Multivariate Forecasts

Today

• Multivariate forecasts
• Multi-period forecasts
• Vector Autoregression (VAR) methods

Multivariate Forecasts

• Previous problems we have seen in class involve forecasting a single number \(y_{T+h}\)
• Often need to forecast more than a single object
  • Need to know multiple aspects of future situationto make policy or decisions
    • Ideal monetery policy depends on future state of both inflation and unemployment
  • May need to make multiple related decisions
    • Business may care about performance of each of its many establishments
• Even when forecasting a single target, can help to use multiple variables
  • May be used directly to improve predictions
  • May be used indirectly to construct a better univariate forecast from a multivariate one

Multivariate Forecasting Setup

• Similar to univariate case, except we care about \(m\) different outcomes
• Formally, data \(\mathbf{y}_t=(y_{1t},y_{2t},\ldots,y_{mt})^{\prime}\) is an m-dimensional vector in \(\mathbf{Y}=\mathbb{R}^m\)
• Data available at time t is to build a forecast is \(\mathcal{Z}_t=\{\mathcal{Y}_t,\mathcal{X}_t\}\in\underset{t}{\times}\mathbf{Z}=\underset{t}{\times}(\mathbf{Y}\times\mathbf{X})\)
  • \(\mathcal{Y}_t=\{\mathbf{y}_s\}_{s=1}^{t}\) are past observations of the series to be forecast
  • \(\mathcal{X}_t=\{\mathbf{x}_s\}_{s=1}^{t}\) are past observations of other series which are not the target of forecast, but may be used for forecasting
• Produce a multivariate forecast \(\widehat{\mathbf{y}}_{T+h}=f(\mathcal{Z}_t)=(f_1(\mathcal{Z}_t),f_2(\mathcal{Z}_t),\ldots,f_m(\mathcal{Z}_t))^{\prime}\) using a forecasting rule \(f: \underset{t}{\otimes}\mathbf{Z}\to\mathbf{Y}\)
• As always, quality of forecast can be expressed using a loss function \(\ell():\ \mathbf{Y}\times\mathbf{Y}\to\mathbb{R}\)
• In statistical setting, \(\{z_t\}_{t=1}^{T+h}\) taken to come from a probability distribution \(p\in\mathcal{P}\)
  • Goal is to choose rule with small risk \(E_p[\ell(\mathbf{y}_{T+h},f(\mathcal{Z}_{T}))]\)
  • Empirical Risk Minimizer \(\widehat{f}^{ERM}=\underset{f\in\mathbf{F}}{\arg\min}\frac{1}{T-h}\sum_{t=1}^{T}\ell(\mathbf{y}_{t+h},f(\mathcal{Z}_{t}))\) remains a fine choice

Multivariate Forecasting Goals

• Loss function \(\ell(\mathbf{y}_{T+h},\widehat{\mathbf{y}}_{T+h})\) should express preferences over different degrees of accuracy of different parts of the forecast
• Simplest case: Additive loss
  • For univariate loss functions \(\{\ell_i(.,.)\}_{i=1}^{m}\) and weights \(\{w_i\}_{i=1}^{m}\)
  • \(\ell(\mathbf{y}_{T+h},\widehat{\mathbf{y}}_{T+h})=\sum_{i=1}^{m}w_{i}\ell_{i}(y_{i,T+h},f_{i}(\mathcal{Z}_t))\)
  • Weights reflect importance of each problem, and \(\ell_i\) describes cost of errors in each
• When problem depends on joint performance across tasks, loss function can express this directly
  • Example: weighted square loss \(\ell(\mathbf{y},\widehat{\mathbf{y}})=\sum_{i,j=1}^m w_{ij}(y_{i}-\widehat{y}_{i})(y_{j}-\widehat{y}_{j})\)
  • Weights \(w_{ij}\), restricted so \(w_{ij}=w_{ji}\), reflect attention to interactions between problems
    • Additional restrictions needed to make sure loss always positive and increasing
  • If \(w_{ij}=0\) for \(i\neq j\), get back to additive loss

