Financial Information and Macroeconomic Forecasts

Contributor Notes

Authors’ E-Mail Addresses: YChen2@imf.org, RRanciere@imf.org

We study the forecasting power of financial variables for macroeconomic variables for 62 countries between 1980 and 2013. We find that financial variables such as credit growth, stock prices and house prices have considerable predictive power for macroeconomic variables at one to four quarters horizons. A forecasting model with financial variables outperforms the World Economic Outlook (WEO) forecasts in up to 85 percent of our sample countries at the four quarters horizon. We also find that cross-country panel models produce more accurate out-of-sample forecasts than individual country models.

Abstract

We study the forecasting power of financial variables for macroeconomic variables for 62 countries between 1980 and 2013. We find that financial variables such as credit growth, stock prices and house prices have considerable predictive power for macroeconomic variables at one to four quarters horizons. A forecasting model with financial variables outperforms the World Economic Outlook (WEO) forecasts in up to 85 percent of our sample countries at the four quarters horizon. We also find that cross-country panel models produce more accurate out-of-sample forecasts than individual country models.

I. Introduction

The crisis of 2007 to 2009—originated from the U.S. subprime mortgage market—caused widespread disruptions in the financial market including contraction in credit supplies and declines in asset prices. The crisis was followed by the global economic downturn of the Great Recession that spread to virtually all advanced economies and many emerging markets. These developments have contributed to an intense debate on macro-financial linkages.

In this paper, we study macro-financial linkages in the context of macroeconomic forecasts. Building our analysis on a large literature on macroeconomic forecasts, we examine the forecasting power of financial variables for macroeconomic variables for 62 countries between 1980 and 2013. We show that incorporating financial variables such as credit growth, stock prices, house prices, and bond yields in an otherwise simple model significantly improves the accuracy of macroeconomic forecasts.

The rational of using financial variables to forecast macroeconomic variables is threefold. First, in the presence of financial market imperfections when the Modigliani-Miller theorem does not hold, changes in credit conditions are likely to result in changes in future macroeconomic conditions. In addition, changes in asset prices, by affecting the wealth of firms and households, affect their investment and consumption decisions. Second, financial variables, by their forward-looking nature, incorporate information about the future of the economy that are not yet reflected in current macroeconomic outcomes. Finally, in countries where macroeconomic variables are collected with considerable time lags, contemporaneous financial variables can help nowcasting these variables.

Our methodology is chosen to be deliberately simple to facilitate easy replication. The model can be estimated either country-by-country or in a cross-country panel. The simplicity of the model makes it applicable to countries with very limited financial data. In its simplest specification, the model uses only one financial variable that is available for most countries: private sector credit growth.2 For countries with available data, the model can be augmented to include additional financial variables such as stock prices, house prices, corporate and sovereign bond yields, and deposit and borrowing rates.

We find that credit growth is significantly associated with GDP growth. The effect is large in the baseline model with only one financial variable: credit growth. In nowcasting, a one standard deviation increase in credit growth (i.e. a 24 percentage point increase in annualized rate) is associated with a 1.79 percentage point increase in annualized GDP growth, which corresponds to about 1/3 of one standard deviation of the annualized GDP growth in our sample. A one standard deviation increase in credit growth is associated with 1.15 percentage point increase in GDP growth at one quarter ahead horizon, and a 0.46 percentage point increase at four quarters ahead horizon. In the augmented model with other financial variables, the effect of credit growth remains significant in nowcasting and at one quarter horizon.

Credit growth is also significantly associated with consumption growth, investment growth, and inflation. The effect is strongest for investment growth. A one standard deviation increase in credit growth is associated with a 6.9 percentage point increase in investment growth in nowcasting, a 4.2 percentage point increase at one-quarter horizon, and a 2.0 percentage point increase at four-quarter horizon.

Conditional on credit growth, stock prices and house prices also predict GDP growth, consumption growth, and investment growth in most of the specifications. Conditional on these variables, deposit and lending rates have little predictive power for macro variables with one exception. The lending rate is strongly positively associated with future investment growth. Corporate and sovereign bond yields also have some predictive power for macro variables. But their effects are not robust when other financial variables are included.

Our findings are robust across different country groups (i.e. advanced economies, emerging markets and low-income countries).3 In particular, credit growth is significantly associated with GDP growth in all country groups. The coefficients tend to be larger among emerging market and low-income countries than among advanced economies. The only exception is that when sovereign and corporate bond yields are included, credit growth loses statistical significance. Stock prices and house prices also retain predictive power for advanced economies and emerging markets.4

Given that similar empirical relationships hold across countries, a natural question to ask is whether cross country information helps to predict individual country’s macro outcomes. To answer this question, we compare the performance of our model based on individual country regressions to those based on panel regressions. We find that the out-of-sample forecasts based on panel regressions always outperform those based on individual country regressions even though individual country regressions always have better in-sample fit. Our result suggests that cross country information helps to improve individual country forecast.

