nep-for New Economics Papers
on Forecasting
Issue of 2018‒01‒01
nine papers chosen by
Rob J Hyndman
Monash University

  1. Forecast Performance in Times of Terrorism By Jonathan Benchimol; Makram El-Shagi
  2. Predicting US Inflation: Evidence from a New Approach By Afees A. Salisu; Kazeem Isah
  3. Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators By Mikosch, Heiner; Solanko, Laura
  4. Does Business Con?dence Matter for Investment? By Hashmat Khan; Santosh Upadhayaya
  5. Forecasting Tourist Arrivals in Prague: Google Econometrics By Zeynalov, Ayaz
  6. A UK financial conditions index using targeted data reduction: forecasting and structural identification By Kapetanios, George; Price, Simon; Young, Garry
  7. A BVAR Model for Forecasting of Czech Inflation By Frantisek Brazdik; Michal Franta
  8. Is CAViaR model really so good in Value at Risk forecasting? Evidence from evaluation of a quality of Value-at-Risk forecasts obtained based on the: GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and the historical simulation models depending on the stability of financial markets By Mateusz Buczyński; Marcin Chlebus
  9. The strong increase of Austrian government debt in the Kreisky era: Austro-Keynesianism or just stubborn forecast errors? By Florian Brugger; Joern Kleinert

  1. By: Jonathan Benchimol; Makram El-Shagi (Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China)
    Abstract: Governments, central banks, and private companies make extensive use of expert and market-based forecasts in their decision-making processes. These forecasts can be affected by terrorism, which should be considered by decision makers. We focus on terrorism, as a mostly endogenously driven form of political uncertainty, and use new econometric tests to assess the forecasting performance of market and professional inflation and exchange-rate forecasts in Israel. We show that expert forecasts are better than market-based forecasts, particularly during periods of terrorism. However, forecasting performance and abilities of both market-based and expert forecasts are significantly reduced during such periods. Thus, policymakers should be particularly attentive to terrorism when considering inflation and exchange-rate forecasts.
    Keywords: inflation, exchange rate, forecast performance, terrorism, market forecast, expert forecast
    JEL: C53 E37 F37 F51
    Date: 2017–12
  2. By: Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan); Kazeem Isah (Centre for Econometric and Allied Research, University of Ibadan)
    Abstract: In this paper, we further subject to empirical scrutiny the conclusion of Stock and Watson (1999) that commodity prices do not improve the traditional Phillips curve-based inflation forecasts. Thus, a multi-predictor framework for US inflation is constructed by augmenting the traditional Phillips curve with symmetric and asymmetric oil price changes. We show that the underlying predictors of US inflation exhibit persistence, endogeneity and conditional heteroscedasticity effects which have implications on forecast performance. Thus, we employ the Westerlund and Narayan (WN hereafter) (2015) estimator which allows for these effects in the predictive model. Also, we follow the linear multi-predictor set-up by Makin et al. (2014) which is an extension of the bivariate predictive model of WN (2015). Thereafter, we extend the former in order to construct a non-linear multi-predictor model that allows for asymmetries based on Shin et al. (2014) approach. Using historical monthly and quarterly data for relevant variables ranging from 1957 to 2017, we demonstrate that the oil price-based augmented Phillips curve will outperform the traditional version if the inherent effects in the predictors are captured in the predictive model. In addition, we also construct a Dynamic Model Averaging version for the augmented Phillips curve, as well as linear time-series models as Autoregressive Integrated Moving Average (ARIMA) and Fractionally Integrated versions (ARFIMA). The WN-based approach is found to outperform the alternative models that ignore the inherent effects. Our results are robust to different measures of inflation, data frequencies and multiple in-sample periods and forecast horizons.
