nep-for New Economics Papers
on Forecasting
Issue of 2018‒03‒26
eight papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting tax revenues in an emerging economy: The case of Albania By Sabaj, Ernil; Kahveci, Mustafa
  2. Explaining and Forecasting Euro Area Inflation: the Role of Domestic and Global Factors By S. Béreau; V. Faubert; K. Schmidt
  3. Exchange rate volatility: A forecasting approach of using the ARCH family along with ARIMA SARIMA and semi-structural-SVAR in Turkey. By Ganbold, Batzorig; Akram, Iqra; Fahrozi Lubis, Raisal
  4. Reassessing the information content of the Commitments of Traders positioning data for exchange rate changes By Nicholas Mulligan; Daan Steenkamp
  5. Multivariate Periodic Stochastic Volatility Models: Applications to Algerian dinar exchange rates and oil prices modeling By Nadia Boussaha; Faycal Hamdi; Saïd Souam
  6. Volatility in equity markets and monetary policy rate uncertainty By Kaminska, Iryna; Roberts-Sklar, Matt
  7. Dismiss the Gap? A Real-Time Assessment of the Usefulness of Canadian Output Gaps in Forecasting Inflation By Lise Pichette; Marie-Noëlle Robitaille; Mohanad Salameh; Pierre St-Amant
  8. Modeling and forecasting commodity market volatility with long-term economic and financial variables By Nguyen, Duc Khuong; Walther, Thomas

  1. By: Sabaj, Ernil; Kahveci, Mustafa
    Abstract: Fiscal balance is one of the main concerns of fiscal policy. Although academic and political choices on budget deficit vary due to perspective differences, improving the quality of revenue and expenditure forecasting has become prominent. The seminal researches on this topic present that tax revenue forecasts suffer from high positive biases. As tax forecasts have chain implications on the expenditures side as well, this might lead to high unexpected deficits. According to the IMF 2016 country report on Albania, emerging market economies are suffering higher than advanced ones in tax revenue forecasting. The aim of this paper is to implement new forecasting models and to apply forecast combinations for Albania, where forecast errors are higher than average. The estimation results show that influence of internal and external factors on tax revenue forecasting create a significant improvement on tax revenue accuracy. The estimations and forecast combinations of this paper perform lower errors than official forecasts, which indicate that revision of tax forecasting methodology can increase the accuracy of predictions for emerging market economies.
    Keywords: Tax revenue, Forecasting, Combination, Emerging market
    JEL: C52 C53 E27 E6 E62 H68
    Date: 2018–02–07
  2. By: S. Béreau; V. Faubert; K. Schmidt
    Abstract: In this paper, we study the fit and the predictive performance of the Phillips curve for euro area inflation with regard to different inflation series, time periods and predictor variables, notably different global factors. We compare the relative performance of a large set of alternative global factors in the Phillips curve, such as commodity prices, import prices, global consumer inflation, global economic slack and foreign demand. We find that traditional global indicators such as oil prices and import prices provide more accurate information for euro area headline inflation than global slack measures. In what regards the forecast ability of the Phillips curve for headline inflation, we show that it is unstable and depends strongly on the time period. Global factors provide only limited additional information for forecasting. In addition, we explore whether domestic demand and global factors are useful for analysing the entire conditional distribution of euro area inflation. We find that their impact varies across inflation quantiles (low vs. high inflation) and that inflation is more persistent at the low end of the distribution. We provide evidence that quantile information can lead to more accurate forecasts in periods of persistently low inflation.
    Keywords: Inflation; Forecasting; Phillips curve; Quantile regression.
    JEL: E31 E37 C22 C53
    Date: 2018
  3. By: Ganbold, Batzorig; Akram, Iqra; Fahrozi Lubis, Raisal
    Abstract: The ability to predict the volatility of Exchange rate is an enormous challenge when it comes to economic and financial considerations. In this context, it is important to be able to predict the exchange rate volatility in financial markets and the world economy. This paper proposes a heightened approach to modeling and forecasting of exchange rate volatility in Turkey. For past recent years, Turkey experienced political turbulence that the possibility of effecting exchange rate, thus create uncertainty volatility of exchange rate. Therefore daily exchange rate data have been taken from 2005-2017 and applied autoregressive conditional heteroskedasticity ARCH and GARCH families (EGARCH, IGARCH, and PARCH) to forecast exchange rate volatility. The proposed methodology able to calculate the breakpoint by including dummy variables. The result is more confined after including dummy that EGARCH (1,1) is best performing to forecast exchange rate volatility and successfully overcome the leverage effect on the exchange rate. Moreover, this paper also investigates the monthly data forecasting by applying ARIMA SARIMA along with SVAR technique for next few months. And Exchange rate pass-through also encounter it, which indicates the pass-through is more pronounced in PPI than CPI. The forecast result of SARIMA and SVAR distribute the same direction of fluctuation in the exchange rate that is declining of the current exchange rate in the future. However, ARIMA’s forecast tends to increase and different with two models.
