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Laborda, J.; Ruano, S.; Zamanillo, I. Predictors of GDP Growth. Encyclopedia. Available online: https://encyclopedia.pub/entry/45906 (accessed on 16 November 2024).
Laborda J, Ruano S, Zamanillo I. Predictors of GDP Growth. Encyclopedia. Available at: https://encyclopedia.pub/entry/45906. Accessed November 16, 2024.
Laborda, Juan, Sonia Ruano, Ignacio Zamanillo. "Predictors of GDP Growth" Encyclopedia, https://encyclopedia.pub/entry/45906 (accessed November 16, 2024).
Laborda, J., Ruano, S., & Zamanillo, I. (2023, June 21). Predictors of GDP Growth. In Encyclopedia. https://encyclopedia.pub/entry/45906
Laborda, Juan, et al. "Predictors of GDP Growth." Encyclopedia. Web. 21 June, 2023.
Predictors of GDP Growth
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This entry is adapted from the peer-reviewed paper 10.3390/math11122625

Over decades, economists devoted a substantial amount of effort to model economic growth. There exists a wide literature that supports the importance of different kinds of variables to predict the evolution of GDP. 

GDP deep learning time fusion transformers multi-horizon forecasting

1. Financial Variables and Leading Indicators

Financial variables, such as the prices of financial instruments, interest rates, interest rate spreads, stock price indexes, and monetary aggregates, have significant predictive content for economic activity since they are forward-looking variables, and therefore, are useful indicators in macroeconomic prediction. For a comprehensive literature review, see [1].
1. The Yield Curve. The spreads between interest rates for different maturities tend to be interpreted as the market expectations of future rates corresponding to the period between the two maturities. Intuitively, long-term rates incorporate the expectations of financial markets on future short-term rates. Consequently, a negative-sloped or flat curve means that markets’ prospects involve a decrease in future real interest rates, which is associated with weak economic activity or downturn.
Evidence on the predictive power of the spread between long-term and short-term government bond rates, called the slope of the yield curve, for inflation and real economic activity is wide and robust across countries and time periods ([2][3][4][5][6][7][8]).
Ref. [9] provides the theoretical basis for this statistical evidence. In particular, the main implication of the analytical rational expectations model is that the relationships are not structural since they are influenced by the monetary policy regime. In other words, the extent to which the yield curve is a good predictor depends on the form of the monetary policy reaction function, which, in turn, may depend on explicit policy objectives. The yield curve has predictive power, for example, if the monetary authority follows strict or flexible inflation targeting or if policy follows the [10] rule.
2. Corporate Bond Spreads. Asset purchase programs, forward guidance, and other unconventional monetary policies can lower long-term interest rates, altering the information content of the yield curve. However, even in such circumstances, the behavior of the corporate bond credit spread curve varies over the business cycle, potentially containing more information about the future.
Many studies focused on corporate bond spreads ([11][12][13][14][15][16][17]), providing strong evidence for the link between this spread and the economic activity.
Researchers include in the model the ratio of the Moody’s U.S. Baa corporate bond yields to that of Aaa as a global proxy for credit spread.
3. The Composite Leading Indicator. The combination of multiple leading variables in composite leading indicators (CLIs) pursues a more accurate prediction of the development of the reference series. CLIs are designed to predict the development of the business cycle, focusing on the identification of turning points that occur when the growth rate moves from an expansion period to a contraction period or vice versa. Empirical evidence supporting the usefulness of the CLI, both in-sample and out-of-sample real-time, in a real time context, is wide. Some examples are [2][18][19][20][21].
This CLI shows short-term economic movements in qualitative rather than quantitative terms. A CLI reading above (below) 100 precedes levels of GDP above (below) its long-term trend.
4. The Industrials Commodity Price Index. The CRB Raw Industrials Spot Index, drawn from Bloomberg, is a synthetic measure of price movements of 13 sensitive basic commodities whose markets are presumed to be among the first to be influenced by changes in economic conditions. As such, it serves as one early indication of imminent changes in business activity.
The criteria for the selection of commodities are: (i) wide use for further processing (basic); (ii) freely traded in an open market; (iii) sensitive to changing conditions significant in those markets; and (iv) sufficiently homogeneous or standardized so that uniform and representative price quotations can be obtained over a period of time.
Then, the Spot Market Index is defined as the unweighted geometric mean of the individual commodity price relatives (i.e., the ratios of the current prices to the base period prices).
Different papers empirically examine the interactions between commodity prices, money, interest rates, goods, and economic growth ([22][23][24][25][26]). In particular, Ref. [26] explores how the commodity market can predict GDP growth for countries worldwide, rather than a few specific countries or regions. They find commodity returns significantly predict the next quarter’s GDP growth, and thus can be considered as leading indicators of economic growth.

