Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach

In this paper a nonparametric approach for estimating mixed‐frequency forecast equations is proposed. In contrast to the popular MIDAS approach that employs an (exponential) Almon or Beta lag distribution, we adopt a penalized least‐squares estimator that imposes some degree of smoothness to the lag distribution. This estimator is related to nonparametric estimation procedures based on cubic splines and resembles the popular Hodrick–Prescott filtering technique for estimating a smooth trend function. Monte Carlo experiments suggest that the nonparametric estimator may provide more reliable and flexible approximations to the actual lag distribution than the conventional parametric MIDAS approach based on exponential lag polynomials. Parametric and nonparametric methods are applied to assess the predictive power of various daily indicators for forecasting monthly inflation rates. It turns out that the commodity price index is a useful predictor for inflations rates 20–30 days ahead with a hump‐shaped lag distribution. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting Inflation Across Euro Area Countries and Sectors: A Panel VAR Approach

In this paper, we adopt a panel vector autoregressive (PVAR) approach to estimating and forecasting inflation dynamics in four different sectors—industry, services, construction and agriculture—across the euro area and its four largest member states: France, Germany, Italy and Spain. By modelling inflation together with real activity, employment and wages at the sectoral level, we are able to disentangle the role of unit labour costs and profit margins as the fundamental determinants of price dynamics on the supply side. In out‐of‐sample forecast comparisons, the PVAR approach performs well against popular alternatives, especially at a short forecast horizon and relative to standard VAR forecasts based on aggregate economy‐wide data. Over longer forecast horizons, the accuracy of the PVAR model tends to decline relative to that of the univariate alternatives, while it remains high relative to the aggregate VAR forecasts. We show that these findings are driven by the event of the Great Recession. Our qualitative results carry over to a multi‐country extension of the PVAR approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting Performance of Nonlinear Models for Intraday Stock Returns

We studied the predictability of intraday stock market returns using both linear and nonlinear time series models. For the S&P 500 index we compared simple autoregressive and random walk linear models with a range of nonlinear models, including smooth transition, Markov switching, artificial neural network, nonparametric kernel regression and support vector machine models for horizons of 5, 10, 20, 30 and 60 minutes. The empirical results indicate that nonlinear models outperformed linear models on the basis of both statistical and economic criteria. Specifically, although return serial correlation receded by around 10 minutes, return predictability still persisted for up to 60 minutes according to nonlinear models, even though profitability decreases as time elapses. More flexible nonlinear models such as support vector machines and artificial neural network did not clearly outperform other nonlinear models. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting US GNP growth: The role of uncertainty

A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.     

Forecasting Interest Rates and Yield Spreads: The Informational Content of Implied Futures Yields and Best-fitting Forward Rate Models

Forecasts of interest rates for different maturities are essential for forecasts of asset prices. The growth of derivatives markets coupled with the development of complex theories of the term structure of interest rates have provided forecasters with a rich array of variables for predicting interest rates and yield spreads. This paper extends previous work on forecasting future interest rates and yield spreads using market data for T-bills, T-Notes, and Treasury Bond spot and futures contracts. The information conveyed in technical models that use market data is also assessed, using a recent innovation in interest rate modelling, the maximum smoothness approach. Forecasts from this model are compared with predicted yields and yield spreads derived from futures prices as well as with those of the random walk model. The results show some evidence of market segmentation, with more arbitrage evident for nearby maturities. Market participants appear to show a greater degree of consensus on short-term interest rates than on longer-term interest rates. There is some indication that forecasts from the futures markets are marginally better than those provided by those of the maximum-smoothness approach, consistent with the informational advantages of futures markets. Finally, futures and maximum-smoothness market forecasts are shown to outperform those of the random walk model.© 1997 John Wiley & Sons, Ltd.     

Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve

We develop a semi‐structural model for forecasting inflation in the UK in which the New Keynesian Phillips curve (NKPC) is augmented with a time series model for marginal cost. By combining structural and time series elements we hope to reap the benefits of both approaches, namely the relatively better forecasting performance of time series models in the short run and a theory‐consistent economic interpretation of the forecast coming from the structural model. In our model we consider the hybrid version of the NKPC and use an open‐economy measure of marginal cost. The results suggest that our semi‐structural model performs better than a random‐walk forecast and most of the competing models (conventional time series models and strictly structural models) only in the short run (one quarter ahead) but it is outperformed by some of the competing models at medium and long forecast horizons (four and eight quarters ahead). In addition, the open‐economy specification of our semi‐structural model delivers more accurate forecasts than its closed‐economy alternative at all horizons. Copyright © 2014 John Wiley & Sons, Ltd.     