    Keywords: OECD; US, Phillips curve, Asymmetries, Inflation forecasts, Forecast evaluation
    JEL: C53 E31 E37
    Date: 2017–12
  3. By: Mikosch, Heiner; Solanko, Laura
    Abstract: GDP forecasters face tough choices over which leading indicators to follow and which forecasting models to use. To help resolve these issues, we examine a range of monthly indicators to forecast quarterly GDP growth in a major emerging economy, Russia. Numerous useful indicators are identified and forecast pooling of three model classes (bridge models, MIDAS models and unrestricted mixed-frequency models) are shown to outperform simple benchmark models. We further separately examine forecast accuracy of each of the three model classes. Our results show that differences in performance of model classes are generally small, but for the period covering the Great Recession unrestricted mixed-frequency models and MIDAS models clearly outperform bridge models. Notably, the sets of top-performing indicators differ for our two subsample observation periods (2008Q1–2011Q4 and 2012Q1–2016Q4). The best indicators in the first period are traditional real-sector variables, while those in the second period consist largely of monetary, banking sector and financial market variables. This finding supports the notion that highly volatile periods of recession and subsequent recovery are driven by forces other than those that prevail in more normal times. The results further suggest that the driving forces of the Russian economy have changed since the global financial crisis.
    JEL: C53 E27
    Date: 2017–11–30
  4. By: Hashmat Khan (Department of Economics, Carleton University); Santosh Upadhayaya (Department of Economics, Carleton University)
    Abstract: Business con?dence is a well-known leading indicator of future output. Whether it has information about future investment is, however, unclear. In this paper, we determine how informative business con?dence is for investment growth independently of other variables using US business con?dence survey data for 1955Q1–2016Q4. Our main ?ndings are: (i) business con?dence leads US business investment growth by one quarter, and structures investment by two quarters; (ii) business con?dence has predictive ability for investment growth; (iii) remarkably, business con?dence has superior forecasting power, relative to conventional predictors, for investment downturns over 1–3 quarter forecast horizons and for the sign of investment growth over a 2–quarter forecast horizon; and (iv) impulse response analysis reveals that exogenous shifts in business con?dence re?ect short-lived non-fundamental factors, consistent with the ‘animal spirits’ view of investment. Our ?nd-ings have implications for improving investment forecasts, developing new business cycle models, and studying the role of social and psychological factors determining investment growth.
    Keywords: Business con?dence, Investment, Forecasting, Downturns, Directional forecasts
    JEL: C32 E22 E32 E37
    Date: 2017–12–21
  5. By: Zeynalov, Ayaz
    Abstract: It is expected that what people are searching for today is predictive of what they have done recently or will do in the near future. This study analyzes the reliability of Google search data in predicting tourist arrivals and overnight stays in Prague. Three differ- ently weighted weekly Mixed-data sampling (MIDAS) models, ARIMA(1,1,1) with Monthly Google Trends information and model without informative Google Trends variable have been evaluated. The main objective was to assess whether Google Trends information is useful for forecasting tourist arrivals and overnight stays in Prague, as well as whether higher fre- quency data (weekly data) outperform same frequency data methods. The results of the study indicate an undeniable potential that Google Trends offers more accurate forecast- ing, particularly for tourism. The forecasting of the indicators using weekly MIDAS-Beta for tourist arrivals and weekly MIDAS-Almon models for overnight stays outperformed monthly Google Trends using ARIMA and models without Google Trends. The results confirm that predications based on Google searches are advantageous for policy makers and business operating in the tourism sector.
    Keywords: Google trends, Mixed-data sampling, forecasting, tourism
    JEL: C53 E17 L83
    Date: 2017–12–01
  6. By: Kapetanios, George (Kings College, University of London); Price, Simon (Essex Business School, City University and CAMA); Young, Garry (NIESR and CFM)
    Abstract: A financial conditions index (FCI) is designed to summarise the state of financial markets. Two are constructed with UK data. The first is the first principal component of a set of financial indicators. The second comes from a new approach taking information from a large set of macroeconomic variables weighted by the joint covariance with a subset of the financial indicators (a set of spreads), using multivariate partial least squares, again using the first factor. The resulting FCIs are broadly similar. They both have some forecasting power for monthly GDP in a quasi-real-time recursive evaluation from 2011–14 and outperform an FCI produced by Goldman Sachs. A second factor, that may be interpreted as a monetary conditions index, adds further forecast power, while third factors have a mixed effect on performance. The FCIs are used to improve identification of credit supply shocks in an SVAR. The main effects relative to an SVAR excluding an FCI of the (adverse) credit shock IRFs are to make the positive impact on inflation more precise and to reveal an increased positive impact on spreads.
