FBBABLLR1CBQ_US Commercial Banks: Assets - Bank Credit - Loans and Leases - Residential Real Estate (Bil, $, SA)

Transcription

1 Notes on new forecast variables November 2018 Loc Quach Moody s Analytics added 11 new U.S. variables to its global model in November. The variables pertain mostly to bank balance sheets and delinquency rates, but other ad hoc variables were added as well. The purpose of these additions was to enhance our forecast offerings and improve the global model product. The new forecasts leverage the existing Moody s Analytics forecasting suite. FBBABLLR1CBQ_US Commercial Banks: Assets - Bank Credit - Loans and Leases - Residential Real Estate (Bil, $, SA) Moody s Analytics has added a residential real estate bank assets forecast to its global model. The forecast for this variable draws upon the forecast for total real estate loans. This variable is included on the left-hand side of the forecast equation, and the residential percentage of total real estate assets is modeled as the differenced variable with a differenced log functional form. The explanatory variable is the ratio of house prices to commercial real estate prices. An increase in the ratio translates into a larger share of residential real estate assets as a proportion of total real estate assets. The equation fails a Breusch-Godfrey test for serial correlation, but Moody s Analytics permitted serial correlation in this instance because of the persistence of house prices. The equation demonstrated much lower RMSE in back-testing than an AR(2) model. FBBABLLROCCBQ_US Commercial Banks: Assets - Bank Credit - Loans and Leases - Commercial Real Estate (Bil, $, SA) Moody s Analytics has added a commercial real estate bank assets forecast to its global model. The forecast for this variable draws upon the forecasts for total real estate assets and residential real estate assets. The forecast is an identity; its value is equal to the difference between total and residential real estate assets. This identity exists in history, providing Moody s Analytics with justification to maintain the identity in our forecast. FBBLDPCBQ_US Commercial Banks: Liabilities - Deposits (Bil. $, SA) Moody s Analytics has added a commercial bank deposits forecast to its global model. The forecast for this variable leverages Moody s Analytics forecasts on bank deposits included in the M1 and M2 monetary aggregates. Moody s Analytics also utilizes a lagged dependent variable. We tested both a lagged dependent variable version of this equation and a univariate one that depended solely on monetary category components. The one that included a lagged dependent variable produced a better forecast, lower back-testing error, and intuitive shock properties. FBCFDELLCCQ_US Delinquency Rates: Top 100 Commercial banks - Credit Card Consumer Loans (%,SA) Moody s Analytics has added a credit card bank delinquency rate forecast to its global model. We relied heavily on the ABA credit card delinquency rate forecast as the primary explanatory variable for this concept. We modeled in level terms, which was appropriate because the dependent variable is stationary. We included a lagged dependent variable and an AR(1) term as well. We also tested versions of this specification without these two explanatory variables and with a differenced functional form.

