nep-for New Economics Papers
on Forecasting
Issue of 2014‒06‒22
nine papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting exchange rates better than the random walk thanks to machine learning techniques By Christophe Amat; Tomasz Michalski; Gilles Stoltz
  2. Using Data on Seller Behavior to Forecast Short-run House Price Changes By Anenberg, Elliot; Laufer, Steven
  3. Nowcasting U.S. Headline and Core Inflation By Knotek, Edward S.; Zaman, Saeed
  4. Forecasting conditional volatility on the RIN market using MS GARCH model By Kakorina, Ekaterina
  5. Model Risk of Risk Models By Danielsson, Jon; James, Kevin; Valenzuela, Marcela; Zer, Ilknur
  6. Inflation Dynamics and Business Cycles By Suleyman Hilmi Kal; Nuran Arslaner; Ferhat Arslaner
  7. Zero lower bound, unconventional monetary policy and indicator properties of interest rate spreads By Hännikäinen, Jari
  8. Tips from TIPS: the informational content of Treasury Inflation-Protected Security prices By D'Amico, Stefania; Kim, Don H.; Wei, Min
  9. Asymmetric Information and Rationalizability By Gabriel Desgranges; Stéphane Gauthier

  1. By: Christophe Amat (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959); Tomasz Michalski (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959); Gilles Stoltz (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959)
    Abstract: Simple exchange rate models based on economic fundamentals were shown to have a difficulty in beating the random walk when predicting the exchange rates out of sample in the modern floating era. Using methods from machine learning -- adaptive sequential ridge regression with discount factors -- that prevent overfitting in-sample for better and more stable forecasting performance out-of-sample we show that fundamentals from the PPP, UIRP and monetary models consistently improve the accuracy of exchange rate forecasts for major currencies over the floating period era 1973--2013 and are able to beat the random walk prediction giving up to 5% improvements in terms of the RMSE at a 1 month forecast. ''Classic'' fundamentals hence contain useful information about exchange rates even for short forecasting horizons - and the Meese and Rogoff [1983] puzzle is overturned. Such conclusions cannot be obtained when rolling or recursive OLS regressions are used as is common in the literature.
    Keywords: exchange rates; forecasting; machine learning; purchasing power parity; uncovered interest rate parity; monetary exchange rate models
    Date: 2014–06–10
  2. By: Anenberg, Elliot (Board of Governors of the Federal Reserve System (U.S.)); Laufer, Steven (Board of Governors of the Federal Reserve System (U.S.))
    Abstract: We construct a new "list-price index" that accurately reveals trends in house prices several months before existing sales price indices like Case-Shiller. Our index is based on the repeat-sales approach but for recent months uses listings data, which are available essentially in real time, instead of transactions data, which become available with signiffcant lags. Our index methodology is motivated by a simple model of the home-selling problem that shows how listings variables such as the list price and marketing time help predict the final sales price. In a sample of three large MSAs over the years 2008-2012, our index (i) accurately forecasts the Case-Shiller index several months in advance, (ii) outperforms forecasting models that do not use listings data, and (iii) outperforms the market's expectation as inferred from prices on Case-Shiller future contracts.
    Keywords: House price indices; house price forecasting; housing market microstructure
    Date: 2014–01–29
  3. By: Knotek, Edward S. (Federal Reserve Bank of Cleveland); Zaman, Saeed (Federal Reserve Bank of Cleveland)
    Abstract: Forecasting future inflation and nowcasting contemporaneous inflation are difficult. We propose a new and parsimonious model for nowcasting headline and core inflation in the U.S. price index for personal consumption expenditures (PCE) and the consumer price index (CPI). The model relies on relatively few variables and is tested using real-time data. The model’s nowcasting accuracy improves as information accumulates over the course of a month or quarter, and it easily outperforms a variety of statistical benchmarks. In head-to-head comparisons, the model’s nowcasts of CPI infl ation outperform those from the Blue Chip consensus, with especially significant outperformance as the quarter goes on. The model’s nowcasts for CPI and PCE inflation also significantly outperform those from the Survey of Professional Forecasters, with similar nowcasting accuracy for core inflation measures. Across all four inflation measures, the model’s nowcasting accuracy is generally comparable to that of the Federal Reserve’s Greenbook.
    Keywords: inflation; nowcasting; forecasting; real-time data; professional forecasters; Greenbook.
    JEL: C53 E3 E37
    Date: 2014–05–01
  4. By: Kakorina, Ekaterina
    Abstract: In the recent years the topic about pollution of environment is quite popular. Many countries organize the government policy taking into account environmentally friendly policy. Maybe because of big share of the world pollution the USA organized not only the emission market, but also the RIN market where RIN is a 38-digit serial number, tax, security and investment. All actors of the RIN market can be divided into six groups: farmers, refiners, blenders, owners of fuel stations, EPA and private agencies. Models which can forecast are ARMA, ARMA-GARCH, GARCH-M and MS ARMA-GARCH. We identify that non-path-dependent MS AR(1)-GARCH-M(1,1) model cannot forecast better than AR(1)-t-GARCH(1,1) model, because it cannot forecast zero returns. Additionally, according to White’s test we identify than standard normal distribution is better then Student-t. At the same time our forecast of volatility using MS GARCH with standard normal distribution does not work the right way. In other words, forecasted volatility and returns are not fluctuated and also forecasted returns differ significantly from the real returns, especially, after the fourth period. Futhermore, we compare our price forecast with data which are presented by EPA (bid and ask prices). Using White test again, we find that the statistic is less in our case. In addition, the price does not change the way it should do, in other words, maybe we do not include a significant factor in our analysis.