Forecasting Rules for Multivariate Forecasts

• Simplest way to build a class of multivariate forecast rules is to combine univariate classes
  • \(\mathcal{F}=\times_{i=1}^{m}\mathbf{F}_i\), where \(\mathcal{F}_i=\{f:\mathbf{Z}\to\mathbb{R}\}\) is a univariate class like the linear classes from before, is called separable
• When loss is additive AND rule class \(\mathcal{F}\) separable, ERM reduces to solving all the problems separately
  • Weights don’t matter since problems do not interact
  • Often a reasonable choice when there is no direct relationship between the problems
• Even if problems separate, may make sense to use method combining information
  • Stein (1956) showed an example where even with additive loss and all components of \(\mathbf{y_t}\) independent, can do better than separable rules
  • Shrinkage: share info across forecasts to spread error across different components
• May use rule sets \(\mathcal{F}\) where, e.g., one parameter affects forecast rule for multiple variables
  • Common in model-based macroeconomics, where theory can help specify rules
    • Called “cross-equation restrictions”
  • In cases where multiple series directly linked by accounting equations, can help to impose them
    • Called “hierarchical forecasts”
  • Example: restrict coefficients \(\beta_1,\beta_2,\ldots,\beta_{m}\) in set of linear forecasts so that total size bounded
    • E.g. \(\sum_{i=1}^m|\beta_i|\leq B\) or \(\sum_{i=1}^m(\beta_i)^2\leq B\)

Vector Autoregression

• Most common (explicitly) multivariate forecasting method by far
  • Popularized in economics by Sims (1980)
• Combines \(m\) linear regression classes based on \(p\) lags of all \(m\) variables
  • Called a Vector Autoregression of Order \(p\) or VAR(p)
• Forecast of \(y_{it}\) depends linearly on \(p\) lags of itself and of the other \(m-1\) variables \[\mathcal{F}=\{\forall i=1\ldots m,\ f_{i}(\mathcal{Y}_{t})=\beta_{i,0}+\sum_{k=1}^{m}\sum_{j=1}^{p}\beta_{i,jk}y_{k,t-j}, \beta\in\mathbb{R}^{m\times p(m+1)}\}\]
• Distinct from running \(m\) separate AR(p) models because other variables enter in
  • All variables enter in same way in all equations, so simple to specify
• With (additive) square loss for each variable, ERM estimation can be done by running Ordinary Least Squares Regression for each outcome separately
• Also commonly estimated with weighted square loss: Generalized Least Squares

Implementation and Example: GDP Forecasting Revisited

• Estimation by equation-by-equation OLS implemented as command VAR in library vars
• Input is a ts object containing \(m\) time series and choice of order \(p\)
  • Options to include linear trend (type=trend) and seasonal dummies at frequency f (season=f) in each equation

#Libraries
library(fpp2) #Forecasting and Plotting tools
library(vars) #Vector Autoregressions
library(fredr) #Access FRED Data
library(knitr) #Use knitr to make tables
library(kableExtra) #Extra options for tables

##Obtain and transform NIPA Data (cf Lecture 08)

fredr_set_key("8782f247febb41f291821950cf9118b6") #Key I obtained for this class

#US GDP components 
GDP<-fredr(series_id = "GDP",
           observation_start = as.Date("1947-01-01"),
           observation_end=as.Date("2019-02-12")) #Gross Domestic Product

#US GDP components from NIPA tables (cf http://www.bea.gov/national/pdf/nipaguid.pdf)
PCEC<-fredr(series_id = "PCEC",
            observation_start = as.Date("1947-01-01"),
            observation_end=as.Date("2019-02-12")) #Personal consumption expenditures
FPI<-fredr(series_id = "FPI",
           observation_start = as.Date("1947-01-01"),
           observation_end=as.Date("2019-02-12")) #Fixed Private Investment
CBI<-fredr(series_id = "CBI",
           observation_start = as.Date("1947-01-01"),
           observation_end=as.Date("2019-02-12")) #Change in Private Inventories
NETEXP<-fredr(series_id = "NETEXP",
              observation_start = as.Date("1947-01-01"),
              observation_end=as.Date("2019-02-12")) #Net Exports of Goods and Services
GCE<-fredr(series_id = "GCE",
           observation_start = as.Date("1947-01-01"),
           observation_end=as.Date("2019-02-12")) #Government Consumption Expenditures and Gross Investment

#Format the series as quarterly time series objects, starting at the first date
gdp<-ts(GDP$value,frequency = 4,start=c(1947,1),names="Gross Domestic Product") 
pcec<-ts(PCEC$value,frequency = 4,start=c(1947,1),names="Consumption")
fpi<-ts(FPI$value,frequency = 4,start=c(1947,1),names="Fixed Investment")
cbi<-ts(CBI$value,frequency = 4,start=c(1947,1),names="Inventory Growth")
invest<-fpi+cbi #Private Investment
netexp<-ts(NETEXP$value,frequency = 4,start=c(1947,1),names="Net Exports")
gce<-ts(GCE$value,frequency = 4,start=c(1947,1),names="Government Spending")

