Fan Charts Research Articles

A VAR Model for Non-Performing-Loans in Albania

The objective of this paper is to distinguish the relationship of macroeconomic and macro financial factors with non-performing loans in the Albanian banking system for the last 10 years, through a VAR model. VAR models are used to discover the relationship between variables, while the model selection criteria are used to select the appropriate lag. The chosen lag is 4 and the macroeconomic variables besides NPL included in the study are unemployment, economic growth and consumer price index. The results of the model proved that NPL ratio is sensitive to previous periods of NPL movement. Other macroeconomic variables are insignificant related to actual NPL. Furthermore, through the representation of fan charts we expect a reduction on NPL ratio on the upcoming quarter, which signifies a stronger economy.

Fan charts in era of big data and learning

We propose how to construct big data-driven macroeconomic fan charts, using machine learning methods to reflect the information in 216 relevant economic variables. Such data-rich fan charts do not rely on restrictive model assumptions and allow the exploration of non-Gaussian, asymmetric, heavy-tailed data and their non-linear interactions. By allowing complex patterns to be learned from a data-rich environment, our fan charts are useful for decision making that depends on the uncertainty of a potentially large number of economic variables — most public policy issues.

Constructing Fan Charts from the Ragged Edge of SPF Forecasts

Constructing Fan Charts from the Ragged Edge of SPF Forecasts

Real‐Time Perceptions of Historical GDP Data Uncertainty*

AbstractGDP is measured with error. But data uncertainty is rarely communicated quantitatively in real‐time. An exception are the fan charts for historical real GDP growth published by the Bank of England. To assess how well data uncertainty is understood, we first evaluate the accuracy of the historical fan charts. We find that data uncertainties can be accurately quantified, even without judgement, using past revisions data. Secondly, we conduct an online survey to gauge perceptions of GDP data uncertainty across a wider set of experts. Our results call for greater communication of data uncertainties to anchor experts' dispersed expectations.

Learning Probability Distributions in Macroeconomics and Finance

We propose a deep learning approach to probabilistic forecasting of macroeconomic and financial time series. Being able to learn complex patterns from a data rich environment, our approach is useful for a decision making that depends on uncertainty of large number of economic outcomes. Specifically, it is informative to agents facing asymmetric dependence of their loss on outcomes from possibly non-Gaussian and non-linear variables. We show the usefulness of the proposed approach on the two distinct datasets where a machine learns the pattern from data. First, we construct macroeconomic fan charts that reflect information from high-dimensional data set. Second, we illustrate gains in prediction of stock return distributions which are heavy tailed, asymmetric and suffer from low signal-to-noise ratio.

Socioeconomic Effects of COVID-19 Pandemic: Exploring Uncertainty in the Forecast of the Romanian Unemployment Rate for the Period 2020–2023

During the health crisis, it is vital to protect not only the critical sectors of the economy, the assets, technology, and infrastructure, but first and foremost, it is fundamental to protect jobs and workers. The current COVID-19 pandemic has had a strong impact on the labor market from three main perspectives: number of jobs (through unemployment and underemployment), quality of work (through wages, or access to social protection), and through the effects on specific groups, with a higher degree of vulnerability to unfavorable labor market outcomes. The measures aiming to reduce economic activity and social contacts lead to a reduction of labor demand and implicitly to the increase of the unemployment rate. In this context, it becomes even more relevant to be able to monitor the unemployment rate, providing relevant forecasts that include the effects of market shocks. Thus, our paper aims to forecast the unemployment rate for the period 2020–2023 using the Box-Jenkins methodology based on ARIMA models, exploring also the uncertainty based on fan charts. Although the baseline forecast offers valuable information, a good understanding of risks and uncertainties related to this forecast is equally important. The empirical results highlighted an ascending trend for unemployment rate during 2020, followed by a slow and continuous decrease until the end of 2023 with a high probability for the forecast to be above the central projection.