To assess the forecasting performance of our models, we compare the out-of-sample forecasting errors of our models with those from a benchmark model in which macroeconomic variables are forecasted based on past macroeconomic outcomes. We also compare the forecasting errors of our model with those implied by publicly available forecasts from the IMF’s World Economic Outlook (WEO). The WEO publishes biannually IMF’s projections on national accounts and other indicators for member countries. We find that our financial models have more accurate forecasts for GDP growth than WEO predictions. A simple financial model with credit growth has smaller forecasting errors than WEO forecasts for 69 percent of the countries in our sample. An extended financial model with credit growth, stock prices, and house prices has smaller forecasting errors than WEO forecasts for 85 percent of the countries.

The paper draws on several strands of the literature. First, it builds on a recent literature showing that credit conditions are driving part of business cycle fluctuations (Bernanke, Gertler, and Gilchrist, 1996; Gilchrist and Zakrajsek, 2012; Philippon, 2009). Second, our results are consistent with prior empirical evidence that financial variables are leading indicators of business cycles, as shown by Leamer (2007) for house price, and by Claessens, Kose, and Torrones (2009) and Estrella and Mishkin (1998) for other financial variables. From a methodological perspective, this paper relates to the discussion of the pros and cons of using pooled international data in forecast models (Garcia-Ferrer, et al., 1987; Hoogstrate, Palm, and Pfann. 2000)

Prior empirical research on macro financial forecasts mostly explores the procyclical nature of financial variables in a small set of advanced economies. In particular, most studies focus on forecasting macroeconomic activities using asset prices, broadly defined to include interest rates, interest spreads, returns, and the value of financial and tangible assets such as bonds, stocks, and housing (for a literature review, see Stock and Watson, 2003). Asset prices data has the advantage of being observed in real time with small measurement errors. However, it is only available for a limited set of countries. Our paper contributes to this literature by broadening our understanding of these relationships with a model and data that is applicable to a large number of countries.

The rest of the paper is organized as follows. Section 2 introduces our forecasting model and data. Section 3 present our results. Section 4 concludes.

II. Empirical Model and Data

To assess the predictive ability of financial variables for macroeconomic activity, we estimate the following forecasting model:

hYc,t+h=α+Σi=1pβiYc,ti+γXct+μc+ɛc,t+h,(1)

where Yct is a quarterly macroeconomic indicator (to be specified below) for country c in quarter t. hYc,t+hz/(h+1)ln(Yt+h/Yt1) measures the annualized growth rate, where h ≥ 0 is the forecast horizon. z = 400 is a scaling constant. Xct is a vector of predictors. μc is country fixed effect. We include the lagged value ∇Yc,t-i as predictors because the left-hand-side variable is likely to be serially correlated. Thus the coefficient γ captures the marginal information content of predictors Xct beyond that contained in ∇Yc,t-i. We use Akaike Information Criterion (AIC) to determine the lag length p in each specification. We use Newey-West standard errors to correct for the autocorrelation and heteroskedasticity of the moving-average error term εc,t+h resulting from overlapping observations.5 The timing adopted by this framework allows for “nowcasting” (i.e. h = 0 ), in which contemporaneous financial variables are used to forecast macroeconomic activities. This is most useful when macroeconomic indicators are observed with lags but contemporaneous financial variables are readily available.

We consider the following key measures of macroeconomic activities on the left-hand side: GDP growth, private consumption growth, private investment growth, and consumer price indexes (CPI) inflation. We use a vector of financial variables as predictors including private sector credit growth, stock prices, and house prices, the bank prime loan rate, and the deposit rate. For a subsample of advanced economies for which data is available, we also estimate an extended model with additional data on sovereign bond yields and corporate bond yields. The main sources for house prices are the OECD and the Bank of International Settlements (BIS). The main source for stock prices and bond yields is Bloomberg. We use two policy controls: government consumption as a proxy for fiscal policy and short term interest rate as a proxy for monetary policy. GDP, consumption, investment, and all financial variables are converted into real terms using country-specific GDP deflator. For all variables that are not seasonally adjusted in the raw data, we perform seasonal adjustment using the X-12-ARIMA method proposed by the U.S. Census Bureau. Table A1 in the Appendix summarizes data sources for each of the variables.

There are considerable variations in the availability of quarterly data over 1980-2013 even in the sample of advanced economies. While some countries such as France, US, or Japan have a quasi-exhaustive data coverage for the baseline model, others such as Sweden, Germany or Netherlands exhibit significant data gaps. Using data for corporate bond yields significantly reduces the sample coverage for advanced economies. Moreover, the number of quarterly observations is larger for advanced than for emerging countries. While there are, on average, 87 quarterly observations per country for advanced economies, there are, on average, only 41 quarterly observations per country for emerging economies. We present summary statistics in Table 1 We present the list of countries included in our regressions in Appendix Table A2.

Table 1

Summary Statistics

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Notes: AE refers to advanced economies, EM refers to emerging markets, and LIC refers to low-income countries. All variables are winsorized at 2 percent.

