nep-for New Economics Papers
on Forecasting
Issue of 2016‒01‒03
seven papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Oil and Stock Returns with a Qual VAR using over 150 Years of Data By Rangan Gupta; Mark E. Wohar
  2. Nowcasting in Real Time Using Popularity Priors By Monokroussos, George
  3. Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models By Zeyyad Mandalinci
  4. Nowcasting Indonesia By Luciani, Matteo; Pundit, Madhavi; Ramayandi, Arief; Veronese , Giovanni
  5. Characterizing Investor Expectations for Assets with Varying Risk By Eric Gaus; Arunima Sinha
  6. Public Debt Sustainability in Developing Asia: An Update By Ferrarini , Benno; Ramayandi, Arief
  7. Macroprudential policy and forecasting using Hybrid DSGE models with financial frictions and State space Markov-Switching TVP-VARs By Stelios D. Bekiros; Alessia Paccagnini

  1. By: Rangan Gupta (Department of Economics, University of Pretoria); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, USA, and School of Business and Economics, Loughborough University, UK.)
    Abstract: The extant literature suggests that oil price, stock price and economic activity are all endogenous and the linkages between these variables are nonlinear. Against this backdrop, the objective of this paper is to use a Qualitative Vector Autoregressive (Qual VAR) to forecast (West Texas Intermediate) oil and (S&P500) stock returns over a monthly period of 1884:09 to 2015:08, using an in-sample period of 1859:10-1884:08. Given that there is no data on economic activity at monthly frequency dating as far back as 1859:09, we measure the same using the NBER recession dummies, which in turn, can be easily accommodated in a Qual VAR as an endogenous variable. In addition, the Qual VAR is inherently a nonlinear model as it allows the oil and stock returns to behave as nonlinear functions of their own past values around business cycle turning points. Our results show that, for both oil and stock returns, the Qual VAR model outperforms the random walk model (in a statistically significant way) at all the forecasting horizons considered, i.e., one- to twelve-months-ahead. In addition, the Qual VAR model, also outperforms the AR and VAR models (in a statistically significant manner) at medium- to long-run horizons for oil returns, and short- to medium-run horizons for stock returns.
    Keywords: Vector Autoregressions, Business Cycle Turning Points, Forecasting, Oil and Stock Prices
    JEL: C32 C53 E32 G10 G17 Q41
    Date: 2015–12
  2. By: Monokroussos, George
    Abstract: This paper proposes a Bayesian nowcasting approach that utilizes information coming both from large real-time data sets and from priors constructed using internet search popularity measures. Exploiting rich information sets has been shown to deliver significant gains in nowcasting contexts, whereas popularity priors can lead to better nowcasts in the face of model and data uncertainty in real time, challenges which can be particularly relevant during turning points. It is shown, for a period centered on the latest recession in the United States, that this approach has the potential to deliver particularly good real-time nowcasts of GDP growth.
    Keywords: Nowcasting, Gibbs Sampling, Factor Models, Kalman Filter, Real-Time Data, Google Trends, Monetary Policy, Great Recession.
    JEL: C11 C22 C53 E37 E52
    Date: 2015–11–01
  3. By: Zeyyad Mandalinci (Queen Mary University of London)
    Abstract: This paper carries out a comprehensive forecasting exercise to assess out-of-sample forecasting performance of various econometric models for inflation across three dimensions; time, emerging market countries and models. The competing forecasting models include univariate and multivariate, fixed and time varying parameter, constant and stochastic volatility, small and large dataset, with and without bayesian variable selection models. Results indicate that the forecasting performance of different models change notably both across time and countries. Similar to some of the recent findings of the literature that focus on developed countries, models that account for stochastic volatility and time-varying parameters provide more accurate forecasts for inflation than alternatives in emerging markets.
