nep-for New Economics Papers
on Forecasting
Issue of 2015‒04‒25
twenty-two papers chosen by
Rob J Hyndman
Monash University

  1. Real-time forecasting with a MIDAS VAR By Mikosch, Heiner; Neuwirth , Stefan
  2. Nowcasting and Forecasting Economic Growth in the Euro Area using Principal Components By Irma Hindrayanto; Siem Jan Koopman; Jasper de Winter
  3. Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP By Tóth, Peter
  4. Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice By Laurent Callot; Anders B. Kock; Marcelo C. Medeiros
  5. Forecasting Earnings Forecasts By Bert de Bruijn; Philip Hans Franses
  6. Improving Density Forecasts and Value-at-Risk Estimates by Combining Densities By Anne Opschoor; Dick van Dijk; Michel van der Wel
  7. Forecasting Co-Volatilities via Factor Models with Asymmetry and Long Memory in Realized Covariance By Manabu Asai; Michael McAleer
  8. How Informative are the Unpredictable Components of Earnings Forecasts? By Bert de Bruijn; Philip Hans Franses
  9. An adaptive approach to forecasting three key macroeconomic variables for transitional China By Niu, Linlin; Xua , Xiu; Chen , Ying
  10. The Forecast Combination Puzzle: A Simple Theoretical Explanation By Gerda Claeskens; Jan Magnus; Andrey Vasnev; Wendun Wang
  11. Score Driven exponentially Weighted Moving Average and Value-at-Risk Forecasting By André Lucas; Xin Zhang
  12. Forecasting the term structure of crude oil futures prices with neural networks By Jozef Barunik; Barbora Malinska
  13. Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices By Eran Raviv; Kees E. Bouwman; Dick van Dijk
  14. Realized Volatility Risk By David E. Allen; Michael McAleer; Marcel Scharth
  15. Inflation Forecasting and the Distribution of Price Changes By Sartaj Rasool Rather; Sunil Paul; S. Raja Sethu Durai
  16. On the calculation of safety stocks By Syntetos, A.A.; Teunter, R.H.
  17. Interpreting Financial Market Crashes as Earthquakes: A New early Warning System for Medium Term Crashes By Francine Gresnigt; Erik Kole; Philip Hans Franses
  18. Combined Density Nowcasting in an Uncertain Economic Environment By Knut Are Aastveit; Francesco Ravazzolo; Herman K. van Dijk
  19. Indeterminacy, Misspecification and Forecastability: Good Luck in Bad Policy? By Luca Fanelli; Marco M. Sorge
  20. The Impact of Jumps and Leverage in Forecasting Co-Volatility By Manabu Asai; Michael McAleer
  21. Future world market prices of milk and feed looking into the crystal ball By Hansen, Bjørn Gunnar; Li, Yushu
  22. Forecast Uncertainty and the Taylor Rule By Christian Bauer; Matthias Neuenkirch

  1. By: Mikosch, Heiner (BOFIT); Neuwirth , Stefan (BOFIT)
    Abstract: This paper presents a MIDAS type mixed frequency VAR forecasting model. First, we propose a general and compact mixed frequency VAR framework using a stacked vector approach. Second, we integrate the mixed frequency VAR with a MIDAS type Almon lag polynomial scheme which is designed to reduce the parameter space while keeping models fexible. We show how to recast the resulting non-linear MIDAS type mixed frequency VAR into a linear equation system that can be easily estimated. A pseudo out-of-sample forecasting exercise with US real-time data yields that the mixed frequency VAR substantially improves predictive accuracy upon a standard VAR for dierent VAR specications. Forecast errors for, e.g., GDP growth decrease by 30 to 60 percent for forecast horizons up to six months and by around 20 percent for a forecast horizon of one year.