    Keywords: Exchange rate, Volatility, Forecast, SVAR
    JEL: F31 F37
    Date: 2017
  4. By: Nicholas Mulligan; Daan Steenkamp (Reserve Bank of New Zealand)
    Abstract: There is a large literature focused on exchange rate forecasting. A common finding is that it is difficult to systematically predict exchange rate movements, consistent with the results of Meese and Rogoff (1983), who showed that it is difficult to beat a random walk forecast. This note heads down the same well-worn path, but assesses the usefulness of the Commitments of Traders (COT) positioning data for understanding and predicting exchange rate movements. COT data has been used extensively in research assessing the impact of speculation in commodity markets, but there has been comparatively little recent research into the usefulness of this data for thinking about foreign exchange market developments. COT data also provides classifications of trading entities that allow the trading behaviour of different groups of traders (such as hedgers and speculators), and their respective market impact, to be examined. This note focuses on the positioning of speculative traders who are thought to express their beliefs of future currency movements through futures positions. The COT data is available at weekly frequency and aggregates holdings of futures in key US markets. The lag between the collection of the COT data and its publication may in fact limit its information content. For example, assuming markets are informationally efficient, new information is expected to be quickly incorporated into spot exchange rates and futures positions at the same time. Therefore, the data cannot provide insight as to whether exchange rates change contemporaneously as futures positions are opened or closed (in real-time). The key question examined in this note is what the information content of COT positioning data is for major currencies, and at what horizon is positioning data best for forecasting exchange rate changes. As has been found by earlier studies, our results suggest that futures positioning data can help with interpretation of historical exchange rate changes, although its use as a predictor of exchange rates is limited. However, higher frequency positioning data, such as hourly or daily data, may in fact have predictive power.
    Date: 2018–03
  5. By: Nadia Boussaha; Faycal Hamdi; Saïd Souam
    Abstract: The contribution of this paper is twofold. In a first step, we propose the so called Periodic Multivariate Autoregressive Stochastic Volatility (PV ARSV) model, that allows the Granger causality in volatility in order to capture periodicity in stochastic conditional variance. After a thorough discussion, we provide some probabilistic properties of this class of models. We thus propose two methods for the estimation problem, one based on the periodic Kalman filter and the other on the particle filter and smoother with Expectation-Maximization (EM) algorithm. In a second step, we propose an empirical application by modeling oil price and three exchange rates time series. It turns out that our modeling gives very accurate results and has a well volatility forecasting performance.
    Keywords: Multivariate periodic stochastic volatility; periodic stationarity; periodic Kalman filter; particle filtering; exchange rates; Saharan Blend oil.
    JEL: C32 C53 F31 G17
    Date: 2018
  6. By: Kaminska, Iryna (Bank of England); Roberts-Sklar, Matt (Bank of England)
    Abstract: Asset pricing models assume the risk-free rate to be a key factor for equity prices. Hence, there should be a strong link between monetary policy rate uncertainty and equity return volatility, both in theory and data. This paper uses regression-based projections for realized variance to examine the relationship between short horizon forecasts of equity variance and proxies for monetary policy rate uncertainty. By assessing various projection models for UK, US and euro-area equity indices, we show that the proxies for monetary policy rate uncertainty have a significant and positive predictive power for the equity return variance. Adding monetary policy rate uncertainty variables can significantly improve forecasting models for equity variance and volatility at weekly, monthly and even quarterly horizons. The findings imply that market views of short-term interest rate developments may indeed be embedded in equity prices and their variations.
    Keywords: Equity indices; monetary policy rate uncertainty; option implied volatility; realized volatility; risk-free interest rates; volatility forecasting
    JEL: C22 C52 E52 G12
    Date: 2017–12–21
  7. By: Lise Pichette; Marie-Noëlle Robitaille; Mohanad Salameh; Pierre St-Amant
    Abstract: We use a new real-time database for Canada to study various output gap measures. This includes recently developed measures based on models incorporating many variables as inputs (and therefore requiring real-time data for many variables). We analyze output gap revisions and assess the usefulness of these gaps in forecasting total CPI inflation and three newly developed measures of core CPI inflation: CPI-median, CPI-trim and CPI-common. We also study whether labour-input gaps, projected output gaps, and simple combinations of output gaps can add useful information for forecasting inflation. We find that estimates of excess capacity (the extent to which the economy is below potential) were probably too large around the 2008-2009 recession, as they subsequently tended to be revised down. In addition, we find that, when forecasting CPI-common and CPI-trim, some gaps appear to provide information that reduces forecast errors when compared with models that use only lags of inflation. However, forecast improvements are rarely statistically significant. In addition, we find little evidence of the usefulness of output gaps for forecasting inflation measured by total CPI and CPI-median.
    Keywords: Econometric and statistical methods, Inflation and prices, Potential output
    JEL: C53 E37
    Date: 2018
  8. By: Nguyen, Duc Khuong; Walther, Thomas
    Abstract: This paper investigates the time-varying volatility patterns of some major commodities as well as the potential factors that drive their long-term volatility component. For this purpose, we make use of a recently proposed GARCH-MIDAS approach which typically allows us to examine the role of economic and financial variables of different frequencies. Using commodity futures for crude oil (WTI and Brent), gold, silver and platinum, our results show the necessity of disentangling the short- and long-term components in modeling and forecasting commodity volatility. They also indicate that the long-term volatility of most commodity futures is significantly driven by the level of the general real economic activity as well as the changes in consumer sentiment, industrial production, and economic policy uncertainty. However, the forecasting results are not alike across commodity futures as no single model fits all commodities.
    Keywords: Commodity futures, GARCH,Long-term volatility, Macroeconomic effects, Mixed data sampling.
    JEL: C52 C53 G17
    Date: 2017–05

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