How can an economic scenario generation model cope with abrupt changes in financial markets?

PurposeIt is quite possible that financial institutions including life insurance companies would encounter turbulent situations such as the COVID-19 pandemic before policies mature. Constructing models that can generate scenarios for major assets to cover abrupt changes in financial markets is thus essential for the financial institution's risk management.Design/methodology/approachThe key issues in such modeling include how to manage the large number of risk factors involved, how to model the dynamics of chosen or derived factors and how to incorporate relations among these factors. The authors propose the orthogonal ARMA–GARCH (autoregressive moving-average–generalized autoregressive conditional heteroskedasticity) approach to tackle these issues. The constructed economic scenario generation (ESG) models pass the backtests covering the period from the beginning of 2018 to the end of May 2020, which includes the turbulent situations caused by COVID-19.FindingsThe backtesting covering the turbulent period of COVID-19, along with fan charts and comparisons on simulated and historical statistics, validates our approach.Originality/valueThis paper is the first one that attempts to generate complex long-term economic scenarios for a large-scale portfolio from its large dimensional covariance matrix estimated by the orthogonal ARMA–GARCH model.

Assessing public debt sustainability for Costa Rica using the fiscal reaction function

This paper assesses Costa Rica's public debt sustainability empirically using three complementary approaches: the calculation of the debt-stabilizing primary fiscal balance obtained from the government's intertemporal budget constraint, the estimation of the parameters of a fiscal policy reaction function (FRF) following the methodology originally proposed by proposed by Bohn (2007); and the estimation of fan charts for the primary fiscal balance and public debt expressed as shares of GDP following the approach proposed by Celasun et al. (2006). With annual data from the period 1974–2018, we find that debt has been unsustainable for specific long- and short-term episodes. For the most recent observations, the conclusion is that debt trajectory is unsustainable. Given that a major fiscal reform was approved at the end of 2018, an uncertainty evaluation of its impact on the primary balance's adjustment path until 2023 and 2030 is included using the official estimated reform projections. The results indicate that the maximum level of the debt ratio will be 68% in 2026, after which this upward trend reverses.

Is the recent increase in national homicide abnormal? Testing the application of fan charts in monitoring national homicide trends over time

PurposeThe goal of this study is to compare the increase in the 2015 national homicide rate to the historical data series and other violent crime rate changes. MethodsWe use ARIMA models and a one-step ahead forecasting technique to predict national homicide, rape, robbery, and aggravated assault rates in the United States. Annual Uniform Crime Report data published by the Federal Bureau of Investigation are used in our analysis. ResultsThe 2015 homicide rate increased above the 90% prediction interval for our model, but not more conservative intervals. Predictions intervals for other national level crime rates consistently produced correct coverage using our forecasting approach. ConclusionsOur findings provide weak evidence that the national homicide rate spiked in 2015, though data for 2016–2018 do not show a continued anomalous increase in the U.S. homicide rate.

Disaggregated Short-Term Inflation Forecast (STIF) for Monetary Policy Decision in Sierra Leone

In this paper, the researchers have developed a short term inflation forecasting (STIF) model using Box-Jenkins time series approach (ARIMA) for analysing inflation and associated risks in Sierra Leone. The model is aided with fan charts for all thirteen components, including the Headline CPI as communication tools to inform the general public about uncertainties that surround price dynamics in Sierra Leone – this then make it possible for policy makers to utilise expert judgments in a bid to stabilize the economy. The uniqueness of this paper is its interpretation of risks to each of the disaggregated components, while also improving credibility of decisions taken by policy makers at the Bank of Sierra Leone [BSL]. Empirically, Food and Non-Alcoholic Beverages, Housing and Health components indicate that shock arising from within or outside of Sierra Leone can significantly impact headline CPI, with immediate pass-through effect of high prices on consumers’ spending, at least in the short-run. The outcome of this empirical research shows uniqueness of the disaggregated model in tailoring policy makers’ attention towards targeting sector-specific policy interventions. Fan Charts produced have also highlighted degree of risks, which is based on confidence bands, which shows deviation from the baseline forecast. The ultimate goal is to improve sectoral productive capacity, while at the same time, monitoring price volatility spill-over through empirical disaggregation of the CPI basket – in association with this, outcome from the study also shows that the use of multivariate models like VAR would be welcome to monitor events on price dynamics in the national economy.

