Autoregressive approaches to import-export time series I: basic techniques

This work is the first part of a project dealing with an in-depth study of effective techniques used in econometrics in order to make accurate forecasts in the concrete framework of one of the major economies of the most productive Italian area, namely the province of Verona. In particular, we develop an approach mainly based on vector autoregressions, where lagged values of two or more variables are considered, Granger causality, and the stochastic trend approach useful to work with the cointegration phenomenon. Latter techniques constitute the core of the present paper, whereas in the second part of the project, we present how these approaches can be applied to economic data at our disposal in order to obtain concrete analysis of import--export behavior for the considered productive area of Verona.Comment: Published at http://dx.doi.org/10.15559/15-VMSTA22 in the Modern Stochastics: Theory and Applications (https://www.i-journals.org/vtxpp/VMSTA) by VTeX (http://www.vtex.lt/

Untenable nonstationarity: An assessment of the fitness for purpose of trend tests in hydrology

The detection and attribution of long-term patterns in hydrological time series have been important research topics for decades. A significant portion of the literature regards such patterns as ‘deterministic components’ or ‘trends’ even though the complexity of hydrological systems does not allow easy deterministic explanations and attributions. Consequently, trend estimation techniques have been developed to make and justify statements about tendencies in the historical data, which are often used to predict future events. Testing trend hypothesis on observed time series is widespread in the hydro-meteorological literature mainly due to the interest in detecting consequences of human activities on the hydrological cycle. This analysis usually relies on the application of some null hypothesis significance tests (NHSTs) for slowly-varying and/or abrupt changes, such as Mann-Kendall, Pettitt, or similar, to summary statistics of hydrological time series (e.g., annual averages, maxima, minima, etc.). However, the reliability of this application has seldom been explored in detail. This paper discusses misuse, misinterpretation, and logical flaws of NHST for trends in the analysis of hydrological data from three different points of view: historic-logical, semantic-epistemological, and practical. Based on a review of NHST rationale, and basic statistical definitions of stationarity, nonstationarity, and ergodicity, we show that even if the empirical estimation of trends in hydrological time series is always feasible from a numerical point of view, it is uninformative and does not allow the inference of nonstationarity without assuming a priori additional information on the underlying stochastic process, according to deductive reasoning. This prevents the use of trend NHST outcomes to support nonstationary frequency analysis and modeling. We also show that the correlation structures characterizing hydrological time series might easily be underestimated, further compromising the attempt to draw conclusions about trends spanning the period of records. Moreover, even though adjusting procedures accounting for correlation have been developed, some of them are insufficient or are applied only to some tests, while some others are theoretically flawed but still widely applied. In particular, using 250 unimpacted stream flow time series across the conterminous United States (CONUS), we show that the test results can dramatically change if the sequences of annual values are reproduced starting from daily stream flow records, whose larger sizes enable a more reliable assessment of the correlation structures

On extending process monitoring and diagnosis to the electrical and mechanical utilities: an advanced signal analysis approach

This thesis is concerned with extending process monitoring and diagnosis to electrical and mechanical utilities. The motivation is that the reliability, safety and energy efficiency of industrial processes increasingly depend on the condition of the electrical supply and the electrical and mechanical equipment in the process. To enable the integration of electrical and mechanical measurements in the analysis of process disturbances, this thesis develops four new signal analysis methods for transient disturbances, and for measurements with different sampling rates. Transient disturbances are considered because the electrical utility is mostly affected by events of a transient nature. Different sampling rates are considered because process measurements are commonly sampled at intervals in the order of seconds, while electrical and mechanical measurements are commonly sampled with millisecond intervals. Three of the methods detect transient disturbances. Each method progressively improves or extends the applicability of the previous method. Specifically, the first detection method does univariate analysis, the second method extends the analysis to a multivariate data set, and the third method extends the multivariate analysis to measurements with different sampling rates. The fourth method developed removes the transient disturbances from the time series of oscillatory measurements. The motivation is that the analysis of oscillatory disturbances can be affected by transient disturbances. The methods were developed and tested on experimental and industrial data sets obtained during industrial placements with ABB Corporate Research Center, Kraków, Poland and ABB Oil, Gas and Petrochemicals, Oslo, Norway. The concluding chapters of the thesis discuss the merits and limitations of each method, and present three directions for future research. The ideas should contribute further to the extension of process monitoring and diagnosis to the electrical and mechanical utilities. The ideas are exemplified on the case studies and shown to be promising directions for future research.Open Acces

Unit Roots Tests with Smooth Breaks: An Application to the Nelson-Plosser Data Set

This paper reconsiders the nature of the trends (i.e. deterministic or stochastic) in macroeconomic time series. For this purpose, the paper employs two new tests that display robustness to structural breaks of unknown forms, irrespective of the date and/or location of the breaks. These tests approximate structural changes as smooth processes via Flexible Fourier transforms. The tests deliver strong evidence in favor of a nonlinear deterministic trend for real GNP, real per capita GNP, employment, the unemployment rate, and stock prices. Further, the two tests confirm the existence of stochastic trends in nominal GNP, consumer prices, real wages, monetary aggregates, velocity, and bond yields. In general, it appears that real variables are stationary while nominal ones have a unit root.Unit Roots, Stationarity Tests, Structural Change

Multivariate KPI for energy management of cooling system in food industry

Within EU, the food industry is currently ranked among the energy-intensive sectors, mainly as a consequence of the cooling system shareover the total energy demand. As such, the definition of appropriate key performance indicators (KPI) for ammonia chillers can play a strategic role for the efficient monitoring of the energy performance of the cooling systems. The goal of this paper is to develop an appropriate management approach, to account for energy inefficiency of the single compressors, and to identify the specific variables driving the performance outliers. To this end, a new KPI is proposed which correlates the energy consumption and the different process variables. The construction of the new indicator was carried out by means of multivariate statistical analysis, in particular using Kernel Partial Least Square (KPLS).This method is able to evaluate the maximum correlation between dataset and energy consumption employing nonlinear regression techniques. The validity of the new KPI is discussed on a case study relevant to the cooling system of a frozen ready meals industry. The assessment of the proposed metric is one against Specific Energy Consumption (SEC) like indicator, typically used in the context of the Energy Management Systems

Statistical Analysis in Art Conservation Research

Evaluates all components of data analysis and shows that statistical methods in conservation are vastly underutilized. Also offers specific examples of possible improvements

Random Walks and Non-Linear Paths in Macroeconomic Time Series: Some Evidence and Implications

This paper investigates whether the inherent non-stationarity of macroeconomic time series is entirely due to a random walk or also to non-linear components. Applying the numerical tools of the analysis of dynamical systems to long time series for the US, we reject the hypothesis that these series are generated solely by a linear stochastic process. Contrary to the Real Business Cycle theory that attributes the irregular behavior of the system to exogenous random factors, we maintain that the fluctuations in the time series we examined cannot be explained only by means of external shocks plugged into linear autoregressive models. A dynamical and non-linear explanation may be useful for the double aim of describing and forecasting more accurately the evolution of the system. Linear growth models that find empirical verification on linear econometric analysis, are therefore seriously called in question. Conversely non-linear dynamical models may enable us to achieve a more complete information about economic phenomena from the same data sets used in the empirical analysis which are in support of Real Business Cycle Theory. We conclude that Real Business Cycle theory and more in general the unit root autoregressive models are an inadequate device for a satisfactory understanding of economic time series. A theoretical approach grounded on non-linear metric methods, may however allow to identify non-linear structures that endogenously generate fluctuations in macroeconomic time series.Random Walks, Real Business Cycle Theory, Chaos