2. The Credit Cycle

The credit cycle and the economic cycle are closely related. Many studies provide empirical evidence supporting that endogenous credit supply expansions precede a decline in real GDP (see [27], for a review). The intuition is that, in the supply side of financial markets, risk appetite and the debt accumulation evolve over the business cycle following a regular process, and ultimately, this credit cycle translates to the real economy through defaults that materialize credit risk, and the end, financial constraints affecting the real economy. In particular, the Minsky’s financial instability hypothesis ([28][29][30][31]) predicts that, for a given microeconomic condition, the likelihood of facing credit constraints decreases in periods of GDP expansion and increases in periods of contraction.
Specifically, it is defined as the ratio of the total debt of non-financial private sectors at market value of one country over its nominal GDP.

3. World Trade and Economic Integration across Countries

As was first stressed by the classics, Adam Smith and David Ricardo, trade promotes growth by allowing the optimal use of resources. Empirical evidence is profuse and supports that trade tends to favor development, given that it stimulates technical progress, which is spread across countries through the importation of capital goods that incorporate innovations (for a survey, see [32]).
Particularly, exports promote economic growth through several channels: they enhance a better allocation of resources through specialization on goods that have an improved comparative advantage, favoring productivity gains through economies of scale, spillover effects, and learning-by-doing. In this sense, trade integration enables a higher external demand that increases the probability and/or intensity of exporting, and therefore, of economic growth, especially in periods where domestic demand is under pressure ([33][34][35]).
International trade was also identified as a channel through which shocks are internationally transmitted, contributing to the synchronization in business cycles across countries. In particular, countries joining a currency union may lose their ability to stabilize cyclical fluctuations through independent counter-cyclical monetary policy. In general, empirical research found that pairs of countries with relatively strong economic linkages, not only in terms of trade intensity, but also in terms of financial and institutional integration, tend to have highly correlated business cycles. For example, Refs. [36][37][38] find that the closer the trade linkages are, the higher the correlation in countries’ business cycles are as well. Similarly, Ref. [39] shows that more financially integrated countries display more correlated business cycles.
Researchers incorporate in the model the World Trade Volume Index that is monthly computed by the Netherlands Bureau for Economic Policy Analysis. This index, defined as the arithmetic average of world exports and imports of goods, constitutes an indicator of global economic activity. It covers the United States, Japan, EU, and four groups of emerging countries: Asian countries (excluding Japan), Eastern Europe and CIS countries, Latin America, and Africa and the Middle East.
Here, researchers have to emphasize the ability of the temporal fusions transformers methodology to capture cross-country business cycle co-movements, even if the drivers of this synchronization are not explicitly introduced in the list of explanatory variables.