Inflation projections for monetary policy decision making

Many central banks set inflation targets over the medium term and inflation projections are a key input for monetary policy decision making. In this paper, we present the procedures used by the Spanish Central Bank staff to project consumer price inflation. We also provide some illustrations of their policy uses, such as fan charts, deflation probabilities and the monitoring of inflation targets.

Gauging the uncertainty of the economic outlook using historical forecasting errors: The Federal Reserve’s approach

The Federal Open Market Committee (FOMC) of the U.S. Federal Reserve regularly publishes participants’ qualitative assessments of forecast uncertainty, expressed relative to that seen on average in the past. The benchmarks used for these historical comparisons are the average root mean squared forecast errors (RMSEs) made by various private and government forecasters over the past twenty years. This paper documents how these benchmarks are constructed and discusses some of their properties. We draw several conclusions. First, if past performance is a reasonable guide to future accuracy, considerable uncertainty surrounds macroeconomic projections. Second, different forecasters have similar accuracy. Third, estimates of uncertainty about future real activity and interest rates are now considerably greater than prior to the financial crisis; in contrast, estimates of inflation accuracy have changed little. Finally, fan charts, constructed under certain assumptions and viewed in conjunction with the FOMC’s qualitative assessments, provide a reasonable approximation to future uncertainty.

Present state and perspectives of variable renewable energies in Spain

In accordance with the Paris Climate Agreement (2015) and the more recent European Union Winter Package of November 2016, the European nations have committed to drastically cut CO2 emissions during the next decades, especially in the power sector. To this end, Spain as well as many other European countries are initiating plans for a large deployment of variable renewable energy sources (VRES), especially motivated by the huge lowering in prices of solar and wind installations. In the first part of this work, a detailed analysis of the current Spanish electricity mix is carried out, especially of the present generation by VRES. To this end, we present hourly and daily fan charts, for the different days of the week as well as months or seasons of the year. These studies show that the current power system is quite varied and presents a large installed capacity in relation to peak demand. Other aspects, that will surely assist the transition to lower emission targets are the following: the recent adjudication of 9000MW of VRES, which will be operational within the next 2-3 years; a large overcapacity of Combined Cycle Gas Turbines (CCGT) plants, which could be used during the transition as backup plants; and the relatively large hydro-pump potential for the storage of possible VRES surpluses. Finally, the possibility of decommissioning several nuclear plants in a few years is also discussed.

Temperature-Induced Topological Phase Transition in HgTe Quantum Wells.

We report a direct observation of temperature-induced topological phase transition between the trivial and topological insulator states in an HgTe quantum well. By using a gated Hall bar device, we measure and represent Landau levels in fan charts at different temperatures, and we follow the temperature evolution of a peculiar pair of "zero-mode" Landau levels, which split from the edge of electronlike and holelike subbands. Their crossing at a critical magnetic field B_{c} is a characteristic of inverted band structure in the quantum well. By measuring the temperature dependence of B_{c}, we directly extract the critical temperature T_{c} at which the bulk band gap vanishes and the topological phase transition occurs. Above this critical temperature, the opening of a trivial gap is clearly observed.

