nep-for New Economics Papers
on Forecasting
Issue of 2016‒12‒11
ten papers chosen by
Rob J Hyndman
Monash University

  1. Stock Return Prediction with Fully Flexible Models and Coefficients By Byrne, Joseph; Fu, Rong
  2. Growth-enhancing effect of openness to trade and migrations: What is the effective transmission channel for Africa? By Rachidi Kotchoni; Dalibor Stevanovic
  3. Changes in Inflation Predictability in Major Latin American Countries By Garcés Díaz Daniel
  4. Adaptive models and heavy tails with an application to inflation forecasting By Delle Monache, Davide; Petrella, Ivan
  5. The Role of Partisan Conflict in Forecasting the U.S. Equity Premium: A Nonparametric Approach By Rangan Gupta; John W. Muteba Mwamba; Mark E. Wohar
  6. Macroeconomic now- and forecasting based on the factor error correction model using targeted mixed frequency indicators By Kurz-Kim, Jeong-Ryeol
  7. A Bayesian Infinite Hidden Markov Vector Autoregressive Model By Didier Nibbering; Richard Paap; Michel van der Wel
  8. Modelling Chinese Inbound Tourism Arrivals into Christchurch By Fieger, Peter; Rice, John
  9. Money and Inflation: Evidence from P-Star Model By Sunil Paul; Sartaj Rasool Rather; M. Ramachandran
  10. Estimation of time-dependent Hurst exponents with variational smoothing and application to forecasting foreign exchange rates By Matthieu Garcin

