nep-for New Economics Papers
on Forecasting
Issue of 2013‒10‒05
six papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting GDP at the regional level with many predictors By Lehmann, Robert; Wohlrabe, Klaus
  2. Can Economic Uncertainty, Financial Stress and Consumer Sentiments Predict U.S. Equity Premium? By Rangan Gupta; Shawkat Hammoudeh; Mampho P. Modise; Duc Khuong
  3. Bank lending in a cointegrated VAR model By Filippo Maria Pericoli; Roberto Galli; Cecilia Frale; Stefania Pozzuoli
  4. Macroeconomic forecasting and structural analysis through regularized reduced-rank regression By Emmanuela Bernardini; Gianluca Cubadda
  5. Mobility in an Enlarging European Union: Projections of Potential Flows from EU's Eastern Neighbors and Croatia By Fertig, Michael; Kahanec, Martin
  6. Can Information Demand Help to Predict Stock Market Liquidity ? Google it ! By Mohamed Arouri; Amal Aouadi; Philippe Foulquier; Frédéric Teulon

  1. By: Lehmann, Robert; Wohlrabe, Klaus
    Abstract: In this paper, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden- Württemberg) and Eastern Germany. We overcome the problem of a ’data-poor environment’ at the sub-national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single– indicator, multi–indicator, pooled and factor forecasts in a pseudo real–time setting. Our results show that we can significantly increase forecast accuracy compared to an autoregressive benchmark model, both for short and long term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP.
    Keywords: regional forecasting; forecast combination; factor models; model confidence set; data–rich environment
    JEL: C32 C52 C53 E37 R11
    Date: 2013–09–14
  2. By: Rangan Gupta; Shawkat Hammoudeh; Mampho P. Modise; Duc Khuong
    Abstract: This article attempts to examine whether the equity premium in the United States can be predicted from a comprehensive set of 18 economic and financial predictors over a monthly out-of-sample period of 2000:2 to 2011:12, using an in-sample period of 1990:2-2000:1. To do so, we consider, in addition to the set of variables used in Rapach and Zhou (2013), the forecasting ability of four other important variables: the US economic policy uncertainty, the equity market uncertainty, the University of Michigan’s index of consumer sentiment, and the Kansas City Fed’s financial stress index. Using a more recent dataset compared to that of Rapach and Zhou (2013), our results from predictive regressions show that the newly added variables do not play any significant statistical role in explaining the equity premium relative to the historical average benchmark over the out-of-sample horizon, even though they are believed to possess valuable informative content about the state of the economy and financial markets. Interestingly, however, barring the economic policy uncertainty index, the three other indexes considered in this study yields economically significant out-of-sample gains, especially during recessions, when compared to the historical benchmark.
    Keywords: Equity premium forecasting, asset pricing model, economic uncertainty, business cycle
    JEL: C22 C38 C53 C58 E32 G11 G12 G14 G17
    Date: 2013–09–01
  3. By: Filippo Maria Pericoli; Roberto Galli; Cecilia Frale; Stefania Pozzuoli
    Abstract: This paper aims at identifying the link between financial markets and the real sector of the economy. Following the literature on the topic, we select a small set of variables representing the principal financial and real dynamics observed for the Italian economy. As a first result, we find cointegration among the chosen set of variables. Thus we specify and estimate a Vector Error Correction Model which captures both the long-run and the short-term dynamics of the multivariate system. The main innovation of this work lies in investigating the link between lending and growth at a monthly frequency. Moreover, we allow the model to include a structural break due to the latest economic and financial crisis. The model obtained represents an innovative forecasting tool for improving the knowledge, nowcasting and shortterm forecasting of the business cycle by exploiting shocks originating from the lending market that propagate to the real economy.
    Keywords: Bank Lending, Forecast, Cointegrated VAR
    JEL: C53 E47 E51
    Date: 2013–09
  4. By: Emmanuela Bernardini (Banca d'Italia); Gianluca Cubadda (University of Rome "Tor Vergata")
    Abstract: This paper proposes a strategy to detect and impose reduced-rank restrictions in medium vector autoregressive models. In this framework, it is known that Canonical Correlation Analysis (CCA) does not perform well because inversions of large covariance matrices are required. We propose a method that combines the richness of reduced-rank regression with the simplicity of naive univariate forecasting methods. In particular, we suggest to use a proper shrinkage estimator of the autocovariance matrices that are involved in the computation of CCA, thus obtaining a method that is asymptotically equivalent to CCA, but it is numerically more stable in finite samples. Simulations and empirical applications document the merits of the proposed approach both in forecasting and in structural analysis.
    Keywords: Reduced rank regression; vector autoregressive models; shrinkage estimation; macroeconomic forecasting.
    JEL: C32
    Date: 2013–10–03
  5. By: Fertig, Michael (ISG, Cologne); Kahanec, Martin (Central European University)
    Abstract: This study evaluates potential migration flows to the European Union from its eastern neighbors and Croatia. We perform out-of-sample forecasts using an adaption of the model of Hatton (1995) to time series cross-sectional data about post-enlargement migration flows following the EU's 2004 enlargement. We consider two baseline policy scenarios, with and without accession of sending countries to the EU. Our results show that migration flows are driven by migration costs and economic conditions, but the largest effects accrue to policy variables. In terms of the predicted flows: (i) we can expect modest migration flows in case of no liberalization of labor markets and only moderately increased migration flows under liberalization; (ii) after an initial increase following liberalization, migration flows will subside to long run steady state; (iii) Ukraine will send the most migrants; and (iv) the largest inflows in absolute terms are predicted for Germany, Italy and Austria, whereas Ireland, Denmark, Finland and again Austria are the main receiving countries relative to their population.
    Keywords: migration, free movement of workers, European Union, Eastern Partnership, EU enlargement, migration potential, out-of-sample forecasting
    JEL: F22 C23 C53
    Date: 2013–09
  6. By: Mohamed Arouri; Amal Aouadi; Philippe Foulquier; Frédéric Teulon
    Abstract: Numerous recent studies indicate that investors’ information demand affects stock market return and volatility. In this paper, we contribute to the literature by investigating whether information demand is a significant determinant of liquidity in the French stock market. Our main findings suggest that internet research volume tends to be positively related to market liquidity. In the out-of-sample analysis, we show that introducing information demand variables significantly improves liquidity forecasting.
    Keywords: Information demand, Financial markets, Stock liquidity.
    JEL: C32 D83 G12 G14
    Date: 2013–09–26

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