    Keywords: Forecasting, Bayesian Analysis, Emerging Markets, Forecast Comparison
    JEL: E37 C11 E31
    Date: 2015–12
  4. By: Luciani, Matteo (Board of Governors of the Federal Reserve System); Pundit, Madhavi (Asian Development Bank); Ramayandi, Arief (Asian Development Bank); Veronese , Giovanni (Banca d'Italia)
    Abstract: We produce predictions of the current state of the Indonesian economy by estimating a Dynamic Factor Model on a dataset of 11 indicators (also followed closely by market operators) over the time period 2002 to 2014. Besides the standard difficulties associated with constructing timely indicators of current economic conditions, Indonesia presents additional challenges typical to emerging market economies where data are often scant and unreliable. By means of a pseudo-real-time forecasting exercise we show that our model outperforms univariate benchmarks, and it does comparably with predictions of market operators. Finally, we show that when quality of data is low, a careful selection of indicators is crucial for better forecast performance.
    Keywords: Dynamic Factor Models; emerging market economies; nowcasting
    JEL: C32 C53 E37 O53
    Date: 2015–12–15
  5. By: Eric Gaus (Ursinus College); Arunima Sinha (Fordham University)
    Abstract: How do financial market investors form expectations about assets with different risk characteristics? We examine this question using Euro-area yield curves for AAA-rated and AAA-with-other bonds. Investors' conditional forecasts about the yield curves for different assets, at various forecasting horizons, are modeled using a VAR model with time-varying parameters. Two processes are assumed for the evolution of these parameters: a constant-gain learning model and a new endogenous learning technique proposed here. Both these algorithms allow investors to account for structural changes in the data. The endogenous learning mechanism also allows investors to compensate for large deviations in observed coefficients used for forecasting, relative to past data. Daily data is used to estimate the gain parameters for the learning algorithms, and we find that these gains vary across asset types, implying investors form conditional expectations differently for assets with differential risks. For 2005-2015, the investors' conditional forecasts for the AAA-rated bonds are better described using the endogenous learning mechanism, implying that investors with lower risk preferences are more sensitive to large deviations in the data.
    Keywords: Adaptive learning, Investor beliefs, Risk
    JEL: E52 D83
    Date: 2015–09–01
  6. By: Ferrarini , Benno (Asian Development Bank); Ramayandi, Arief (Asian Development Bank)
    Abstract: Our previous assessment of debt sustainability in developing Asia, conducted in 2011, found that the region’s fiscal outlook was mostly benign. In this study we update the debt sustainability assessment, taking stock of the latest data and including a larger number of countries. With the benefit of hindsight, we assess the accuracy of our earlier debt ratio forecasts and the underlying macroeconomic assumptions. By and large, we find that standard debt sustainability analysis (DSA) represents a valid forecasting tool, able to predict debt ratios fairly accurately under reasonable assumptions and circumstances. Further, our fan chart analysis confirms the importance for stochastic analysis to integrate standard DSA, in order to capture heightened macroeconomic volatility, which we observe for some countries in the region. Looking forward to 2020, debt ratio projections confirm that the outlook remains benign for the region as a whole, country heterogeneity notwithstanding. On the issue of DSA methods and implementation, we emphasize the importance of macroeconomic forecast accuracy and suggest that volatility be captured by risk analysis tools that would optimally flank the standard DSA framework.
    Keywords: debt-to-GDP ratio; fan charts; public debt sustainability; sovereign debt
    JEL: E62 H63 H68
    Date: 2015–12–14
  7. By: Stelios D. Bekiros; Alessia Paccagnini
    Abstract: We focus on the interaction of frictions both at the firm level and in the banking sector in order to examine the transmission mechanism of the shocks and to reflect on the response of the monetary policy to increases in interest rate spreads, using DSGE models with financial frictions. However, VAR models are linear and the solutions of DSGEs are often linear approximations; hence they do not consider time variation in parameters that could account for inherent nonlinearities and capture the adaptive underlying structure of the economy, especially in crisis periods. A novel method for time-varying VAR models is introduced. As an extension to the standard homoskedastic TVP-VAR, we employ a Markov-switching heteroskedastic error structure. Overall, we conduct a comparative empirical analysis of the out-of-sample performance of simple and hybrid DSGE models against standard VARs, BVARs, FAVARs, and TVP-VARs, using data sets from the U.S. economy. We apply advanced Bayesian and quasi-optimal filtering techniques in estimating and forecasting the models.
    Keywords: Financial frictions; Time-varying coefficients; Quasi-optimal filtering
    Date: 2015–10

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