    Keywords: RIN market, Renewable Identification Number, ecology, security, volatility forecast, price forecast, MS GARCH
    JEL: C5 C58 G17 Q28
    Date: 2014–07
  5. By: Danielsson, Jon (London School of Economics); James, Kevin (London School of Economics); Valenzuela, Marcela (University of Chile); Zer, Ilknur (Board of Governors of the Federal Reserve System (U.S.))
    Abstract: This paper evaluates the model risk of models used for forecasting systemic and market risk. Model risk, which is the potential for different models to provide inconsistent outcomes, is shown to be increasing with and caused by market uncertainty. During calm periods, the underlying risk forecast models produce similar risk readings, hence, model risk is typically negligible. However, the disagreement between the various candidate models increases significantly during market distress, with a no obvious way to identify which method is the best. Finally, we discuss the main problems in risk forecasting for macro prudential purposes and propose an evaluation criteria for such models.
    Keywords: Value-at-Risk; expected shortfall; systemic risk; financial stability; Basel III; CoVaR; MES
    Date: 2014–04–16
  6. By: Suleyman Hilmi Kal; Nuran Arslaner; Ferhat Arslaner
    Abstract: This paper aims to investigate whether the effect of inflation expectations, exchange rate, money supply, industrial production and import prices on inflation depends on business cycle. For this purpose, a two states Markov Switching Auto Regression model with time varying transition probabilities to a generic inflation model is implemented for the period 2003-2013. In the model the states are assigned whether output gap is positive or negative. The inflation forecasting in-sample and out-of-sample is also utilized by adopting mean squared error and Diebold Mariano test to measure explanatory and forecasting power of our model. Our main finding provides that the determinants of inflation have different dynamics during boom periods as compared to recessions.
    Keywords: Inflation; Output Gap; Markov Switching Autoregressions; Business Cycles
    JEL: C32 E30 E31 E37 E58
    Date: 2014–03
  7. By: Hännikäinen, Jari
    Abstract: This paper re-examines the out-of-sample predictive power of interest rate spreads when the short-term nominal rates have been stuck at the zero lower bound and the Fed has used unconventional monetary policy. Our results suggest that the predictive power of some interest rate spreads have changed since the beginning of this period. In particular, the term spread has been a useful leading indicator since December 2008, but not before that. Credit spreads generally perform poorly in the zero lower bound and unconventional monetary policy period. However, the mortgage spread has been a robust predictor of economic activity over the 2003–2014 period.
    Keywords: business fluctuations; forecasting; interest rate spreads; monetary policy; zero lower bound; real-time data
    JEL: C53 E32 E44 E52 E58
    Date: 2014–06–18
  8. By: D'Amico, Stefania (Federal Reserve Bank of Chicago); Kim, Don H. (Board of Governors of the Federal Reserve System (U.S.)); Wei, Min (Board of Governors of the Federal Reserve System (U.S.))
    Abstract: TIPS are notes and bonds issued by the U.S. Treasury with coupons and principal payments indexed to inflation. Using no-arbitrage term structure models, we show that TIPS yields contained liquidity premiums as large as 100 basis points when TIPS were first issued, reflecting the newness of the instrument, and up to 350 basis points during the recent financial crisis, reflecting common funding constraints affecting a variety of financial markets. Applying our models to the U.K. data also reveals liquidity premiums in index-linked gilt yields that spiked to nearly 250 basis points at the height of the crisis. Ignoring TIPS liquidity premiums is shown to significantly distort the information content of TIPS yields and TIPS breakeven inflation rate, two widely-used empirical proxies for real rates and expected inflation.
    Keywords: TIPS; liquidity premium; no-arbitrage term structure model; TIPS breakeven inflation; expected inflation; inflation risk premium; survey forecasts
    Date: 2014–01–31
  9. By: Gabriel Desgranges (THEMA - Théorie économique, modélisation et applications - CNRS : UMR8184 - Université de Cergy Pontoise); Stéphane Gauthier (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris)
    Abstract: We study how asymmetric information affects the set of rationalizable solutions in a linear setup where the outcome is determined by forecasts about this same outcome. The unique rational expectations equilibrium is also the unique rationalizable solution when the sensitivity of the outcome to agents' forecasts is less than one, provided that this sensitivity is common knowledge. Relaxing this common knowledge assumption, multiple rationalizable solutions arise when the proportion of agents who know the sensitivity is large, and the uninformed agents believe it is possible that the sensitivity is greater than one. Instability is equivalent to existence of some kind of sunspot equilibria.
    Keywords: Asymmetric information; common knowledge; eductive learning; rational expectations; rationalizability
    Date: 2013–11

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