  1. By: Byrne, Joseph; Fu, Rong
    Abstract: We evaluate stock return predictability using a fully flexible Bayesian framework, which explicitly allows for different degrees of time-variation in coefficients and in forecasting models. We believe that asset return predictability can evolve quickly or slowly, based upon market conditions, and we should account for this. Our approach has superior out-of-sample predictive performance compared to the historical mean, from a statistical and economic perspective. We also find that our model statistically dominates its nested models, including models in which parameters evolve at a constant rate. By decomposing sources of prediction uncertainty into five parts, we find that our fully flexible approach more precisely identifies time-variation in coefficients and in forecasting models, leading to mitigation of estimation risk and forecasting improvements. Finally, we relate predictability to the business cycle.
    Keywords: Stock Return Prediction, Time-Varying Coefficients and Forecasting Models, Bayesian econometrics, Forecast combination
    JEL: C11 G11 G12 G17
    Date: 2016–11–09
  2. By: Rachidi Kotchoni; Dalibor Stevanovic
    Abstract: This paper proposes a framework to produce real time multi-horizon forecasts of business cycle turning points, average forecasts of economic activity as well as conditional forecasts that depend on whether the horizon of interest belongs to a recession episode or not. Our forecasting models take the form of an autoregression of order one that is augmented with either a probability of recession or an inverse Mills ratio. Our empirical results suggest that a static Probit model that uses only the Term Spread as regressor provides comparable fit to the data as more sophisticated non-static Probit models. We also find that the dynamic patterns of the Term Structure of recession probabilities are quite informative about business cycle turning points. Our most parsimonious augmented autoregressive model delivers better out-of-sample forecasts of GDP growth than the benchmark models considered. We construct several Term Structures of recession probabilities since the last official NBER turning point. The results suggest that there has been no harbinger of a recession for the US economy since 2010Q4 and that there is none to fear at least until 2018Q1. GDP growth is expected to rise steadily between 2016Q3 and 2018Q1 in the range [2.5%,3.5%].
    Keywords: Augmented Autoregressive Model, Conditional Forecasts, Economic Activity, Inverse Mills Ratio, Probit, Recession.
    JEL: C35 C53 E27 E37
    Date: 2016
  3. By: Garcés Díaz Daniel
    Abstract: Forecasts of inflation in the United States since the mid eighties have had smaller errors than in the past, but those conditional on commonly used variables cannot consistently beat the ones from univariate models. This paper shows through simple modifications to the classical monetary model that something similar occurred in those major Latin American economies that achieved their own "Great Moderation." For those countries that did not attain macroeconomic stability, inflation forecasting conditional on some variables has not changed. Allowing the parameters that determine Granger causality to change when the monetary regime does, makes possible the estimation of parsimonious inflation models for all available data (eight decades for one country and five for the others). The models so obtained ouperform others in pseudo out-of-sample forecasts for most of the period under study, except in the cases when an inflation targeting policy was successfully implemented.
    Keywords: Money;exchange rate;cointegration;inflation forecasting
    JEL: C32 E41 E42 E52
    Date: 2016–12
  4. By: Delle Monache, Davide; Petrella, Ivan
    Abstract: This paper introduces an adaptive algorithm for time-varying autoregressive models in the presence of heavy tails. The evolution of the parameters is determined by the score of the conditional distribution, the resulting model is observation-driven and is estimated by classical methods. In particular, we consider time variation in both coefficients and volatility, emphasizing how the two interact with each other. Meaningful restrictions are imposed on the model parameters so as to attain local stationarity and bounded mean values. The model is applied to the analysis of inflation dynamics with the following results: allowing for heavy tails leads to significant improvements in terms of fit and forecast, and the adoption of the Student-t distribution proves to be crucial in order to obtain well calibrated density forecasts. These results are obtained using the US CPI inflation rate and are confirmed by other inflation indicators, as well as for CPI inflation of the other G7 countries.
    Keywords: adaptive algorithms, inflation, score-driven models, student-t, time-varying parameters.
    JEL: C22 C51 C53 E31
    Date: 2016–09–01
  5. By: Rangan Gupta (Department of Economics, University of Pretoria, South Africa); John W. Muteba Mwamba (Faculty of Economic and Financial Sciences, University of Johannesburg, South Africa); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha USA, and School of Business and Economics, Loughborough University, UK)
    Abstract: Information on partisan conflict is shown to matter in forecasting the U.S. equity premium, especially when accounting for omitted nonlinearities in their relationship, via a nonparametric predictive regression approach over the monthly period 1981:1-2016:6. Unlike as suggested by a linear predictive model, the nonparametric functional coefficient regression that includes the partisan conflict index, enhances significantly the out-of-sample excess stock returns predictability.
    Keywords: : Equity Premium, Partisan Conflict Index, Linear and Nonparametric Predictive Regressions
    JEL: C14 C22 C53 G1 G18
    Date: 2016–12
  6. By: Kurz-Kim, Jeong-Ryeol
    Abstract: Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling technique. Other improvements are also studied mostly in two directions: a pre-selection is used to optimally choose a small number of indicators from a large number of indicators. The error correction mechanism takes into account the co-integrating relationship between the key variables and factors and, hence, captures the long-run dynamics of the non-stationary macroeconomic variables. This papers proposes the factor error correction model using targeted mixedfrequency indicators, which combines the three refinements for the dynamic factor model, namely the mixed data sampling technique, pre-selection methods, and the error correction mechanism. The empirical results based on euro-area data show that the now- and forecasting performance of our new model is superior to that of the subset models.
    Keywords: Factor model,MIDAS,Lasso,Elastic Net,ECM,Nowcasting,Forecasting
    JEL: C18 C23 C51 C52 C53
    Date: 2016
  7. By: Didier Nibbering (Erasmus University Rotterdam, The Netherlands); Richard Paap (Erasmus University Rotterdam, The Netherlands); Michel van der Wel (Erasmus University Rotterdam, The Netherlands)
    Abstract: We propose a Bayesian infinite hidden Markov model to estimate time-varying parameters in a vector autoregressive model. The Markov structure allows for heterogeneity over time while accounting for state-persistence. By modelling the transition distribution as a Dirichlet process mixture model, parameters can vary over potentially an infinite number of regimes. The Dirichlet process however favours a parsimonious model without imposing restrictions on the parameter space. An empirical application demonstrates the ability of the model to capture both smooth and abrupt parameter changes over time, and a real-time forecasting exercise shows excellent predictive performance even in large dimensional VARs.
    Keywords: Time-Varying Parameter Vector Autoregressive Model; Semi-parametric Bayesian Inference; Dirichlet Process Mixture Model; Hidden Markov Chain; Monetary Policy Analysis; Real-time Forecasting
    JEL: C11 C14 C32 C51 C54
    Date: 2016–12–06
  8. By: Fieger, Peter; Rice, John
    Abstract: New data and modelling approaches are improving the usefulness of internet search data for forecasting inbound tourist arrivals. This short paper provides evidence of the usefulness of Baidu search data in predicting Chinese inbound tourist arrivals into a specific region in New Zealand. It also compares three modelling approaches, finding a Vector Autoregressive approach the most useful.
    Keywords: Tourism forecasting, Applied Econometrics
    JEL: D12 L83 R1
    Date: 2016–10–01
  9. By: Sunil Paul (Madras School of Economics); Sartaj Rasool Rather (Madras School of Economics); M. Ramachandran (Department of Economics, Pondicherry University, Puducherry)
    Abstract: This study uses P-star model to examine the role of money in explaining inflation in India. In particular, we compare the performance of traditional Phillips curve approach against P-star model in forecasting inflation. Moreover, the study estimates P-star model using the alternative measures of money such as simple sum and Divisia M3, to examine the relevance of aggregation theoretic monetary aggregates in explaining inflation. The empirical results indicate that P-star model with real money gap has an edge over traditional Phillips curve approach in forecasting inflation. More importantly, we found that the P-star model estimated with Divisia real money gap performs better than its simple sum counterpart. These results highlight the role of money in explaining inflation in India.Length: 39 pages
    Keywords: Inflation, P-star, Philips curve, Divisia monetary aggregates Classification-JEL: C43; E49
    Date: 2015–08
  10. By: Matthieu Garcin (Natixis Asset Management, Labex ReFi - Université Paris1 - Panthéon-Sorbonne)
    Abstract: Hurst exponents depict the long memory of a time series. For human-dependent phenomena, as in finance, this feature may vary in the time. It justifies modelling dynamics by multifractional Brownian motions, which are consistent with time-varying Hurst exponents. We improve the existing literature on estimating time-dependent Hurst exponents by proposing a smooth estimate obtained by variational calculus. This method is very general and not restricted to the sole Hurst framework. It is globally more accurate and easier than other existing non-parametric estimation techniques. Besides, in the field of Hurst exponents, it makes it possible to make forecasts based on the estimated multifractional Brownian motion. The application to high-frequency foreign exchange markets (GBP, CHF, SEK, USD, CAD, AUD, JPY, CNY and SGD, all against EUR) shows significantly good forecasts. When the Hurst exponent is higher than 0.5, what depicts a long-memory feature, the accuracy is higher.
    Keywords: Hurst exponent,Euler-Lagrange equation,non-parametric smoothing,foreign exchange forecast,Multifractional brownian motion
    Date: 2016–11–19

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