nep-for New Economics Papers
on Forecasting
Issue of 2017‒02‒26
six papers chosen by
Rob J Hyndman
Monash University

  1. Joint Forecast Combination of Macroeconomic Aggregates and Their Components By Cobb, Marcus P A
  2. Aggregate Density Forecasting from Disaggregate Components Using Large VARs By Cobb, Marcus P A
  3. Exchange rate prediction redux: new models, new data, new currencies By Cheung, Yin-Wong; Chinn, Menzie D.; Garcia Pascual, Antonio; Zhang, Yi
  4. Macroeconomic Uncertainty Through the Lens of Professional Forecasters By Jo, Soojin; Sekkel, Rodrigo
  5. Estimation for the Prediction of Point Processes with Many Covariates By Alessio Sancetta
  6. Macroeconomic Forecasting in Times of Crises By Pablo Guerron-Quintana; Molin Zhong

  1. By: Cobb, Marcus P A
    Abstract: This paper presents a framework that extends forecast combination to include an aggregate and its components in the same process. This is done with the objective of increasing aggregate forecasting accuracy by using relevant disaggregate information and increasing disaggregate forecasting accuracy by providing a binding context for the component’s forecasts. The method relies on acknowledging that underlying a composite index is a well defined structure and its outcome is a fully consistent forecasting scenario. This is particularly relevant for people that are interested in certain components or that have to provide support for a particular aggregate assessment. In an empirical application with GDP data from France, Germany and the United Kingdom we find that the outcome of the combination method shows equal aggregate accuracy to that of equivalent traditional combination methods and a disaggregate accuracy similar or better to that of the best single models.
    Keywords: Bottom-up forecasting; Forecast combination; Hierarchical forecasting; Reconciling forecasts
    JEL: C53 E27 E37
    Date: 2017–02
  2. By: Cobb, Marcus P A
    Abstract: When it comes to point forecasting there is a considerable amount of literature that deals with ways of using disaggregate information to improve aggregate accuracy. This includes examining whether producing aggregate forecasts as the sum of the component’s forecasts is better than alternative direct methods. On the contrary, the scope for producing density forecasts based on disaggregate components remains relatively unexplored. This research extends the bottom-up approach to density forecasting by using the methodology of large Bayesian VARs to estimate the multivariate process and produce the aggregate forecasts. Different specifications including both fixed and time-varying parameter VARs and allowing for stochastic volatility are considered. The empirical application with GDP and CPI data for Germany, France and UK shows that VARs can produce well calibrated aggregate forecasts that are similar or more accurate than the aggregate univariate benchmarks.
    Keywords: Density Forecasting; Bottom-up forecasting; Hierarchical forecasting; Bayesian VAR; Forecast calibration
    JEL: C32 C53 E37
    Date: 2017–02
  3. By: Cheung, Yin-Wong; Chinn, Menzie D.; Garcia Pascual, Antonio; Zhang, Yi
    Abstract: Previous assessments of nominal exchange rate determination, following Meese and Rogoff (1983) have focused upon a narrow set of models. Cheung et al. (2005) augmented the usual suspects with productivity based models, and "behavioral equilibrium exchange rate" models, and assessed performance at horizons of up to 5 years. In this paper, we further expand the set of models to include Taylor rule fundamentals, yield curve factors, and incorporate shadow rates and risk and liquidity factors. The performance of these models is compared against the random walk benchmark. The models are estimated in error correction and first-difference specifications. We examine model performance at various forecast horizons (1 quarter, 4 quarters, 20 quarters) using differing metrics (mean squared error, direction of change), as well as the “consistency” test of Cheung and Chinn (1998). No model consistently outperforms a random walk, by a mean squared error measure, although purchasing power parity does fairly well. Moreover, along a direction-of-change dimension, certain structural models do outperform a random walk with statistical significance. While one finds that these forecasts are cointegrated with the actual values of exchange rates, in most cases, the elasticity of the forecasts with respect to the actual values is different from unity. Overall, model/specification/currency combinations that work well in one period will not necessarily work well in another period. JEL Classification: F31, F47
    Keywords: behavioral equilibrium exchange rate model, exchange rates, forecasting performance, interest rate parity, monetary model
    Date: 2017–02
  4. By: Jo, Soojin (Federal Reserve Bank of Dallas); Sekkel, Rodrigo (Bank of Canada)
    Abstract: We analyze the evolution of macroeconomic uncertainty in the United States, based on the forecast errors of consensus survey forecasts of various economic indicators. Comprehensive information contained in the survey forecasts enables us to capture a real-time subjective measure of uncertainty in a simple framework. We jointly model and estimate macroeconomic (common) and indicator-specific uncertainties of four indicators, using a factor stochastic volatility model. Our macroeconomic uncertainty has three major spikes aligned with the 1973–75, 1980, and 2007–09 recessions, while other recessions were characterized by increases in indicator-specific uncertainties. We also show that the selection of data vintages affects the estimates and relative size of jumps in estimated uncertainty series. Finally, our macroeconomic uncertainty has a persistent negative impact on real economic activity, rather than producing “wait-and-see” dynamics.
    Keywords: Factor stochastic volatility model; survey forecasts; Uncertainty
    Date: 2017–01–26
  5. By: Alessio Sancetta
    Abstract: Estimation of the intensity of a point process is considered within a nonparametric framework. The intensity measure is unknown and depends on covariates, possibly many more than the observed number of jumps. Only a single trajectory of the counting process is observed. Interest lies in estimating the intensity conditional on the covariates. The impact of the covariates is modelled by an additive model where each component can be written as a linear combination of possibly unknown functions. The focus is on prediction as opposed to variable screening. Conditions are imposed on the coefficients of this linear combination in order to control the estimation error. The rates of convergence are optimal when the number of active covariates is large. As an application, the intensity of the buy and sell trades of the New Zealand dollar futures is estimated and a test for forecast evaluation is presented. A simulation is included to provide some finite sample intuition on the model and asymptotic properties.
    Date: 2017–02
  6. By: Pablo Guerron-Quintana; Molin Zhong
    Abstract: We propose a parsimonious semiparametric method for macroeconomic forecasting during episodes of sudden changes. Based on the notion of clustering and similarity, we partition the time series into blocks, search for the closest blocks to the most recent block of observations, and with the matched blocks we proceed to forecast. One possibility is to compare local means across blocks, which captures the idea of matching directional movements of a series. We show that our approach does particularly well during the Great Recession and for variables such as inflation, unemployment, and real personal income. When supplemented with information from housing prices, our method consistently outperforms parametric linear, nonlinear, univariate, and multivariate alternatives for the period 1990 - 2015.
    Keywords: Forecasting ; Great Recession ; Nearest neighbor ; Semiparametric methods
    JEL: C14 C53
    Date: 2017–01–31

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