III. Empirical Results

Because the data coverage is very unbalanced across countries, we estimate the model for the sample of all countries and for four different subsamples of countries: two for advanced economies (AE), one for emerging markets (EM), and one for low-income countries (LIC). For advanced economies, we first present results for all countries using the baseline specification. We then present results for the extended model with sovereign bond yields and corporate bond yields for the subsample of countries for which data are available. Finally, we compare forecasting errors of the panel models with those of the individual country models, and with those implied by IMF’s WEO forecasts, on an in-sample and out-of-sample basis.

A. Panel Estimation Results

Table 2 presents the panel regressions on the sample of all countries with available data. Panel A and B present the results for GDP growth and consumption growth, while Panel C and D present the results for investment and inflation. In each panel, columns 1 to 3 presents nowcasting results, columns 4 to 6 forecasting results at one quarter ahead horizon, and columns 7 to 9 forecasting results at four quarters ahead horizon.

Table 2

Panel Regression Results: All Countries

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We present three specifications: a baseline specification with credit growth and the policy controls (government consumption and policy rate), and two augmented specifications: one that adds stock prices and house prices, and another one that further adds deposit and lending rates. Compared to the baseline specification, using the most comprehensive set of variables leads to a reduction of the number of observations by about one half. In order to be able to compare the relative importance of predictor variables, we report standardized coefficients for all financial variables and policy controls.

Credit growth is significantly associated with GDP growth in most specifications. The effect is large in our baseline specifications. In nowcasting, a one standard deviation increase in credit growth (i.e. a 24 percentage point increase in annualized rate) is associated with a 1.79 percentage point increase in annualized GDP growth, which corresponds to about 1/3 of one standard deviation of the annualized GDP growth in our sample. A one standard deviation increase in credit growth is associated with 1.15 percentage point increase in GDP growth at one quarter ahead horizon, and a 0.46 percentage point increase at four quarters ahead horizon. In the augmented specifications, the effect remains significant for nowcasting and at one quarter ahead horizon with a slightly smaller effect (0.87 percentage point and 0.96 percentage points for nowcasting; and 0.65 percentage point and 0.43 percentage point at the one quarter ahead horizon). But credit growth is not statistically significant at four quarters ahead horizon. Credit growth similarly has forecasting power for consumption growth and investment growth. However, the effect of credit growth on investment growth is not significant in the augmented specifications. Credit growth is negatively associated with inflation growth but the effect tends to vanish in augmented specifications and at four quarters ahead horizon.

Stock prices and house prices are positively associated with GDP growth in all specifications. The effects are significant at one percent confidence level and tend to be large. At one quarter ahead horizon, a one standard deviation increase in either the house price index or the stock price index lead to an increase in GDP growth of 0.95 percentage point. The effect at four quarters horizon remains strong: 0.73 percentage point for the stock price index and 0.85 percentage point for house prices index. The effect for nowcasting is slightly smaller for the stock price index (0.59 percentage point) but larger for the house prices index (1.01 percentage points). Stock prices and house prices also predict consumption growth. An increase in house prices has a stronger impact on consumption growth than an increase in stock prices. This result is not surprising given than housing wealth represents 50 percent or more of total household wealth (ECB 2004, Iacoviello 2012). Stock and house prices also have a large and significant impact on investment growth in most specification. Neither stock prices nor house prices significantly predict inflation.

Conditional on other financial variables and policy controls, the deposit and lending rates do not significantly predict GDP growth. An increase in the deposit rate does predict lower consumption growth and higher investment growth in the future.

Table 3 presents similar results for advanced economies which broadly confirms the results obtained for the full sample. The same is true for the sample of advanced economies with information on sovereign bond yields (Table 4), but the impact of credit growth is weaker in that sample. A one standard deviation increase in the corporate bond yield leads to a percentage reduction in GDP growth of 0.7 and 0.75 percentage points at one and fourth quarter horizon respectively in the baseline specification. The negative effect is smaller (0.48 percentage point) in the most comprehensive specification and at the four quarters ahead horizon. An increase in the corporate bond yield has an even stronger negative effect on investment growth but the results are not robust beyond the baseline specification. The results on corporate bond yields are in line with the findings of Gilchrist and Zakrajsek (2012) for the U.S. economy.

Table 3

Panel Regression Results: Advanced Economies

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Table 4

Panel Regression Results: Advanced Economies Subsample with Bond Yields

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Table 5 presents the results for emerging markets. The effect of credit growth on GDP growth is much stronger than what it is for advanced economies but its forecasting ability is only significant at the one quarter ahead horizon. Another notable difference is that house price growth is negatively correlated with investment growth, and that an increase in the lending rate has a strong negative impact on investment. The first fact is consistent with a house price boom crowding out investment, and the second with the presence of severe borrowing constraints tying up investment to lending conditions in emerging markets.

Table 5

Panel Regression Results: Emerging Markets

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