market Performance of OECD countries: an empirical examination of the role of cost competitiveness

This paper investigates the relationship between export market shares and relative unit labour costs using a long panel of twelve manufacturing industries across fourteen OECD countries. We ask two questions: (a) how sensitive are export market shares to changes in relative costs and (b) what determines the degree of sensitivity? Although both costs and embodied technology are important, we find that neither can fully explain changing export positions. We explore whether the residual country-specific trends might be linked to Ѥeep' structural features of economies such as human capital investment and national ownership patterns. On the second question, the sensitivity of exports to labour costs is lower in high tech industries and in core ERM countries. The industry elasticities have increased over time, especially in industries subject to increasing product market competition. We discuss the implications of these findings for European Monetary Union.

Monetary Policy, Global Liquidity and Commodity Price Dynamics

This paper examines the interactions between money, interest rates, goods and commodity prices at a global level. For this purpose, we aggregate data for major OECD countries and follow the Johansen/Juselius cointegrated VAR approach. Our empirical model supports the view that, when controlling for interest rate changes and thus different monetary policy stances, money (defi ned as a global liquidity aggregate) is still a key factor to determine the long-run homogeneity of commodity prices and goods prices movements. The cointegrated VAR model fi ts with the data for the analysed period from the 1970s until 2008 very well. Our empirical results appear to be overall robust since they pass inter alia a series of recursive tests and are stable for varying compositions of the commodity indices. The empirical evidence is in line with theoretical considerations. The inclusion of commodity prices helps to identify a signifi cant monetary transmission process from global liquidity to other macro variables such as goods prices. We fi nd further support of the conjecture that monetary aggregates convey useful information about variables such as commodity prices which matter for aggregate demand and thus infl ation. Given this clear empirical pattern it appears justifi ed to argue that global liquidity merits attention in the same way as the worldwide level of interest rates received in the recent debate about the world savings and liquidity glut as one of the main drivers of the current fi nancial crisis, if not possibly more.Commodity prices; cointegration; CVAR analysis; global liquidity; infl ation; international spillovers

Monetary Policy, Global Liquidity and Commodity Price Dynamics

This paper examines the interactions between money, interest rates, goods and commodity prices at a global level. For this purpose, we aggregate data for major OECD countries and follow the Johansen/Juselius cointegrated VAR approach. Our empirical model supports the view that, when controlling for interest rate changes and thus different monetary policy stances, money (defined as a global liquidity aggregate) is still a key factor to determine the long-run homogeneity of commodity prices and goods prices movements. The cointegrated VAR model fits with the data for the analysed period from the 1970s until 2008 very well. Our empirical results appear to be overall robust since they pass inter alia a series of recursive tests and are stable for varying compositions of the commodity indices. The empirical evidence is in line with theoretical considerations. The inclusion of commodity prices helps to identify a significant monetary transmission process from global liquidity to other macro variables such as goods prices. We find further support of the conjecture that monetary aggregates convey useful information about variables such as commodity prices which matter for aggregate demand and thus inflation. Given this clear empirical pattern it appears justified to argue that global liquidity merits attention in the same way as the worldwide level of interest rates received in the recent debate about the world savings and liquidity glut as one of the main drivers of the current financial crisis, if not possibly more.Commodity prices, cointegration, CVAR analysis, global liquidity, inflation, international spillovers

Global Liquidity and Commodity Prices: A Cointegrated VAR Approach for OECD Countries

This paper examines the interactions between money, consumer prices and commodity prices at a global level from 1970 to 2008. Using aggregated data for major OECD countries and a cointegrating VAR framework, we are able to establish long run and short run relationships among these variables while the process is mainly driven by global liquidity. According to our empirical findings, different price elasticities in commodity and consumer goods markets can explain the recently observed overshooting of commodity over consumer prices. Although the sample period is rather long, recursive tests corroborate that our CVAR fits the data very well.Commodity prices, cointegration, CVAR analysis, global liquidity, inflation, international spillovers

Robust and Misspecification Resistant Model Selection in Regression Models with Information Complexity and Genetic Algorithms

In this dissertation, we develop novel computationally effiient model subset selection methods for multiple and multivariate linear regression models which are both robust and misspecification resistant. Our approach is to use a three-way hybrid method which employs the information theoretic measure of complexity (ICOMP) computed on robust M-estimators as model subset selection criteria, integrated with genetic algorithms (GA) as the subset model searching engine. Despite the rich literature on the robust estimation techniques, bridging the theoretical and applied aspects related to robust model subset selection has been somewhat neglected. A few information criteria in the multiple regression literature are robust. However, none of them is model misspecification resistant and none of them could be generalized to the misspecified multivariate regression. In this dissertation, we introduce for the first time both robust and misspecification resistant information complexity (ICOMP) criterion to fill in the gap in the literature. More specifically in multiple linear regression, we introduce robust M-estimators with misspecification resistant ICOMP and use the new information criterion as the fitness fuction in GA to carry out the model subset selection. For multivariate linear regression, we derive the two-stage robust Mahalanobis distance (RMD) estimator and introduce this RMD estimator in the computation of information criteria. The new information criteria are used as the fitness function in the GA to perform the model subset selection. Comparative studies on the simulated data for both multiple and multivariate regression show that the robust and misspecification resistant ICOMP outperforms the other robust information criteria and the non-robust ICOMP computed using OLS (or MLE) when the data contain outliers and error terms in the model deviate from a normal distribution. Compared with the all possible model subset selection, GA combined with the robust and misspecification resistant infromation criteria is proved to be an effective method which can quickly find the a near subset, if not the best, without having to search the whole subset model space