    Keywords: Forecasting; mixed frequency data; MIDAS; VAR; real time
    JEL: C53 E27
    Date: 2015–04–13
  2. By: Irma Hindrayanto (De Nederlandsche Bank); Siem Jan Koopman (VU University Amsterdam, the Netherlands); Jasper de Winter (De Nederlandsche Bank, the Netherlands)
    Abstract: Many empirical studies have shown that factor models produce relatively accurate forecasts compared to alternative short-term forecasting models. These empirical findings have been established for different macroeconomic data sets and different forecast horizons. However, various specifications of the factor model exist and it is a topic of debate which specification is most effective in its forecasting performance. Furthermore, the forecast performances of the different specifications during the recent financial crisis are also not well documented. In this study we investigate these two issues in depth. We empirically verify the forecast performance of three factor model approaches and report our findings in an extended empirical out-of-sample forecasting competition for quarterly growth of gross domestic product in the euro area and its five largest countries over the period 1992-2012. We also introduce two extensions of existing factor models to make them more suitable for real-time forecasting. We show that the factor models have been able to systematically beat the benchmark autoregressive model, both before as well as during the financial crisis. The recently proposed collapsed dynamic factor model shows the highest forecast accuracy for the euro area and the majority of countries that we have analyzed. The forecast precision improvements against the benchmark model can range up to 77% in mean square error reduction, depending on the country and forecast horizon.
    Keywords: Factor models, Principal component analysis, Forecasting, Kalman filter, State space method, Publication lag, Mixed frequency
    JEL: C32 C53 E17
    Date: 2014–08–22
  3. By: Tóth, Peter
    Abstract: In this article we estimate a small dynamic factor model (DFM) for the short-term forecasting of Slovak GDP. The model predicts the developments of real activity in the next two quarters on the basis of monthly data, which are published earlier than GDP. The regular release of various monthly indicators allows about a weekly update of the short-term outlook. Our DFM contains six monthly indicators, which are retail sales, sales in industry and construction, employment in selected industries, health care contributions of employers, export and the PMI for the eurozone. These approximate the production, expenditure and income side of GDP. The forecast accuracy of the factor model prevails over simple approaches not relying on monthly data, such as the random walk and the autoregressive models of the GDP series.
    Keywords: dynamic factor model, GDP, short-term forecasting
    JEL: C52 C53 E23 E27
    Date: 2014–10–02
  4. By: Laurent Callot (VU University Amsterdam, the Netherlands); Anders B. Kock (Aarhus University, Denmark); Marcelo C. Medeiros (Pontifical Catholic University of Rio de Janeiro, Brasil)
    Abstract: In this paper we consider modeling and forecasting of large realized covariance matrices by penalized vector autoregressive models. We propose using Lasso-type estimators to reduce the dimensionality to a manageable one and provide strong theoretical performance guarantees on the forecast capability of our procedure. To be precise, we show that we can forecast future realized covariance matrices almost as precisely as if we had known the true driving dynamics of these in advance. We next investigate the sources of these driving dynamics for the realized covariance matrices of the 30 Dow Jones stocks and find that these dynamics are not stable as the data is aggregated from the daily to the weekly and monthly frequency. The theoretical performance guarantees on our forecasts are illustrated on the Dow Jones index. In particular, we can beat our benchmark by a wide margin at the longer forecast horizons. Finally, we investigate the economic value of our forecasts in a portfolio selection exercise and find that in certain cases an investor is willing to pay a considerable amount in order get access to our forecasts.
    Keywords: Realized covariance, vector autoregression, shrinkage, Lasso, forecasting, portfolio allocation
    JEL: C22
    Date: 2014–11–13
  5. By: Bert de Bruijn (Erasmus University Rotterdam); Philip Hans Franses (Erasmus University Rotterdam)
    Abstract: We analyze earnings forecasts retrieved from the I/B/E/S database concerning 596 firms for the sample 1995 to 2011, with a specific focus on whether these earnings forecasts can be predicted from available data. Our main result is that earnings forecasts can be predicted quite accurately using publicly available information. Second, we show that earnings forecasts that are less predictable are also less accurate. We also show that earnings forecasters who quote forecasts that are too extreme need to correct these as the earnings announcement approaches. Finally, we show that the unpredictable component of earnings forecasts can contain information which we can use to improve the forecasts.