2 However, the chosen specification demonstrated the best combination of shock properties, baseline forecast, and low error during equation back-testing. FBCFDELLIQ_US Delinquency Rates: Top 100 Commercial banks - C&I Loans (%,SA) Moody s Analytics has added a C&I bank loan delinquency rate forecast to its global model. We experimented with a number of specifications and transformations. We considered bank charge-off rates as the primary regressor, and while the specifications of this type resulted in good back-testing performance because of the tight correlation between delinquency and charge-off rates, we could not justify charge-offs as drivers of delinquencies, since in practice a loan becomes delinquent before it is charged off. We settled on using the unemployment rate as our primary cyclical driver. Delinquencies and defaults on all loans rise when the economy falls into recession, and the unemployment rate is a great barometer of the business cycle. We also included lending standards with a lag and moving average to capture the phenomenon of laxer underwriting standards leading to a deterioration in loan performance over time. We also added an AR(1) term to eliminate serial correlation in the equation s error terms. The equation was modeled in differenced functional form. FBCFDELLRCQ_US Delinquency Rates: Top 100 Commercial Banks - Commercial Real Estate Loans (%, SA) Moody s Analytics has added a CRE bank loan delinquency rate forecast to its global model. We wanted the structure of this equation to mimic that of the C&I loan delinquency rate forecast. Therefore, we featured the unemployment rate as our primary cyclical driver and loan-specific lending standards. However, the variables historical fluctuations are far more extreme than for C&I loans. As a result, the estimated elasticities using the full range of historical data were extremely high. This rendered the equation extremely sensitive. Slight fluctuations in the economy were capable of pushing the delinquency rate either skyward or below zero. To combat this challenge, Moody s Analytics estimated in a level functional form with a constant term, a lagged dependent variable, and an AR(1). This dampened the elasticities of the cyclical variables. We also instituted a floor for the variable at 0.1. FBCFDELLRRQ_US Delinquency Rates: Top 100 Commercial Banks - Residential Real Estate Loans (%, SA) Moody s Analytics has added a residential real estate bank delinquency rate forecast to its global model. This equation is far more similar to the credit card delinquency rate equation than the C&I and CRE forecast equations. This is because Moody s Analytics could lean on its forecast for MBA loans past due. We experimented with level and differenced functional form, and with and without a lagged dependent variable. The specification that had the best combination of back-testing error, baseline forecast and shock properties was the one that used a differenced functional form with a one-period lagged dependent variable. FHVACHQ_US Homeowner Vacancy Rate (%, SA) Moody s Analytics has added a homeownership vacancy rate forecast to its global model. This was a difficult variable to model, as few variables are highly correlated with it. Before modeling, Moody s Analytics ran a unit root test to determine whether the variable needed to be transformed. The ERS test failed to reject the null hypothesis of a unit root, so Moody s Analytics modeled this variable in differenced log functional form. Moody s Analytics used the rental vacancy rate as a regressor in this

3 forecast equation. This is because we believe a priori that the two should move in tandem, since they are both measures of housing vacancy. We also added a lagged dependent variable. We experimented with an AR(1) term, but it was significantly disturbing the co-movement of the homeowner and rental vacancy rate forecasts. The arrived-upon specification demonstrates no serial correlation, beats the AR(2) benchmark in back-testing, produces a forecast that is in sync with the rental vacancy rate, and has good shock properties. FSP500EQ_US S&P 500 Composite: Price Index - End of period, (Index =10, NSA) Moody s Analytics has added an S&P 500 end-of-period forecast to its global model. The dependent variable had to be influenced by our forecast for the S&P 500, since the only thing separating them was that one was a quarterly average whereas the other was the value of the stock market benchmark at the end of a quarterly period. We experimented with lags, moving averages and different transformation, but it was important to us that the coefficient of the S&P 500 regressor was near 1, implying a 1-for-1 movement in the broader index and the dependent variable. This was accomplished by a simple univariate regression. We did not add a constant term because we didn t want to introduce a time trend in the long-term forecast that would break the co-movement of these two variables. The arrived-upon specification beats the AR(2) benchmark in unconditional back-testing and trounces it in conditional back-testing. In scenarios, it moves in line with the S&P 500 index. FTWDMJRQ_US Weighted Average Exchange Value of U.S. Dollar: Major Currencies Index Nominal Moody s Analytics has added a weighted average exchange value of the U.S. dollar major currencies index forecast to its global model. This variable differs from the nominal broad trade-weighted dollar index only in that it considers just seven currencies rather than a broader basket. The currencies it contains are those issued by the countries of Japan, Canada, Switzerland, Sweden, the euro zone, the United Kingdom, and Australia. Moody s Analytics attempted to put all seven bilateral exchange rates in this regression, but the euro zone, U.K. and Australian currencies had the wrong signs. Therefore, this architecture had to be abandoned. We instead use the nominal broad trade-weighted dollar index. We transformed the regressor and the dependent variable by taking a differenced log since the dependent variable failed an ERS unit root test. The specification outperformed the AR(2) benchmark in backtesting and moves alongside the nominal broad trade-weighted dollar in scenarios. FCPIUEHC_US CPI: Urban Consumer - Owners' equivalent rent of residences, (Index Dec1982=100, SA) Moody s Analytics has added a CPI of owners equivalent rent forecast to its global model. We modeled in differenced log functional form to avoid a spurious regression. We tested different housing-related CPI components and even measures of population. However, the most effective regressor was the shelter CPI for urban consumers. This variable explains the vast majority of the movement in the dependent variable. The equation does not display serial correlation. We did not add a constant term so as not to add an unwanted time trend. The specification performs significantly better than the AR(2) benchmark in both conditional and unconditional back-testing.

