nep-for New Economics Papers
on Forecasting
Issue of 2013‒01‒12
six papers chosen by
Rob J Hyndman
Monash University

  1. Real-time forecasting in a data-rich environment By Liebermann, Joelle
  2. Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment By Katja Drechsel; Rolf Scheufele
  3. Nowcasting Turkish GDP Growth By Huseyin Cagri Akkoyun; Mahmut Gunay
  4. Predicting quarterly aggregates with monthly indicators By Winkelried, Diego
  5. The value of hard and soft data for short-term forecasting of GDP By Keeney, Mary; Kennedy, Bernard; Liebermann, Joelle
  6. Predicting crises: Five essays on the mathematic prediction of economic and social crises By Albers, Scott

  1. By: Liebermann, Joelle (Central Bank of Ireland)
    Abstract: This paper assesses the ability of dierent models to forecast key real and nominal U.S. monthly macroeconomic variables in a data-rich environment from the perspective of a realtime forecaster, i.e. taking into account the real-time data revisions process and data ow. We nd that for the real variables predictability is conned over the recent recession/crisis period. This is in line with the ndings of D'Agostino and Giannone (2012) that gains in relative performance of models using large datasets over univariate models are driven by downturn periods which are characterized by higher comovements. Regarding in ation, results are stable across time, but predictability is mainly found at the very short-term horizons. In ation is known to be hard to forecast, but by exploiting timely information one obtains gains at nowcasting and forecasting one-month ahead, especially with Bayesian VARs. Furthermore, for both real and nominal variables, the direct pooling of information using a high dimensional model (dynamic factor model or Bayesian VAR) which takes into account the cross-correlation between the variables and eciently deals with the \ragged edge" structure of the dataset, yields more accurate forecasts than the indirect pooling of bi-variate forecasts/models.
    Keywords: Real-time data, Nowcasting, Forecasting, Factor model, Bayesian VAR, Forecast pooling
    JEL: C11 C33 C53 E52
    Date: 2012–12
  2. By: Katja Drechsel; Rolf Scheufele
    Abstract: This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows to retrace the driving forces of the forecast and hence enables the interpretability of the forecast outcome.
    Keywords: Contemporaneous aggregation, nowcasting, leading indicators, MIDAS, forecast combination,forecast evaluation
    JEL: E32 E37 C52 C53
    Date: 2012
  3. By: Huseyin Cagri Akkoyun; Mahmut Gunay
    Abstract: In this paper we present backcasts and nowcasts for quarter on quarter Gross Domestic Product (GDP) growth for Turkish economy. GDP growth is one of the most important economic indicators since GDP figures provide comprehensive information regarding the economic activity. GDP data are published with considerable delay, so early estimates of GDP growth may be valuable. For this aim, we use an extended version of the Stock and Watson coincident indicator model that can deal with mixed frequency (such as quarterly and monthly variables), ragged ends (some indicators are published before others), and missing data (data may not be available at the beginning of the sample for some variables). As soft data we use PMI, and as hard data we use industrial production, import and export quantity indices. We perform simulated out of sample forecasting exercise by taking the ?ow of data releases for 2008Q1-2012Q2 into account. Results show that nowcasts obtained with a model including a soft indicator tracks the GDP growth relatively successfully. Also, the model outperforms benchmark AR model.
    Keywords: Time Series, Forecasting, Output Growth
    JEL: C22 C53 E37
    Date: 2012
  4. By: Winkelried, Diego (Central Reserve Bank of Peru)
    Abstract: Many important macroeconomic variables measuring the state of the economy are sampled quarterly and with publication lags, although potentially useful predictors are observed at a higher frequency almost in real time. This situation poses the challenge of how to best use the available data to infer the state of the economy. This paper explores the merits of the so-called Mixed Data Sampling (MIDAS) approach that directly exploits the information content of monthly indicators to predict quarterly Peruvian macroeconomic aggregates. To this end, we propose a simple extension, based on the notion of smoothness priors in a distributed lag model, that weakens the restrictions the traditional MIDAS approach imposes on the data to achieve parsimony. We also discuss the workings of an averaging scheme that combines predictions coming from non-nested specifications. It is found that the MIDAS approach is able to timely identify, from monthly information, important signals of the dynamics of the quarterly aggregates. Thus, it can deliver significant gains in prediction accuracy, compared to the performance of competing models that use exclusively quarterly information.
    Keywords: Mixed-frequency data, MIDAS, model averaging, nowcasting, backcasting
    JEL: C22 C53 E27
    Date: 2012–12
  5. By: Keeney, Mary (Central Bank of Ireland); Kennedy, Bernard (Central Bank of Ireland); Liebermann, Joelle (Central Bank of Ireland)
    Abstract: When monitoring and assessing the state of the economy in real time, policymakers face the problem that Gross Domestic Product (GDP) is released with a lag. For the euro area, the first estimate of GDP for a reference quarter is only released six weeks after the close of the quarter. In the interim period, one can use monthly conjunctural indicators to obtain a more timely estimate of GDP. These indicators include hard data, such as industrial production, and soft data such as PMI surveys. However, the hard data for a reference month are only released with a one or two month lag, whereas the soft data are released at the end of the reference month. Hence, one faces a potential trade-off between reliability and timeliness of information. This letter illustrates the value of soft and hard data for computing an early GDP estimate by running a pseudo real-time forecasting exercise.
    Date: 2012–10
  6. By: Albers, Scott
    Abstract: This volume – Predicting Crisis: Five Essays on the Mathematic Prediction of Economic and Social Crises – is the first of three sets of essays. In this first set the economic and social history of the United States is shown to be a “system of movement,” i.e. a logical and mathematic progression of events which may be analyzed according to a set formula. The model proposed demonstrates that the citizen’s individual “consciousness” is writ large in the macro-economic statistics of this unique economy and thereby made available for inspection at other levels of reality.
    Keywords: Fifth dimension; consciousness; unemployment; Okun’s Law; real GNP; crisis; prediction; mathematics; economic history; cycle; Kondratiev wave; long wave; Golden Mean; phi; pi; mathematic ratio; octave; music; political economy wave; Kaluza; General Theory of Relativity; complexity
    JEL: B51 E0 B31 C0 B4 N00 E1 B52 N0 C02 B24 B5 E3 B0 K0 C00 C5 K00 B50 D4 A10 D0 C53 B40 A13 A12 B59 C22 D00 B41
    Date: 2012–12–29

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