    Keywords: Earnings Forecasts, Earnings Announcements, Financial Markets, Financial Analysts.
    JEL: G17 G24 M41
    Date: 2013–08–22
  6. By: Anne Opschoor (VU University Amsterdam); Dick van Dijk (Erasmus University Rotterdam); Michel van der Wel (Erasmus University Rotterdam)
    Abstract: We investigate the added value of combining density forecasts for asset return prediction in a specific region of support. We develop a new technique that takes into account model uncertainty by assigning weights to individual predictive densities using a scoring rule based on the censored likelihood. We apply this approach in the context of recently developed univariate volatility models (including HEAVY and Realized GARCH models), using daily returns from the S&P 500, DJIA, FTSE and Nikkei stock market indexes from 2000 until 2013. The results show that combined density forecasts based on the censored likelihood scoring rule significantly outperform pooling based on the log scoring rule and individual density forecasts. The same result, albeit less strong, holds when compared to combined density forecasts based on equal weights. In addition, VaR estimates improve a t the short horizon, in particular when compared to estimates based on equal weights or to the VaR estimates of the individual models.
    Keywords: Density forecast evaluation, Volatility modeling, Censored likelihood, Value-at-Risk
    JEL: C53 C58 G17
    Date: 2014–07–21
  7. By: Manabu Asai (Soka University, Japan); Michael McAleer (National Tsing Hua University, Taiwan; Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, and Tinbergen Institute, the Netherlands; Complutense University of Madrid, Spain)
    Abstract: Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory. Using the basic structure of the fMSV model, the authors extend the dynamic correlation MSV model, the conditional/stochastic Wishart autoregressive models, the matrix-exponential MSV model, and the Cholesky MSV model. Empirical results for 7 financial asset returns for US stock returns indicate that the new fMSV models outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the Cholesky MSV model with long memory and asymmetry shows stable and better forecasting performance for one-day, five-day and ten-day horizons in the periods before, during and after the global financial crisis.
    Keywords: Dimension reduction; Factor Model; Multivariate Stochastic Volatility; Leverage Effects; Long Memory; Realized Volatility.
    JEL: C32 C53 C58 G17
    Date: 2014–03–17
  8. By: Bert de Bruijn (Erasmus School of Economics, Erasmus University Rotterdam, the Netherlands); Philip Hans Franses (Erasmus School of Economics, Erasmus University Rotterdam, the Netherlands)
    Abstract: An analysis of about 300000 earnings forecasts, created by 18000 individual forecasters for earnings of over 300 S&P listed firms, shows that these forecasts are predictable to a large extent using a statistical model that includes publicly available information. When we focus on the unpredictable components, which may be viewed as the personal expertise of the earnings forecasters, we see that small adjustments to the model forecasts lead to more forecast accuracy. Based on past track records, it is possible to predict the future track record of individual forecasters.
    Keywords: Earnings Forecasts, Earnings Announcements, Financial Markets, Financial Analysts
    JEL: G17 G24 M41
    Date: 2015–03–06
  9. By: Niu, Linlin (BOFIT); Xua , Xiu (BOFIT); Chen , Ying (BOFIT)
    Abstract: We propose the use of a local autoregressive (LAR) model for adaptive estimation and forecasting of three of China’s key macroeconomic variables: GDP growth, inflation and the 7-day interbank lending rate. The approach takes into account possible structural changes in the data-generating process to select a local homogeneous interval for model estimation, and is particularly well-suited to a transition economy experiencing ongoing shifts in policy and structural adjustment. Our results indicate that the proposed method outperforms alternative models and forecast methods, especially for forecast horizons of 3 to 12 months. Our 1-quarter ahead adaptive forecasts even match the performance of the well-known CMRC Langrun survey forecast. The selected homogeneous intervals indicate gradual changes in growth of industrial production driven by constant evolution of the real economy in China, as well as abrupt changes in interestrate and inflation dynamics that capture monetary policy shifts.
