Recent advances in data recording technology have given researchers new ways of collecting on-line and continuous data for analyzing input-output systems. For example, continuous response digital interfaces are increasingly used in psychophysics. The statistical problem related to these input-output systems reduces to linking time-varying covariates to a continuous response variate. Using real-time data obtained from an experiment in psychoacoustics, we showcase new statistical tools that incorporate dynamical elements of an input-output system. We employ functional data analysis (FDA) methods and a simple differential equation to analyze and model the continuous responses. Furthermore, we outline the issues involved in analyzing input-output systems when the exact form of the underlying mathematical model is not known. Finally, we develop a calibration method to facilitate inter-subject and intra-subject comparisons.

This paper shows that money is a relevant macroeconomic indicator for the description of US monetary policy with simple rules. Empirical analysis based on novel real-time data reveals the economically and statistically significant effect of money on the federal funds rate during the Volcker–Greenspan era, highlighting an interest rate rule that better explains historical policy. The findings suggest that the bias against including money in mainstream macroeconomic models may be due to relying on an incorrect measure of money. A gradual deviation from this rule explains loose monetary policy prior to the Great Recession. Including money aggregates in rule-based policy presents a suitable framework to evaluate and guide Federal Reserve policy.

Forecast errors in budgetary variables are frequent. When systematic, they are a source of concern, as they signal misconduct in fiscal policymaking, undermine the government’s credibility and compromise long-term fiscal sustainability. This paper analyses the characteristics of fiscal forecasting and implementation errors in Italy using real-time data over the period 1998–2009. Several empirical methods are applied in order to identify the features of policymakers’ behaviour in preparing and implementing annual fiscal policy and to discover potential determinants in the formation of the implementation errors. Our results show that implemented budgetary plans systematically fall short one year ahead of ambitious planned adjustments for the main public finance aggregates. Fiscal illusion dominates revenue and GDP forecasting, and preliminary data releases are severely biased estimators of the final data, especially for expenditures. The role of the parliamentary session in driving a severe expenditure drift is confirmed.

This paper develops a new methodology for estimating both the automatic and discretionary components of fiscal policy in one reaction function using the differences between real-time and ex post data. Discretionary policy should respond to information available to the policy maker at the time (real-time data), whereas automatic fiscal policy should respond to the true state of the economy at the time (proxied by the final data). We find that the intended discretionary response of fiscal policy to the cycle is counter-cyclical. Our estimates suggest that the automatic stabilizers are at the lower end of the range found in the related literature. This new methodology reduces the risk present in the conventional CAB approach that part of the discretionary actions may be wrongly attributed to automatic stabilizers. In that sense, automatic stabilizers are typically not as strong as usually claimed. This could be of particular use in countries where insufficient data exist to estimate structural budget sensitivities directly.

This paper assesses the role of qualitative surveys for the early estimation of GDP in the Euro Area in a model-based automated procedure which exploits the timeliness of their release. The analysis is conducted using both an historical evaluation and a real-time case study on the current conjuncture.

An overview is provided of the issues raised in the recent literature on the use of real-time data in the context of nowcasting and forecasting UK macroeconomic events. The ideas are illustrated through two specific applications using UK real-time data available over 1961-2006 and providing probability forecasts that could have been produced in real time over the past twenty years. In the first, we consider the reliability of first-release data on the components of UK aggregate demand by looking at forecasts of the probability of substantial data revisions. In the second, we consider the estimation of the output gap, illustrating the uncertainty surrounding its measurement through density forecasts and focusing on its interpretation in terms of inflationary pressure through an event probability forecast.