In the next section, we will present an overview of the literature on financial variables as predictors of economic activity. 1997)4 evaluated the ability of the yield spread to predict real economic activity in 11 industrialized countries. Additionally, the predicted power of the return distribution can be leveraged using in-sample and out-of-sample forecasting techniques.
The strongest predictive power of proliferation was found in Canada, Germany, and the United States. On the other hand, the ability of yield spreads to predict real economic activity is weakest in Japan and Switzerland. Their measure of the slope of the yield curve is the difference between the two rates SPREADt = RL - RS.
In the final step of their study they looked more closely at the comparative value of the information in the yield curve. This is very poor predictive power when compared to the predictive power of the slope of the yield curve. Heon (2002)9 in their study reviewed the utility of yield differentials for forecasting real GDP growth in the future.
The nominal short-term interest rate dominated the slope of the yield curve in the forecast for GDP growth both in and out of the sample.
On the other hand, in SW model, the means used in forecasting are simply the static principal components of the variables in the panel. The data are monthly seasonally adjusted observations of the total share price index, nominal short-term interest rates, the consumer price index (CPI), the industrial production index, the house prices, the oil prices, the precious metal prices: gold and the money supply (M2). In the United States, housing starts (a real quantity measure) have some predictive content for inflation (Stock 1998; Stock and Watson 1999b).
According to the traditional Keynesian IS-LM view of the monetary transmission mechanism, we know the following. In addition to the shape of the loss function, the forecast horizon h is of crucial importance. If U=1, the predictive performance of the model is as poor as it could possibly be.
The proportion of variance Us shows the ability of the model to reproduce the degree of variability of the variable of interest. Second, for each of the variables, we test for Granger causality between the specific variable and economic activity (measured by industrial production) and select the lag order of the VAR using the Akaike Information Criterion (AIC) and Final Predictor Error (FPE). In the first case, regardless of which of the above-mentioned variables we examine, we find that the probability is higher than 10%, which supports the null hypothesis.
With the notion of systematic error we refer to a high deviation between the mean values of the forecast and the actual series. Next, examining the marginal predictive information of housing, oil, and gold prices in the baseline model, we arrive at the results in Table 13, which are similar to the previous one for bias. However, for h = 48 months it appears to decrease, indicating an improvement in predictive ability over the long horizon.
To examine the marginal forecast information of the term distribution in the base model, we create Table 21. As for the forecast bias of the q=70%, it appears more erratic than for the lower window. They are more or less the same with the previous model, which supports the high predictive ability of the term distribution in the base model.
It shows the forecasting accuracy of the underlying model (stock prices and short-term interest rates), demonstrating that the Theil coefficient decreases with increasing horizon, although the RMSFE shows an insignificant increase. The most common way to maintain inflation is to raise short-term interest rates.
Table of Data) (Table of Data)
Unit Root tests) (Unit Root tests)
SIt, TSt, SRt are short-term interest rates, short-term spreads and stock returns, respectively. SIt, TSt, SRt, MSt and CPIt, represent short-term interest rates, short-term spread, stock returns, money supply and CPI: All urban consumers (all items) respectively. SIt, SRt, HPt, OPt, and GDt represent short-term interest rates, stock returns, housing, spot oil and gold prices, and Panel B, respectively.
SIt, SRt, HPt, OPt, GDt and TSt stand for short-term interest rates, stock returns, housing, spot oil, gold prices and futures spread respectively. OPt, GDt, MSt and VPIt stand for short-term interest rates, stock returns, housing, spot oil and gold prices, money supply and CPI: All urban consumers (all items) respectively and Panel B. TSt, SIt, SRt, HPt , OPt, BBT, MSt and VPIt stand for term spread, short-term interest rates, stock returns, housing, spot oil and gold prices, money supply and CPI: All urban consumers (all items) respectively.
CPIt and TSt stand for short-term interest rates, stock returns, house prices, spot oil prices, precious metal: gold, money supply and CPI: All urban consumers (all goods) respectively. SIt, SRt, HPt, OPt, GDt respectively stand for short-term nominal interest rates, stock returns, house prices, spot oil prices and precious metal: gold. CPIt, MSt stand for short-term interest rates, stock returns, housing prices, spot oil prices, precious metal: gold, money supply and CPI: All urban consumers (all goods) respectively.
SIt, SRt, HPt, OPt, GDt, VPIt model is estimated with short-term interest rates, stock returns, housing, oil and gold prices, consumer price index (as a measure of inflation). SIt, SRt, HPt, OPt, GDt, MSt model are estimated with short-term interest rates, stock returns, housing, oil and gold prices and money supply (as a monetary policy indicator) respectively. SIt, SRt, HPt, VPIt, MSt model are respectively estimated with short-term interest rates, stock returns, house prices, consumer price index (as a measure of inflation) and money supply (as a monetary policy indicator).
The SIt, SRT, CPIt and MSt models are estimated using short-term interest rates, stock returns, oil and gold prices, consumer price index (as a measure of inflation) and money supply (as a monetary policy indicator) respectively. The SIt, SRT, CPIt and MSt models are estimated using short-term interest rates, stock returns, the consumer price index (as a measure of inflation) and the money supply (as a monetary policy indicator), respectively. The SIt, CPIt and MSt models are estimated using short-term interest rates, the consumer price index (as a measure of inflation) and the money supply (as an indicator of monetary policy), respectively.
SIt, SRt, and TSt are for short-term interest rates, stock returns, and the short-term spread, respectively. Short-term interest rates SIt, SRt, HPt, OPt, GDt, CPIt, MSt and TSt, stock returns, house prices, spot oil prices, precious metal: gold, consumer price index, money supply and short-term spreads respectively.
