پیش بینی رشد تولید ناخالص داخلی با داده های بازار مالی در فنلاند: بازنگری حقایق تجربی در یک اقتصاد کوچک باز در طول بحران مالی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|14192||2013||8 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Review of Financial Economics, Available online 26 October 2013
This paper examines the ability of financial variables to predict future economic growth above and beyond past economic activity in a small open economy in the euro area. We aim to clarify potential differences in forecasting economic activity during different economic circumstances. Our results from Finland suggest that the proper choice of forecasting variables is related to general economic conditions. During steady economic growth, the preferred choice for a financial indicator is the short-term interest rate combined with past values of output growth. However, during economic turbulence, the traditional term spread and stock returns are more important in forecasting GDP growth. The time-varying predictive content of the financial variables may be utilized by applying regime-switching nonlinear forecasting models. We propose a novel application using the negative term spread and observed recession as signals to switch between regimes. This procedure yields a significant improvement in forecasting performance at the one-year forecast horizon.
What will be the GDP growth in your country during the next quarter or the next year? Because economic growth is known to be positively serially correlated, the persistence of growth provides a natural starting point for predicting future economic growth during steady economic conditions. However, economic turmoil may pose additional challenges to forecasting. Economists would certainly like to have more predictors of economic growth than simply past growth. Financial market data are forward-looking aggregators of information that are easy to interpret and are observed in real time without measurement errors. Therefore, since the beginning of the 1980s, the potential to utilize financial market information to forecast economic activity has been explored. Certain financial variables, such as interest rates, term spreads and stock returns, are examples of readily available and precise indicators; however, can these variables provide robust forecasts of future economic activity during both steady growth periods and more turbulent conditions? Since the late 1980s, many studies have documented the usefulness of the yield curve or even the simple term spread for predicting economic activity (e.g., Estrella, 2005a, Estrella and Hardouvelis, 1991, Harvey, 1988, Laurent, 1989 and Stock and Watson, 2003). It has become a standard procedure in the U.S. to use the term spread between the ten-year Treasury note and the three-month Treasury bill to predict recessions and future economic activity (e.g., Estrella and Mishkin, 1996 and Haubrich and Dombrosky, 1996). The inversion of the term spread has been demonstrated to be a reliable “advance warning” of a subsequent recession; however, its ability to forecast GDP growth rates is less clear. Many studies have found that since 1985, the term spread has been a less accurate predictor of U.S. output growth (e.g., Chinn and Kucko, 2010 and Stock and Watson, 2003). This phenomenon may reflect either the increased stability of output growth (the Great Moderation) and of other macroeconomic variables since the mid-1980s or changes in the responsiveness of monetary policy to output growth and inflation (Wheelock & Wohar, 2009). If the central bank concentrates exclusively on controlling inflation, then the term spread will most likely be a less accurate predictor of GDP growth (Estrella, 2005b; Stock & Watson, 2003). Despite evidence that parameter instability may weaken the performance of the term spread in predicting economic growth, the spread has gained acceptance as the single best indicator of economic activity and a “near-perfect tool” for forecasting (e.g., Estrella, 2005a). Notwithstanding the predominance of the term spread in forecasting economic activity, Ang, Piazzesi, and Wei (2006) found that the short-term interest rate had more predictive power than any term spread for forecasting GDP growth in the U.S. during the period from 1952 to 2001. Stock prices are forward looking and thus represent another obvious financial indicator of future economic activity. Economists and investors have a well-known rule of thumb that stock market prices predict economic growth approximately one-half year in advance. However, compared with the predictive content of the term spread, less empirical evidence exists regarding the ability of stock prices to predict economic activity (e.g., Stock & Watson, 2003). Chionis, Gogas, and Pragidis (2010) found that augmenting the yield curve with a stock index significantly improved the ability to predict GDP fluctuations in the euro area. Nyberg's (2010) results supported this conclusion with respect to predicting recessions in Germany and the U.S. Junttila and Korhonen (2011) discovered that both stock market dividend yields and short-term interest rates were relevant information variables for forecasting future economic activity in the U.K., the euro area and Japan, particularly during turbulent times. Furthermore, Henry, Olekalns, and Thong (2004) emphasized that stock returns predict GDP when the economy is contracting but that the predictive power of stock returns in non-recession periods is less clear. This mixed evidence is expressed in Samuelson's (1966) famous note: “The stock market has predicted nine out of the last five recessions.” In any event, economic turbulence tends to strengthen the link between the stock market and economic