    Keywords: Forecasting; financial conditions index; targeted data reduction; multivariate partial least squares; credit shocks
    JEL: C53
    Date: 2017–12–08
  7. By: Frantisek Brazdik; Michal Franta
    Abstract: Bayesian vector autoregressions (BVAR) have turned out to be useful for medium-term macroeconomic forecasting. Several features of the Czech economy strengthen the rationale for using this approach. These include in particular the short time series available and uncertainty about long-run trends. We compare forecasts based on a small-scale mean-adjusted BVAR with the official forecasts published by the Czech National Bank (CNB) over the period 2008q3-2016q4. The comparison demonstrates that the BVAR approach can provide more precise inflation forecasts over the monetary policy horizon. For other macroeconomic variables, the CNB forecasts either outperform or are comparable with the forecasts based on the BVAR model.
    Keywords: BVAR, forecast evaluation, inflation targeting, real-time forecasting
    JEL: E37 E52
    Date: 2017–11
  8. By: Mateusz Buczyński (Faculty of Economic Sciences, University of Warsaw); Marcin Chlebus (Faculty of Economic Sciences, University of Warsaw)
    Abstract: In the literature, there is no consensus which Value-at-Risk forecasting model is the best for measuring a market risk in banks. In the study an analysis of Value-at-Risk forecasting models quality over varying economic stability periods for main indices from stock exchanges was conducted. The VaR forecasts from GARCH(1,1), GARCH-t(1,1), GARCH-st(1,1), QML-GARCH(1,1), CAViaR and historical simulation models in periods with contrasting volatility trends (increasing, constantly high and decreasing) for countries economically developed (the USA – S&P 500, Germany - DAX and Japan – Nikkei 225) and economically developing (China – SSE COMP, Poland – WIG20 and Turkey – XU100) were compared. The data samples used in the analysis were selected from period 01.01.1999 – 24.03.2017. To assess the VaR forecasts quality: excess ratio, Basel traffic light test, coverage tests (Kupiec test, Christoffersen test), Dynamic Quantile test, cost functions and Diebold-Marino test were used. Obtained results shows that the quality of Value-at-Risk forecasts for the models varies depending on a volatility trend. However, GARCH-st (1,1) and QML-GARCH(1,1) were found as the most robust models to the different volatility periods. The results shows, as well that the CAViaR model forecasts were less appropriate in the increasing volatility period. Moreover, no significant differences for the VaR forecasts quality were found for the developed and developing countries.
    Keywords: risk management, value at risk, GARCH, CAViaR, historical simulation, quality of model assessment
    JEL: G32 C52 C53 C58
    Date: 2017
  9. By: Florian Brugger (University of Graz, Austria); Joern Kleinert (University of Graz, Austria)
    Abstract: In the Kreisky era (1970–1983), Austrian government debts increased strongly. Historically, the attitude of Kreisky and the Social Democrats towards Keynesian fiscal policy measures to fight unemployment during the oil crises has been held to be responsible for the successive budget deficits. Kreisky’s ideological debt policy has become a narrative that has strongly influenced Austrian fiscal policy until today. While this explanation for the strong increase in public debt during the Kreisky era is widely accepted, it is not necessarily true. In this paper, we assess a different explanation: the deficits might simply have resulted from forecast errors of GDP growth in those turbulent times. We find that about one-third of the increase in the debt-over-GDP ratio is directly explained by short-run forecast errors, i.e., the difference between the approved and the realized budget, and an additional one-fifth is the lower bound of forecast error regarding the long-run growth rate.
    Keywords: Fiscal policy; Government debt; Forecast errors; Narrative economics
    JEL: H62 H68 E37 E65
    Date: 2017–12

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