    Keywords: Chinese economy; local parametric models; forecasting
    JEL: E43 E47
    Date: 2015–04–10
  10. By: Gerda Claeskens (KU Leuven, Belgium); Jan Magnus (VU University Amsterdam, the Netherlands); Andrey Vasnev (University of Sydney, Australia); Wendun Wang (Erasmus University, Rotterdam, the Netherlands)
    Abstract: This papers offers a theoretical explanation for the stylized fact that forecast combinations with estimated optimal weights often perform poorly in applications. The properties of the forecast combination are typically derived under the assumption that the weights are fixed, while in practice they need to be estimated. If the fact that the weights are random rather than fixed is taken into account during the optimality derivation, then the forecast combination will be biased (even when the original forecasts are unbiased) and its variance is larger than in the fixed-weights case. In particular, there is no guarantee that the 'optimal' forecast combination will be better than the equal-weights case or even improve on the original forecasts. We provide the underlying theory, some special cases and an application in the context of model selection.
    Keywords: forecast combination, optimal weights, model selection
    JEL: C53 C52
    Date: 2014–09–19
  11. By: André Lucas (VU University Amsterdam); Xin Zhang (Sveriges Riksbank, Sweden)
    Abstract: We present a simple new methodology to allow for time variation in volatilities using a recursive updating scheme similar to the familiar RiskMetrics approach. We update parameters using the score of the forecasting distribution rather than squared lagged observations. This allows the parameter dynamics to adapt automatically to any non-normal data features and robustifies the subsequent volatility estimates. Our new approach nests several extensions to the exponentially weighted moving average (EWMA) scheme as proposed earlier. Our approach also easily handles extensions to dynamic higher-order moments or other choices of the preferred forecasting distribution. We apply our method to Value-at-Risk forecasting with Student's t distributions and a time varying degrees of freedom parameter and show that the new method is competitive to or better than earlier methods for volatility forecasting of individual stock returns and exchange rates.
    Keywords: dynamic volatilities, time varying higher order moments, integrated generalized autoregressive score models, Exponential Weighted Moving Average (EWMA), Value-at-Risk (VaR)
    JEL: C51 C52 C53 G15
    Date: 2014–07–22
  12. By: Jozef Barunik; Barbora Malinska
    Abstract: The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month, 3-month, 6-month and 12-month-ahead forecasts obtained from focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.
    Date: 2015–04
  13. By: Eran Raviv (Erasmus University Rotterdam); Kees E. Bouwman (Erasmus University Rotterdam); Dick van Dijk (Erasmus University Rotterdam)
    Abstract: The daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average price. Multivariate models for the full panel of hourly prices significantly outperform univariate models of the daily average price, with reductions in Root Mean Squared Error of up to 16%. Substantial care is required in order to achieve these forecast improvements. Rich multivariate models are needed to exploit the relations between different hourly prices, but the risk of overfitting must be mitigated by using dimension reduction techniques, shrinkage and forecast combinations.
    Keywords: Electricity market, Forecasting, Hourly prices, Dimension reduction, Shrinkage, Forecast combinations
    JEL: C53 C32 Q47
    Date: 2013–05–17
  14. By: David E. Allen (Edith Cowan University, Australia); Michael McAleer (Erasmus University Rotterdam, and Complutense University of Madrid); Marcel Scharth (University of New South Wales, Australia)
    Abstract: This discussion paper led to an article in the <I>Journal of Risk and Financial Management</I> (2014). Volume 7(2), pages 80-109.<P> In this paper we document that realized variation measures constructed from highfrequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility model, which incorporates the fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
    Keywords: Realized volatility, volatility of volatility, volatility risk, value-at-risk, forecasting,
    JEL: C14 C22 C58 G15
    Date: 2013–07–16
  15. By: Sartaj Rasool Rather (Madras School of Economics); Sunil Paul (Madras School of Economics); S. Raja Sethu Durai (Madras School of Economics)
    Abstract: This study shows that replacing the traditional measure of asymmetry that is skewness in the inflation forecasting model with an alternative asymmetry measure that captures the joint influence of both skewness and variance on inflation significantly improves the forecast at various horizons. The empirical evidence suggests that it is more appropriate to use such measure of asymmetry in inflation forecast model as it has edge over simple measure of skewness in predicting inflation. These findings are consistent with the prediction of menu cost model that the variance of cross sectional distribution of relative price changes amplifies the impact of skewness on inflation.