4 New equation specifications Dependent Variable: DLOG(FBBABLLR1CBQ_US/FBBABLLRCBQ_US) Date: 10/24/18 Time: 16:24 Sample (adjusted): 2004Q4 2018Q2 Included observations: 55 after adjustments Q_US(-3),6)) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat FBBABLLROCCBQ_US = FBBABLLRCBQ_US - FBBABLLR1CBQ_US Dependent Variable: DLOG(FBBLDPCBQ_US) Date: 10/25/18 Time: 15:22 Sample (adjusted): 1973Q3 2018Q2 Included observations: 180 after adjustments DLOG(FDDQ_US+FOCDQ_US+FSAVCQ_U S+FSTDCQ_US+FMMMFRQ_US) DLOG(FBBLDPCBQ_US(-1)) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat

5 Dependent Variable: FBCFDELLCCQ_US Method: ARMA Generalized Least Squares (Gauss-Newton) Date: 10/26/18 Time: 12:51 Sample: 1991Q2 2018Q2 Included observations: 109 Convergence achieved after 6 iterations Coefficient covariance computed using outer product of gradients d.f. adjustment for standard errors & covariance FABABC_US FBCFDELLCCQ_US(-1) AR(1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat Inverted AR Roots.56 Dependent Variable: D(FBCFDELLIQ_US) Method: ARMA Generalized Least Squares (Gauss-Newton) Date: 11/01/18 Time: 17:21 Sample: 1993Q1 2018Q2 Included observations: 102 Convergence achieved after 3 iterations Coefficient covariance computed using outer product of gradients d.f. adjustment for standard errors & covariance D(@MOVAV(FLBR_US,2)) AR(1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat Inverted AR Roots.32

6 Dependent Variable: FBCFDELLRCQ_I_US Method: ARMA Generalized Least Squares (Gauss-Newton) Date: 11/02/18 Time: 14:29 Sample: 1994Q1 2018Q2 Included observations: 98 Convergence achieved after 30 iterations Coefficient covariance computed using outer product of gradients d.f. adjustment for standard errors & covariance C ),4) FBCFDELLRCQ_I_US(-1) AR(1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) Inverted AR Roots.94

7 Dependent Variable: FBCFDELLRCQ_I_US Method: ARMA Generalized Least Squares (Gauss-Newton) Date: 11/02/18 Time: 14:29 Sample: 1994Q1 2018Q2 Included observations: 98 Convergence achieved after 30 iterations Coefficient covariance computed using outer product of gradients d.f. adjustment for standard errors & covariance C ),4) FBCFDELLRCQ_I_US(-1) AR(1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) Inverted AR Roots.94 Dependent Variable: D(FBCFDELLRRQ_US) Date: 10/29/18 Time: 15:04 Sample (adjusted): 1991Q3 2018Q2 Included observations: 108 after adjustments D(FMBAD_US) D(FBCFDELLRRQ_US(-1)) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat

8 Dependent Variable: DLOG(FCPIUEHC_US) Date: 10/26/18 Time: 08:51 Sample (adjusted): 1983Q2 2018Q2 Included observations: 141 after adjustments DLOG(FCPIUAH1_US) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat FGGDEBTGDP_US = FGTSOTQ_US / FGDP_US * 100 Dependent Variable: DLOG(FHVACHQ_US) Date: 11/01/18 Time: 20:20 Sample (adjusted): 1990Q1 2018Q2 Included observations: 114 after adjustments DLOG(@MOVAV(FHVACRQ_US,20)) DLOG(FHOWNRQ_US(-1)) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat

9 Dependent Variable: DLOG(FSP500EQ_US) Date: 10/25/18 Time: 12:55 Sample (adjusted): 1960Q1 2018Q3 Included observations: 235 after adjustments DLOG(FSP500Q_US) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat Dependent Variable: DLOG(FTWDMJRQ_US) Date: 10/30/18 Time: 23:38 Sample (adjusted): 1973Q2 2018Q3 Included observations: 182 after adjustments DLOG(FTWDBRD_US) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter Durbin-Watson stat