Real‐Time Forecasts of Inflation: The Role of Financial Variables

We present a mixed‐frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed‐frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. Copyright © 2012 John Wiley & Sons, Ltd.     

The US Dollar/Euro Exchange Rate: Structural Modeling and Forecasting During the Recent Financial Crises

The paper investigates the determinants of the US dollar/euro within the framework of the asset pricing theory of exchange rate determination, which posits that current exchange rate fluctuations are determined by the entire path of current and future revisions in expectations about fundamentals. In this perspective, we innovate by conditioning on Fama–French and Carhart risk factors, which directly measures changing market expectations about the economic outlook, on new financial condition indexes and macroeconomic variables. The macro‐finance augmented econometric model has a remarkable in‐sample and out‐of‐sample predictive ability, largely outperforming a standard autoregressive specification. We also document a stable relationship between the US dollar/euro Carhart momentum conditional correlation (CCW) and the euro area business cycle. CCW signals a progressive weakening in economic conditions since June 2014, consistent with the scattered recovery from the sovereign debt crisis and the new Greek solvency crisis exploded in late spring/early summer 2015. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting the US Term Structure of Interest Rates Using Nonparametric Functional Data Analysis

In this paper we consider a novel procedure to forecasting the US zero coupon bond yields for a continuum of maturities by using the methodology of nonparametric functional data analysis (NP‐FDA). We interpret the US yields as curves since the term structure of interest rates defines a relation between the yield of a bond and its maturity. Within the NP‐FDA approach, each curve is viewed as a functional random variable and the dynamics present in the sample are modeled without imposing any parametric structure. In order to evaluate forecast the performance of the proposed estimator, we consider forecast horizons h = 1,3,6,12… months and the results are compared with widely known benchmark models. Our estimates with NP‐FDA present predictive performance superior to its competitors in many situations considered, especially for short‐term maturities. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting Simultaneously High‐Dimensional Time Series: A Robust Model‐Based Clustering Approach

This paper considers the problem of forecasting high‐dimensional time series. It employs a robust clustering approach to perform classification of the component series. Each series within a cluster is assumed to follow the same model and the data are then pooled for estimation. The classification is model‐based and robust to outlier contamination. The robustness is achieved by using the intrinsic mode functions of the Hilbert–Huang transform at lower frequencies. These functions are found to be robust to outlier contamination. The paper also compares out‐of‐sample forecast performance of the proposed method with several methods available in the literature. The other forecasting methods considered include vector autoregressive models with ∕ without LASSO, group LASSO, principal component regression, and partial least squares. The proposed method is found to perform well in out‐of‐sample forecasting of the monthly unemployment rates of 50 US states. Copyright © 2013 John Wiley & Sons, Ltd.     

The Relation Between Overreaction in Forecasts and Uncertainty: A Nonlinear Approach

This paper examines overreaction of oil price forecasters. It takes into account impacts of uncertainty, measured by VSTOXX volatility; noisy signals, measured by oil price volatility; and oil price return on forecast changes. The panel smooth transition regression model is applied with different specifications of the transition function to account for nonlinear relations. Data on oil price expectations for different time horizons are taken from the European Central Bank Survey of Professional Forecasters. The results show that forecast changes are governed by overreaction. However, overreaction is markedly reduced when high levels of uncertainty prevail. On the other hand, noisy signals and positive oil price return tend to cause higher overreaction. Copyright © 2016 John Wiley & Sons, Ltd.     

Structural,VAR and BVAR models of exchange rate determination: A comparison of their forecasting performance

This paper compares the out-of-sample forecasting accuracy of a wide class of structural, BVAR and VAR models for major sterling exchange rates over different forecast horizons. As representative structural models we employ a portfolio balance model and a modified uncovered interest parity model, with the latter producing the more accurate forecasts. Proper attention to the long-run properties and the short-run dynamics of structural models can improve on the forecasting performance of the random walk model. The structural model shows substantial improvement in medium-term forecasting accuracy, whereas the BVAR model is the more accurate in the short term. BVAR and VAR models in levels strongly out predict these models formulated in difference form at all forecast horizons.     