    Keywords: skewness, relative price changes, asymmetry, inflation forecasting
    JEL: E30 E31 E52
    Date: 2015–03
  16. By: Syntetos, A.A.; Teunter, R.H. (Groningen University)
    Abstract: In forecasting and inventory control textbooks and software applications, the variance of the cumulative lead-time forecast error is, almost invariably, taken as the sum of the error variances of the individual forecast intervals. For stationary demand and a constant lead time, this implies multiplying the single period variance (or Mean Squared Error) by the lead-time. This standard approach is shown in this paper to always underestimate the true lead-time demand variability, resulting in too low safety stocks and poor service. For two of the most widely applied forecasting techniques (Single Exponential Smoothing and Simple Moving Average) we present corrected expressions and show that the error in the standard approach is often considerable. The same fundamental problem exists for all forecasting techniques and all demand processes, and so this issue deserves wider recognition and offers ample opportunities for further research.
    Date: 2014
  17. By: Francine Gresnigt; Erik Kole; Philip Hans Franses (Erasmus University Rotterdam, the Netherlands)
    Abstract: We propose a modeling framework which allows for creating probability predictions on a future market crash in the medium term, like sometime in the next five days. Our framework draws upon noticeable similarities between stock returns around a financial market crash and seismic activity around earthquakes. Our model is incorporated in an Early Warning System for future crash days. Testing our EWS on S&P 500 data during the recent financial crisis, we find positive Hanssen-Kuiper Skill Scores. Furthermore our modeling framework is capable of exploiting information in the returns series not captured by well known and commonly used volatility models. EWS based on our models outperform EWS based on the volatility models forecasting extreme price movements, while forecasting is much less time-consuming.
    Keywords: Financial crashes, Hawkes process, self-exciting process, Early Warning System
    JEL: C13 C15 C53 G17
    Date: 2014–06–03
  18. By: Knut Are Aastveit (Norges Bank, Norway); Francesco Ravazzolo (Norges Bank, and BI Norwegian Business School, Norway); Herman K. van Dijk (Erasmus University Rotterdam, and VU University Amsterdam, the Netherlands)
    Abstract: We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian Sequential Monte Carlo method which re-balances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on US real-time data of 120 leading indicators, indicate that CDN gives more accurate density nowc asts of US GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two first months of the quarter. CDN also provides informative signals on model incompleteness during recent recessions. Focusing on the tails, CDN delivers probabilities of negative growth, that provide good signals for calling recessions and ending economic slumps in real time.
    Keywords: Density forecast combination; Survey forecast; Bayesian Filtering; Sequential Monte Carlo Nowcasting, Real-time Data
    JEL: C11 C13 C32 C53 E37
    Date: 2014–12–09
  19. By: Luca Fanelli (University of Bologna); Marco M. Sorge (University of Göttingen and CSEF)
    Abstract: A recent debate in the forecasting literature revolves around the inability of macroecono-metric models to improve on simple univariate predictors, since the onset of the so-called Great Moderation. This paper explores the consequences of equilibrium indeterminacy for quantitative forecasting through standard reduced form forecast models. Exploiting U.S. data on both the Great Moderation and the preceding era, we first present evidence that (i) higher (absolute) forecastability obtains in the former rather than the latter period for all models considered, and that (ii) the decline in volatility and persistence captured by a .nite-order VAR system across the two samples is not associated with inferior (absolute or relative) predictive accuracy. Then, using a small-scale New Keynesian monetary DSGE model as laboratory, we generate arti.cial datasets under either equilibrium regime and investigate numerically whether (relative) forecastability is improved in the presence of indeterminacy. It is argued that forecasting under indeterminacy with e.g. unrestricted VAR models entails misspecification issues that are generally more severe than those one typically faces under determinacy. Irrespective of the occurrence of non-fundamental (sunspot) noise, for certain values of the arbitrary parameters governing solution multiplicity, the pseudo out-of-sample VAR-based forecasts of in.ation and output growth can outperform simple univariate predictors. For other values of these parameters, by contrast, the opposite occurs. In general, it is not possible to establish a one-to-one relationship between indeterminacy and superior forecastability, even when sunspot shocks play no role in generating the data. Overall, our analysis points towards a 'good luck in bad policy' explanation of the (relative) higher forecastability of macroeconometric models prior to the Great Moderation period.
