nep-for New Economics Papers
on Forecasting
Issue of 2015‒01‒19
four papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Czech GDP Using Mixed-Frequency Data Models By Michal Franta; David Havrlant; Marek Rusnak
  2. Range-based Volatility Estimation and Forecasting By Daniel Bencik
  3. A Consumption-Based Approach to Exchange Rate Predictability By Jair N. Ojeda-Joya
  4. On the dynamics of the primary housing market and the forecasting of house prices By Augustyniak, Hanna; Leszczyński, Robert; Łaszek, Jacek; Olszewski, Krzysztof; Waszczuk, Joanna

  1. By: Michal Franta; David Havrlant; Marek Rusnak
    Abstract: In this paper we use a battery of various mixed-frequency data models to forecast Czech GDP. The models employed are mixed-frequency vector autoregressions, mixed-data sampling models, and the dynamic factor model. Using a dataset of historical vintages of unrevised macroeconomic and financial data, we evaluate the performance of these models over the 2005–2012 period and compare them with the Czech National Bank’s macroeconomic forecasts. The results suggest that for shorter forecasting horizons the accuracy of the dynamic factor model is comparable to the CNB forecasts. At longer horizons, mixed-frequency vector autoregressions are able to perform similarly or slightly better than the CNB forecasts. Furthermore, moving away from point forecasts, we also explore the potential of density forecasts from Bayesian mixed-frequency vector autoregressions.
    Keywords: GDP, mixed-frequency data, real-time data, short-term forecasting
    JEL: C53 C82 E52
    Date: 2014–11
  2. By: Daniel Bencik (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nábreží 6, 111 01 Prague 1, Czech Republic)
    Abstract: In this paper, we analyze new possibilities in predicting daily ranges, i.e. differences between daily high and low prices. We empirically assess efficiency gains in volatility estimation when using range-based estimators as opposed to simple daily ranges and explore the use of these more efficient volatility measures as predictors of daily ranges. The array of models used in this paper include the heterogeneous autoregressive model, conditional autoregressive ranges model and a vector error-correction model of daily highs and lows. Contrary to intuition, models based on co-integration of daily highs and lows fail to produce good quality out of sample forecasts of daily ranges. The best one-day-ahead daily ranges forecasts are produced by a realized range based HAR model with a GARCH volatility-of-volatility component.
    Keywords: volatility, returns, futures contracts, cointegration, prediction
    JEL: C52 C53 C58
    Date: 2014–12
  3. By: Jair N. Ojeda-Joya
    Abstract: This paper provides evidence of short-run predictability for the real exchange rate by performing out-of-sample tests of a forecasting equation which is derived from a consumption-based asset pricing model. In this model, the real exchange rate is predictable as a result of the implications of preferences with habit persistence on the pricing of international assets. The implied predictors are: domestic, US and world consumption growth. Empirical exercises show evidence of short-term predictability on the bilateral rates of 15 out of 17 countries vis-à-vis the US over the post Bretton-Woods float. A GMM estimation of the parameters of the model also finds evidence of the presence of habits in consumers’ preferences.
    Keywords: Exchange rates, out-of-sample, predictability, asset pricing, habits
    JEL: C5 F31 F47 G15
    Date: 2014–12–12
  4. By: Augustyniak, Hanna; Leszczyński, Robert; Łaszek, Jacek; Olszewski, Krzysztof; Waszczuk, Joanna
    Abstract: This article discusses and explains the dynamics of the primary housing market, focus-ing on housing supply, demand, price and construction costs dynamics. We focus our attention on the primary housing market, because it can create an excessive supply, which can cause distress to the economy. Due to multiplier effects, even small changes in fundamental factors, such as a minor changes in the interest rate, result in demand shocks. Positive demand shifts cannot be easily satisfied, as supply is rigid in the short run. This usually makes house prices grow and developers increase their production, which will be delivered to the market with a lag. Housing developers have the marketing tools to heat up the market for a prolonged period of time. Rising prices can lead to further demand increases, because housing is a consumer and an investment good. When demand moves back to its long run level, the economy is left with excessive supply, falling prices and bad mortgages. We create a simple four-equation model, which is able to replicate the dynamics of the Warsaw primary housing market. Our model replicates historical data in an appropriate way and we apply it to forecast house prices in the next two years on quarterly basis.
    Keywords: Housing market cycles, disequilibrium demand and supply forecast
    JEL: E32 E37 E44 R21 R31
    Date: 2014–12

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