State-Owned Enterprises Leverage as a Contingency in Public Debt Sustainability Analysis: The Case of the People's Republic of China

We reflect state-owned enterprises’ (SOE) leverage within the standard debt sustainability assessment framework. Based on company data and the interest coverage ratio as a measure of debt at risk, aggregate baseline projections and fan charts gauge SOE debt as a contingent liability to the public sector. We find that SOE leverage in the People’s Republic of China has grown to a large liability that deserves the urgent attention it has been receiving from the authorities. While there is no room for complacency, there is no need for panic either; even if authorities had to step into mop up as much as 20% of SOE debt at risk gone bad, this would appear to be manageable at roughly 2.7% of the gross domestic product in 2016 or 5.5% by 2021. These findings are reflective of discretionary assumptions about future developments in the SOE sector and the broader economy—including baseline conditions premised on preventive government action to slow borrowing—that are adjustable to reflect analysts’ prerogatives and expectations.

Monitoring Volatile Homicide Trends Across U.S. Cities

While U.S. homicide rates remain at near historical lows, large percentage increases in homicide in 2015 have generated much speculation about its causes among policy makers, academics, criminal justice practitioners, media representatives, and other social commentators. In this article, we show how two data visualization tools, funnel charts, and time series fan charts can show the typical volatility in homicide rates in different cities over time. Many of the recent increases are not out of the norm given historical patterns, and so one need not rely on various ex ante hypotheses to explain homicide spikes occurring in many U.S. cities.

Monitoring Volatile Homicide Trends Across U.S. Cities

The recent increase in the national homicide rate in the United States has generated much speculation about its causes among the media. In this article we show how two data visualization tools, funnel charts and time series fan charts, can show the typical volatility in homicide rates in different cities over time. Many of the recent increases are not out of the norm given historical patterns, and so one need not rely on various ex-ante hypotheses to explain recent homicide spikes occurring in some U.S. cities.

UNCERTAINTY AND DENSITY FORECASTS OF ARMA MODELS: COMPARISON OF ASYMPTOTIC, BAYESIAN, AND BOOTSTRAP PROCEDURES

AbstractThe objective of this paper is to analyze the effects of uncertainty on density forecasts of stationary linear univariate ARMA models. We consider three specific sources of uncertainty: parameter estimation, error distribution, and lag order. Depending on the estimation sample size and the forecast horizon, each of these sources may have different effects. We consider asymptotic, Bayesian, and bootstrap procedures proposed to deal with uncertainty and compare their finite sample properties. The results are illustrated constructing fan charts for UK inflation.

How Will Brexit Impact Deposit Balances in the UK

Over the last few years an hypothesis of referendum about the presence in European Union (EU) was emerging in the United Kingdom (UK) based on several political and economic problems around Europe. Nowadays, the EU is the UK’s largest trade partner. For this reason, ‘Brexit’ would lower trade between both parties. The present research will quantify the impacts of ‘Brexit’ in the Monetary Financial Institutions’ (MFI’s) deposits with an asymmetric volatility verified in the currency market. Asymmetric GARCH-family models, such as the TARCH, will be used to modeling markets’ returns that are heavily affected by political events, like the ‘Brexit’. The analysis is subsequently extended to a Holt method for forecasting the Sight Deposits in UK MFI with fan chart. This research considers the amounts outstanding of sterling liabilities in the MFI’s balance sheet (excluding central bank). To determine the uncertainty in the fan charts we will consider the extreme asymmetric volatility in the currency market.

Fan Chart A Tool for NBP's Monetary Policy Making

In this note we describe, in substantial detail, the methodology behind the construction of NBP’s fan chart. This note is meant to help the readers interpret information contained in the mid-term projection, understand the differences in the predicted uncertainty between the projection rounds, and the usefulness of the projection for the monetary policy conduct. We describe the process which leads to the final projection, the methodology of estimation of the variance of the final forecast probability distribution, the method used for quantifying asymmetry of the fan chart and the role the two-piece normal distribution plays in it. Finally, we describe the analysis of the changes in the fan charts between the projection rounds and explain how the narrative associated with the projection is consistent with its assessment of risk.