    Keywords: DSGE, Forecasting, Indeterminacy, Misspecification, VAR system
    JEL: C53 C62 E17
    Date: 2015–04–19
  20. By: Manabu Asai (Soka University, Japan); Michael McAleer (National Tsing Hua University, Taiwan, Erasmus University Rotterdam, the Netherlands, Complutense University of Madrid, Spain)
    Abstract: The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the jump-robust two time scale covariance estimator of Boudt and Zhang (2013)such that the estimated matrix is positive definite. Using this approach we can disentangle the estimates of the integrated co-volatility matrix and jump variations from the quadratic covariation matrix. Empirical results for three stocks traded on the New York Stock Exchange indicate that the co-jumps of two assets have a significant impact on future co-volatility, but that the impact is negligible for forecasting weekly and monthly horizons.
    Keywords: Co-Volatility; Forecasting; Jump; Leverage Effects; Realized Covariance; Threshold
    JEL: C32 C53 C58 G17
    Date: 2015–02–09
  21. By: Hansen, Bjørn Gunnar (TINE SA, Oslo, Norway); Li, Yushu (Dept. of Business and Management Science, Norwegian School of Economics)
    Abstract: Both the world milk price and the world feed price have become more volatile during the last 7-8 years. The ability of dairy farmers to adapt quickly to these changing circumstances will be a key driver for future success, considering that feed is the major cost component in milk production and that the milk market is highly volatile. This development has increased the need for research on price dynamics and price forecasting. The first aim of this paper is to apply the wavelet multi-resolution analysis (MRA) to investigate the cyclical dynamics embedded in and between the world milk and feed prices. Second, the aim is to explore both the long and short interactions and the impulse response functions (IRF) between the two price series in the system of a vector error correction model (VECM). Third, the aim is to produce reliable forecasts for both the milk and the feed price applying a SARIMA model, a VECM model and wavelet MRA. We collected the world milk price and the world feed price series from 2002 to 2015 from the International Farm Comparison Network (IFCN). The analysis revealed that the two price series contain business cycles of approximately 32 months. Further, the two series share a long-run relationship, they are co-integrated, with the feed price as the leading variable. The results also revealed that a combination of different forecasting models can provide reasonably good forecasts of both prices for a period of one year ahead.
    Keywords: Agricultural economics; forecast; wavelet MRA; VECM
    JEL: C02 C12 C22 Q13
    Date: 2015–04–10
  22. By: Christian Bauer; Matthias Neuenkirch
    Abstract: In this paper, we derive a modification of a forward-looking Taylor rule, which integrates two variables measuring the uncertainty of inflation and GDP growth forecasts into an otherwise standard New Keynesian model. We show that certainty-equivalence in New Keynesian models is a consequence of log-linearization and that a second-order Taylor approximation leads to a reaction function which includes the uncertainty of macroeconomic expectations. To test the model empirically, we use the standard deviation of individual forecasts around the median Consensus Forecast as proxy for forecast uncertainty. Our sample covers the euro area, Sweden, and the United Kingdom and the period 1992Q4-2014Q2. We find that while all three central banks react significantly to inflation forecast uncertainty by reducing their policy rates in times of higher inflation expectation uncertainty with an average effect of more than 25 basis points, they do not have significant reactions to GDP growth forecast uncertainty. We conclude with some implications for optimal monetary policy rules and central bank watchers.
    Keywords: Certainty-Equivalence, Consensus Forecasts, Forecast Uncertainty, Global Financial Crisis, Optimal Monetary Policy, Taylor Rule
    JEL: E52 E58
    Date: 2015

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