nep-for New Economics Papers
on Forecasting
Issue of 2018‒02‒05
ten papers chosen by
Rob J Hyndman
Monash University

  1. Improving Forecast Accuracy of Financial Vulnerability: Partial Least Squares Factor Model Approach By Hyeongwoo Kim; Kyunghwan Ko
  2. Predicting earnings and cash flows: The information content of losses and tax loss carryforwards By Dreher, Sandra; Eichfelder, Sebastian; Noth, Felix
  3. Exact Likelihood Estimation and Probabilistic Forecasting in Higher-order INAR(p) Models By Lu, Yang
  4. Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration By Angelica Gianfreda; Luca Rossini; Francesco Ravazzolo
  5. Predicting crypto-currencies using sparse non-Gaussian state space models By Christian Hotz-Behofsits; Florian Huber; Thomas O. Z\"orner
  6. The Role of Macroeconomic, Policy, and Forecaster Uncertainty in Forecast Dispersion By Li, You; Tay, Anthony
  7. Macroeconomic Nowcasting and Forecasting with Big Data By Bok, Brandyn; Caratelli, Daniele; Giannone, Domenico; Sbordone, Argia; Tambalotti, Andrea
  8. Forecaster’s utility and forecasts coherence By Emilio Zanetti Chini
  9. Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria By McKenzie, David J.; Sansone, Dario
  10. Forecasting the Growth Cycles of the Turkish Economy By H. Murat Ozbilgin