Forecasting the Effects of a Canada–US Currency Union on Output and Prices: A Counterfactual Analysis

This paper is a counterfactual analysis investigating the consequences of the formation of a currency union for Canada and the USA: whether outputs increase and prices decrease if these countries form a currency union. We use a two‐country cointegrated model to conduct the counterfactual analysis, where the conditional forecasts are generated based on the Gaussian assumption. To deal with structural breaks and model uncertainty, conditional forecasts are generated from different models/estimation windows and the model‐averaging technique is used to combine the forecasts. We also examine the robustness of our results to parameter uncertainty using the wild bootstrap method. The results show that forming the currency union would probably boost the Canadian economy, whereas it would not have significant effects on US output or Canadian and US price levels. Copyright © 2013 John Wiley & Sons, Ltd.     

Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach

We present and apply singular spectrum analysis (SSA), a relatively new, non‐parametric and data‐driven method for signal extraction (trends, seasonal and business cycle components) and forecasting of UK tourism income. Our results show that SSA slightly outperforms SARIMA and time‐varying‐parameter state space models in terms of root mean square error, mean absolute error and mean absolute percentage error forecasting criteria. Copyright © 2011 John Wiley & Sons, Ltd.     

Predicting US bank failures: A comparison of logit and data mining models

Predicting bank failures is important as it enables bank regulators to take timely actions to prevent bank failures or reduce the cost of rescuing banks. This paper compares the logit model and data mining models in the prediction of bank failures in the USA between 2002 and 2010 using levels and rates of change of 16 financial ratios based on a cross‐section sample. The models are estimated for the in‐sample period 2002–2009, while data for the year 2010 are used for out‐of‐sample tests. The results suggest that the logit model predicts bank failures in‐sample less precisely than data mining models, but produces fewer missed failures and false alarms out‐of‐sample.     

Forecasting Errors,Directional Accuracy and Profitability of Currency Trading: The Case of EUR/USD Exchange Rate

We provide a comprehensive study of out‐of‐sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations, in particular those based on principal components of forecasts, help to improve over benchmark trading strategies, although the excess return per unit of deviation is limited. Copyright © 2016 John Wiley & Sons, Ltd.     

On the Predictive Content of Autoregression Residuals: A Semiparametric,Copula‐Based Approach to Time Series Prediction

This paper proposes an adjustment of linear autoregressive conditional mean forecasts that exploits the predictive content of uncorrelated model residuals. The adjustment is motivated by non‐Gaussian characteristics of model residuals, and implemented in a semiparametric fashion by means of conditional moments of simulated bivariate distributions. A pseudo ex ante forecasting comparison is conducted for a set of 494 macroeconomic time series recently collected by Dees et al. (Journal of Applied Econometrics 2007; 22: 1–38). In total, 10,374 time series realizations are contrasted against competing short‐, medium‐ and longer‐term purely autoregressive and adjusted predictors. With regard to all forecast horizons, the adjusted predictions consistently outperform conditionally Gaussian forecasts according to cross‐sectional mean group evaluation of absolute forecast errors and directional accuracy. Copyright © 2012 John Wiley & Sons, Ltd.     

Univariate Forecasting Comparisons: The Case of the Spanish Automobile Industry

This paper investigates the forecasting ability of unobserved component models, when compared with the standard ARIMA univariate approach. A forecasting exercise is carried out with each method, using monthly time series of automobile sales in Spain. The accuracy of the different methods is assessed by comparing several measures of forecasting performance based on the out-of-sample predictions for various horizons, as well as different assumptions on the models’ parameters. Overall there seems little to choose between the methods in forecasting performance terms but the recursive unobserved component models provide greater flexibility for adaptive applications. © 1997 by John Wiley & Sons, Ltd.     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach

This paper applies the GARCH‐MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short‐term and long‐term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low‐frequency macroeconomic information in the GARCH‐MIDAS model improves the prediction ability of the model, particularly for the long‐term variance component. Moreover, the GARCH‐MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle. Copyright © 2013 John Wiley & Sons, Ltd.