activity. The case of Finland is interesting in many ways. The vast majority of the previous literature has examined larger countries, particularly the G7; however, the predictive content of financial variables is less known in smaller European countries. As a member of the Economic and Monetary Union (EMU), the Finnish economy is subject to the monetary policy of the European Central Bank (ECB), which strongly targets inflation rather than economic growth. Moreover, the monetary policy of the ECB is conducted on the basis of the entire euro area; therefore, interest rates in the euro area may be far from optimal for smaller euro countries that face asymmetric shocks. Indeed, evidence suggests that output shocks have been more country-specific in Finland than in other EU countries (e.g., Haaparanta and Peisa, 1997 and Kinnunen, 1998), and the question of asymmetric shocks was among the main concerns when Finland considered EMU membership in the late 1990s. Therefore, there are good reasons to assess the predictive content of the term spread and the short-term interest rate in small member countries in the euro area. After recovering from an economic depression during the 1990s, Finland experienced an era of continuous and sound growth until the global financial crisis plunged the Finnish economy into a deep recession in 2008. A distinctive feature of this slump was its severity; in a single year, Finland's GDP collapsed by an astonishing 10%, one of the largest decreases in economic activity among developed countries (see Fig. 1). Undoubtedly, the ups and downs of the Finnish economy pose a true challenge for forecasting economic activity. Full-size image (25 K) Fig. 1. The annual GDP growth in Finland and the forecast periods. Figure options This paper contributes to the existing literature by explicitly addressing the predictive content of the classical term spread versus the short-term interest rate and stock returns in the context of a small open economy (SOE). Ang et al. (2006) found that compared with the term spread, short-term interest rates were a better predictor of economic activity in the U.S. Our aim is to test whether this result is specific to the U.S. or whether it holds true for other countries as well. Furthermore, we seek to clarify potential differences in forecasting economic activity between eras of steady growth and economic turbulence, such as the financial and debt crises in Europe. Many of the previous studies have concentrated on the predictive content of a single financial indicator (e.g., Stock & Watson, 2003); however, we assess the predictive content of combinations of indicators. More broadly, this paper provides further information on the predictive ability of financial market indicators in smaller economies, a context that has rarely been examined in the previous literature. The remainder of this paper is organized as follows. Section 2 presents the model setup and the data. Section 3 contains the empirical analysis of the study, and Section 4 concludes.
نتیجه گیری انگلیسی
The purpose of this study was to reinvestigate and clarify the predictive content of readily available and easily observable financial market variables for forecasting future GDP growth during both normal and exceptional economic circumstances. Our results address Finland, a small open economy in the euro area that was heavily influenced by the financial crisis. Our results confirm the usefulness of financial market information for forecasting future economic activity. The proper selection of financial market indicator variables is found to be related to the general health of the economy. During steady growth periods, short term interest rates and past values of output growth play a dominant role in forecasting economic activity. In contrast, during economic turbulence, the importance of the traditional term spread and stock returns markedly increases. Our results also indicate that stock returns as a sole financial predictor of GDP growth perform rather poorly. However, combining stock returns with other financial indicators improves forecasting performance. In general, the evidence from this study is largely consistent with the previous literature, and the results are not necessarily specific to only small open economies. During the financial crisis and its aftermath, central banks were nearly out of conventional monetary policy tools at the bounds that were imposed by a zero interest rate policy. However, it was remarkable that the term spread continued to have predictive power despite being almost entirely determined by the long-term interest rates, which highlights the importance of the long-term rates in determining the level of the term spread. Moreover, stock markets continued to signal the expected effects of unconventional monetary policy on economic growth, even though the short-term interest rates were close to zero lower bound. If the Great Moderation is over and unconventional monetary tools are the New Normal in Western economies, we may expect that stock markets and especially the term spread will regain much of their earlier status in forecasting GDP growth. The main policy implication of this study is that different forecasting models and financial variables are needed, depending on the state of the economy. Due to the time-varying predictive content of the financial variables, forecasters must identify in real time when to switch forecasting models. For that reason, we propose the use of the inversion-recession signal, which makes it possible to use simple and practical regime-switching nonlinear models to forecast growth during different economic circumstances. However, the universality of this proposition remains to be resolved.