  1. By: Hyeongwoo Kim (Department of Economics, Auburn University); Kyunghwan Ko (Economic Research Team, Jeju Branch, The Bank of Korea)
    Abstract: We present a factor augmented forecasting model for assessing the financial vulnerability in Korea. Dynamic factor models often extract latent common factors from a large panel of time series data via the method of the principal components (PC). Instead, we employ the partial least squares (PLS) method that estimates target specific common factors, utilizing covariances between predictors and the target variable. Applying PLS to 198 monthly frequency macroeconomic time series variables and the Bank of Korea's Financial Stress Index (KFSTI), our PLS factor augmented forecasting models consistently outperformed the random walk benchmark model in out-of-sample prediction exercises in all forecast horizons we considered. Our models also outperformed the autoregressive benchmark model in short-term forecast horizons. We expect our models would provide useful early warning signs of the emergence of systemic risks in Korea's financial markets.
    Keywords: Partial least squares, Principal component analysis, Financial stress index, Out-of-sample forecast, RRMSPE, DMW statistics
    JEL: C38 C53 E44 E47 G01 G17
    Date: 2017–05–02
  2. By: Dreher, Sandra; Eichfelder, Sebastian; Noth, Felix
    Abstract: We analyze the relevande of losses, accounting information on tax loss carryforwards, and deferred taxes for the prediction of earnings and cash flows up to four years ahead. We use a unique hand-collected panel of German listed firms encompassing detailed information on tax loss carryforwards and deferred taxes from the tax footnote. Our out-of-sample predictions show that considering accounting informaton on tax loss carryforwards and deferred taxes does not enhance the accuracy of performance forecasts and can even worsen performance predictions. We find that common forecasting approaches that treat positive and negative performances equally or that use a dummy variable for negative performance can lead to biased performance forecasts and we provide a simple empirical specification to account for that issue.
    Keywords: perfomance forecast,in-sample prediction,out-of-sample prediction,loss persistence,deferred taxes,tax loss carryforwards
    JEL: M40 M41 C53
    Date: 2017
  3. By: Lu, Yang
    Abstract: The computation of the likelihood function and the term structure of probabilistic forecasts in higher-order INAR(p) models are qualified numerically intractable and the literature has considered various approximations. Using the notion of compound autoregressive process, we propose an exact and fast algorithm for both quantities. We find that existing approximation schemes induce significant errors for forecasting.
    Keywords: compound autoregressive process, probabilistic forecast of counts, matrix arithmetic.
    JEL: C22 C25
    Date: 2018–01–01
  4. By: Angelica Gianfreda; Luca Rossini; Francesco Ravazzolo
    Abstract: This paper compares alternative univariate versus multivariate models, probabilistic versus Bayesian autoregressive and vector autoregressive specifications for hourly day-ahead electricity prices, with and without renewable energy sources. The accuracy of point and density forecasts are inspected in four main European markets (Germany, Denmark, Italy and Spain) characterized by different levels of renewable energy power generation. Our results show that the Bayesian VAR specifications with exogenous variables dominate other multivariate and univariate specifications, in terms of both point and density forecasting.
    Keywords: Density Forecasting, Electricity Market, Forecasting, Hourly Prices, Renewable Energies.
    Date: 2018–01
  5. By: Christian Hotz-Behofsits; Florian Huber; Thomas O. Z\"orner
    Abstract: In this paper we forecast daily returns of crypto-currencies using a wide variety of different econometric models. To capture salient features commonly observed in financial time series like rapid changes in the conditional variance, non-normality of the measurement errors and sharply increasing trends, we develop a time-varying parameter VAR with t-distributed measurement errors and stochastic volatility. To control for overparameterization, we rely on the Bayesian literature on shrinkage priors that enables us to shrink coefficients associated with irrelevant predictors and/or perform model specification in a flexible manner. Using around one year of daily data we perform a real-time forecasting exercise and investigate whether any of the proposed models is able to outperform the naive random walk benchmark. To assess the economic relevance of the forecasting gains produced by the proposed models we moreover run a simple trading exercise.
    Date: 2018–01
  6. By: Li, You (School of Economics, Singapore Management University); Tay, Anthony (School of Economics, Singapore Management University)
    Abstract: We explore the role of uncertainty in explaining dispersion in professional forecasters’ density forecasts of real output growth and inflation. We consider three separate notions of uncertainty: general macroeconomic uncertainty (the fact that macroeconomic variables are easier to forecast at some times than at others), policy uncertainty, and forecaster uncertainty. We find that dispersion in individual density forecasts is related to overall macroeconomic uncertainty and policy uncertainty, while forecaster uncertainty (which we define as the average in the uncertainty expressed by individual forecasters) appears to have little role in forecast dispersion.
    Date: 2017–11–01
  7. By: Bok, Brandyn; Caratelli, Daniele; Giannone, Domenico; Sbordone, Argia; Tambalotti, Andrea
    Abstract: Data, data, data ... Economists know their importance well, especially when it comes to monitoring macroeconomic conditions -- the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before "big data" became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate best practices of forecasters on trading desks, at central banks, and in other market-monitoring roles. We present in detail the methodology underlying the New York Fed Staff Nowcast, which employs these innovative techniques to produce early estimates of GDP growth, synthesizing a wide range of macroeconomic data as they become available.
    Keywords: business cycle analysis; high-dimensional data; monitoring economic conditions; real-time data flow
    JEL: C32 C53 E3
    Date: 2018–01
  8. By: Emilio Zanetti Chini (Department of Economics and Management, University of Pavia)
    Abstract: I provide general frequentist framework to elicit the forecaster’s expected utility based on a Lagrange Multiplier-type test for the null of locality of the scoring rules associated to the probabilistic forecast. These are assumed to be observed transition variables in a nonlinear autoregressive model to ease the statistical inference. A simulation study reveals that the test behaves consistently with the requirements of the theoretical literature. The locality of the scoring rule is fundamental to set dating algorithms to measure and forecast probability of recession in US business cycle. An investigation of Bank of Norway’s forecasts on output growth leads us to conclude that forecasts are often suboptimal with respect to some simplistic benchmark if forecaster’s reward is not properly evaluated.
    Keywords: Business Cycle, Evaluation, Locality Testing, Nonlinear Time Series, Predictive Density, Scoring Rules, Scoring Structures.
    JEL: C12 C22 C44 C53
    Date: 2018–01
  9. By: McKenzie, David J.; Sansone, Dario
    Abstract: We compare the relative performance of man and machine in being able to predict outcomes for entrants in a business plan competition in Nigeria. The first human predictions are business plan scores from judges, and the second are simple ad-hoc prediction models used by researchers. We compare these (out-of-sample) performances to those of three machine learning approaches. We find that i) business plan scores from judges are uncorrelated with business survival, employment, sales, or profits three years later; ii) a few key characteristics of entrepreneurs such as gender, age, ability, and business sector do have some predictive power for future outcomes; iii) modern machine learning methods do not offer noticeable improvements; iv) the overall predictive power of all approaches is very low, highlighting the fundamental difficulty of picking winners; and v) our models can do twice as well as random selection in identifying firms in the top tail of performance.
    Keywords: business plans; entrepreneurship; Machine Learning; Nigeria
    JEL: C53 L26 M13 O12
    Date: 2017–12
  10. By: H. Murat Ozbilgin
    Abstract: This paper first specifies the medium-term growth cycles for the Turkish economy. The impact of the frequency transformation methods and the time-serious filters on cycles and potential output are discussed. Then a composite leading indicator (CLI) is constructed that is correlated with the third lead of the GDP with a coefficient of 0.9. The CLI signals 11 out of 13 turning points in the Turkish growth cycle in the 1993-2016 period. The CLI is coincident with the remaining two turning points, hence still providing early warning. Within the same period, only two false signals are generated by the CLI. Finally, building on the seminal paper by Neftci (1982), a method for computation of the turning point probabilities is developed. The virtue of the method is that it takes into account the observed deepness and steepness in the series.
    Keywords: Time-series filters, Growth cycles, Composite leading indicators, Turning point probabilities
    JEL: E32 E37 E66
    Date: 2017

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