References

  1. Stock, J.H.; Watson, M.W. Forecasting output and inflation: The role of asset prices. J. Econ. Lit. 2003, 41, 788–829.
  2. Estrella, A.; Mishkin, F.S.; Predicting, U.S. recessions: Financial variables as leading indicators. Rev. Econ. Stat. 1998, 80, 45–61.
  3. Chauvet, M.; Potter, S. Forecasting recessions using the yield curve. J. Forecast. 2005, 24, 77–103.
  4. Harvey, C. The real term structure and consumption growth. J. Financ. Econ. 1988, 22, 305–333.
  5. Laurent, R.D. An interest rate-based indicator of monetary policy. Econ. Perspect. 1988, 12, 3–14.
  6. Estrella, A.; Hardouvelis, G. The term structure as a predictor of real economic activity. J. Financ. 1991, 46, 555–576.
  7. Estrella, A.; Mishkin, F.S. The term structure of interest rates and its role in monetary policy in Europe and the United States: Implications for the European Central Bank. Eur. Econ. Rev. 1997, 41, 1375–1401.
  8. Bernard, H.; Gerlach, S. Does the term structure predict recessions? The international evidence. Int. J. Financ. Econ. 1998, 3, 195–215.
  9. Estrella, A. Why does the yield curve predict output and inflation? Econ. J. 2005, 11, 722–744.
  10. Taylor, J.B. Discretion versus policy rules in practice. J. Monet. Econ. 1993, 39, 195–214.
  11. Gilchrist, S.; Yankov, V.; Zakrajšek, E. Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets. J. Monet. Econ. 2009, 56, 471–493.
  12. Gilchrist, S.; Zakrajšek, E. Credit spreads and business cycle fluctuations. Am. Econ. Rev. 2012, 102, 1692–1720.
  13. Faust, J.; Gilchrist, S.; Wright, J.H.; Zakrajšek, E. Credit spreads as predictors of real-time economic activity: A Bayesian model-averaging approach. Rev. Econ. Stat. 2013, 95, 1501–1519.
  14. Bleaney, M.; Mizen, P.; Veleanu, V. Bond spreads and economic activity in eight European economies. Econ. J. 2016, 126, 2257–2291.
  15. Okimoto, T.; Takaoka, S. The term structure of credit spreads and business cycle in Japan. J. Jpn. Int. 2017, 45, 27–36.
  16. Okimoto, T.; Takaoka, S. The credit spread curve distribution and economic fluctuations in Japan. J. Int. Money Financ. 2022, 122, 102582.
  17. Gilchrist, S.; Mojon, B. Credit risk in the Euro area. Econ. J. 2018, 128, 118–158.
  18. Hamilton, J.D.; Pérez-Quirós, G. Do the Leading Indicators Lead? J. Bus. 1996, 69, 27–49.
  19. Banerjee, T.; Marcellino, M. Are there any reliable leading indicators for US inflation and GDP growth? Int. J. Forecast. 2006, 22, 137–151.
  20. Kulendran, N.; Wong, K.F. Determinants versus Composite Leading Indicators in Predicting Turning Points in Growth Cycle. J. Travel Res. 2011, 50, 417–430.
  21. Tkacova, A.; Gavurova, B.; Behun, M. The Composite Leading Indicator for German Business Cycle. J. Compet. 2017, 9, 114–133.
  22. Hanson, M.S. The “price puzzle” reconsidered. J. Monet. Econ. 2004, 51, 1385–1413.
  23. Beckmann, J.; Belke, A.; Czudaj, R. Does global liquidity drive commodity prices? J. Bank. Financ. 2014, 48, 224–234.
  24. Belke, A.; Bordon, I.; Hendricks, T.W. Monetary policy, global liquidity and commodity price dynamics. N. Am. J. Econ. Financ. 2014, 28, 1–16.
  25. Yardeni, E. Predicting the Markets; YRI Press: Brookville, NY, USA, 2018.
  26. Ge, Y.; Tang, K. Commodity prices and GDP growth. Int. Rev. Financial Anal. 2020, 71, 101512.
  27. Mian, A.R.; Sufi, A. Finance and business cycles: The credit-driven household demand channel. J. Econ. Perspect. 2018, 32, 31–58.
  28. Minsky, H.P. Stabilizing an Unstable Economy; Yale University Press: New Haven, CT, USA, 1986.
  29. Minsky, H.P. The financial Instability Hypothesis; Working Paper 74; The Jerome Levy Economics Institute of Bard College: Annandale-On-Hudson, NY, USA, 1992.
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  31. Minsky, H.P. The Financial Instability Process: A Restatement; Post Keynesian Economic Theory; Arestis, P., Shouras, T., Eds.; Wheatsheaf Books: Sussex, UK, 1985.
  32. Singh, T. Does International Trade Cause Economic Growth? A Survey. World Econ. 2010, 33, 1517–1564.
  33. Esteves, P.S.; Rua, A. Is there a role for domestic demand pressure on export performance? Empir. Econ. 2015, 49, 1173–1189.
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  35. Laborda, J.; Salas, V.; Suárez, C. Manufacturing firms’ export activity: Business and financial cycles overlaps! Int. Econ. 2020, 162, 1–14.
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  37. Clark, T.E.; Van Wincoop, E. Borders and business cycle. J. Int. Econ. 2001, 55, 59–85.
  38. De Soyres, F.; Gaillard, A. Global trade and GDP comovement. J. Econ. Dyn. Control 2022, 138, 104353.
  39. Imbs, J. Trade, finance, specialization and synchronization. Rev. Econ. Stat. 2004, 86, 723–734.
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Subjects: Economics
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Juan Laborda
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Sonia Ruano
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Ignacio Zamanillo
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Revisions: 2 times (View History)
Update Date: 25 Jun 2023
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