​Department of Economics Management and Statistics

We present a dataset for fuel sales analysis at the Italian provincial (NUTS3) level from January 2015 to October 2023 (release V3, January 2024). Fuel sales data are collected at monthly frequency, and are organized by fuel type, usage, and point of sale (highway, municipal road, extra-network road). Fuels data are augmented by a set of socio-economic and geographical variables, which help explain the impact of economic phenomena and topography on fuel sales. The data is collected from the Monthly oil Bullettin of Italian Ministry of Environment and Energy Security (MITE), ISTAT (Istituto Nazionale di Statistica), Bank of Italy and Eurostat, and has been collected through both automated web scraping and manual downloads, then cleaned and reshaped to be suitable for analysis. The produced dataset may be useful for spatiotemporal fuel sales forecasting, air quality analysis, urban mobility, econometric research, as well as machine learning applications. To further assist the user in finding valuable insight, an R Shiny app (freely available at the webpage https://ale-ch.shinyapps.io/it-fuel-dashboard/) was developed for data exploration. App code and the data have been made fully available on the following Github repository (https://github.com/ale-ch/it-fuel-dashboard). The app consists of interactive plots that allow the user to visualize every variable in the dataset at different time ranges and locations, allowing full flexibility in data exploration.

Codes and database originated for the manuscript published in the DOI article: 10.1016/j.jdeveco.2023.103053

Datasets and codes associate wih the paper "Interpreting the oil risk premium: do oil price shocks matter?". Detailed description of the data is contained in the .zip file

Data for "Convergence of European natural gas prices"

time series of selected macroeconomic variables used as observables in our estimation exercise.

Stata data set

This dataset contains the data from the a set of laboratory experiments with human subjects run at the University of Milano Bicocca. The experiment was programmed using zTree (Fischbacher, 2007). Our experimental design involved four sessions of each of our two treatments. As we have 27 subjects per session, we have collected data from a total of 216 subjects, from October 2014 to January 2015. Subjects were recruited from the undergraduate population of the University of Milano-Bicocca, via the ORSEE software (Greiner, 2004). No subject participated in more than one session of this experiment. In December 2015 we have run two additional sessions per treatment where the order of the games played by the subject was reversed.

The above are two of the main codes written in RATS which are used to carry empirical estimates and bootstrapping analysis of the baseline regression (Table 3) used in our study.

Abstract of associated article: According to the Rockets and Feathers Hypothesis (RFH), the transmission mechanism of positive and negative changes in the price of crude oil to the price of gasoline is asymmetric. Although there have been many contributions documenting that downstream prices are more reactive to increases than to decreases in upstream prices, little is known about the forecasting performance of econometric models incorporating asymmetric price transmission from crude oil to gasoline. In this paper we fill this gap by comparing point, sign and probability forecasts from a variety of Asymmetric-ECM (A-ECM) and Threshold Autoregressive ECM (TAR-ECM) specifications against a standard ECM. Forecasts from A-ECM and TAR-ECM subsume the RFH, while the ECM implies symmetric price transmission from crude oil to gasoline. We quantify the forecast accuracy gains due to incorporating the RFH in predictive models for the prices of gasoline and diesel. We show that, as far as point forecasts are involved, the RFH does not lead to significant improvements, while it can be exploited to produce more accurate sign and probability forecasts. Finally, we highlight that the forecasting performance of the estimated models is time-varying.

This folder contains the data and the R code to reproduce the figures and tables in the paper Zito, Rigon and Dunson (2022) - "Inferring Taxonomic placement from DNA barcoding aiding in discovery of new taxa", accepted as open access publication in Methods in Ecology and Evolution. The file "main_FinBOL.R" reproduces the tables in the main document and in the Supporting information available online for the analysis of the FinBOL data, while "main_Simulation_Section4_SI.R" reproduces the simulation in Section 4 of the Supporting information. All data are saved in the folder "data". For replicability purposes, we added version 2.13 of the RDP classifier to the repository, in the folder "RDP/java". This has been downloaded from https://sourceforge.net/projects/rdp-classifier/. For questions, contact the author at alessandro.zito@duke.edu