#Convert to log differences to ensure stationarity and collect in frame
NIPAdata<-ts(data.frame(diff(log(gdp)),diff(log(pcec)),diff(log(invest)),diff(log(gce))),frequency = 4,start=c(1947,1),end=c(2018,3))

• Suppose we want to forecast GDP growth and subcomponents Consumption+Investment+Government jointly
• Estimate a VAR(1) model using log differences of NIPA nominal measurements

NIPAVAR<-VAR(NIPAdata,p=1) #VAR(1) in Y=log.diff.gdp, C=log.diff.pcec, I=log.diff.invest, G=log.diff.gce
NIPAfcst<-forecast(NIPAVAR) #Produce forecasts of all series

Estimated VAR Coefficients

	GDP	Consumption	Investment	Government
Lagged Change log Y	0.1202982	0.4384795	-1.9641401	0.1247220
Lagged Change log C	0.4084516	-0.1129436	3.5886042	0.0105691
Lagged Change log I	0.0356386	-0.0036534	0.3597251	0.0438564
Lagged Change log G	0.0095231	-0.0499654	-0.3891585	0.5990209
Constant	0.0065023	0.0114936	-0.0093123	0.0035254

Forecast Results

autoplot(NIPAfcst)+
  labs(title = "VAR(1) Forecasts of GDP and and NIPA Component Growth")

Multi-period Forecasts

• Existing setup and procedures have described how to make a forecast at a single period \(T+h\)
• For some decision problems, may want to forecast over multiple future periods \(1,2,3,\ldots,h\)
  • When deciding whether to invest in long-lived durable business asset, may need to know how sales will change over lifetime of the asset
• Forecasting \((y_{T+1},y_{T+2},\ldots,y_{T+h})\) is a special case of a multivariate forecasting problem
• Simplest approach: forecast separately at each period up to horizon h
  • Apply ERM (or other rule) at each distance ahead in time
  • In case of OLS predictions, goes by name of local projection approach
• Local projections compute different coefficients for each period
  • Reflects different relationships between variables over time
  • Allows for flexibility especially if best predictor changes at different lengths
• Especially with many time periods, unrestricted approach has disadvantages
  • Does not exploit relationships across horizons
  • Uses many parameters or high model complexity, so is prone to overfitting
    • h times as many as 1 period
  • Can be computationally costly to run procedure \(h\) times

Alternate Approaches

• In principle, could use multivariate forecasting rule in \(h\) dimensions to fit these values
  • E.g., minimize joint empirical risk over all horizons
• In most cases, including commands in forecast library, use a different and faster method
• Recursive substitution is a simple way to turn a forecast rule at horizon h=1 into a forecast for any horizon
• Especially common for autoregression forecasts, but can be applied in many cases
  
• Steps
  1. Compute a single forecast rule \(\widehat{f}(\mathcal{Y}_T)\) for horizon \(h=1\), eg by ERM
  2. Compute prediction \(\widehat{y}_{T+1}=\widehat{f}(\mathcal{Y}_T)\) and add forecast to \(\mathcal{Y}_T\) to produce “augmented” data \(\widehat{\mathcal{Y}}_{T+1}=(\mathcal{Y}_T,\widehat{y}_{T+1})\)
  3. Use same rule to produce forecast \(\widehat{y}_{T+2}=\widehat{f}(\widehat{\mathcal{Y}}_{T+1})\), and add forecast to \(\widehat{\mathcal{Y}}_{T+1}\) to produce “augmented” data \(\widehat{\mathcal{Y}}_{T+2}=(\widehat{\mathcal{Y}}_{T+1},\widehat{y}_{T+2})\)
  4. Continue as above until horizon \(h\) is reached

Recursive Substitution for Multi-period Forecasts

• Idea is that if \(\widehat{f}\) is a good forecasting rule and data distribution is stationary, it would continue to be a good forecasting rule next period, if the data to implement existed
  • This data isn’t available, but if forecasts are good, the forecast might be a reasonable substitute
• The change in this sequence of forecasts as a function of horizon \(h\) in response to a one unit change in \(y_t\) is called the impulse response function of the rule
  • Useful for understanding how predictions are affected by data realizations
• Because method uses only a single fit, it is fast and does not suffer from overfitting problem to same degree as disconnected approach
  • Provides sensible way to “share” parameters across multiple forecasts
  • Doesn’t explicitly optimize multiperiod objective, but can work well so long as initial forecast rule is high quality

Example: Extending an AR(2) Forecast

• Fit \(\widehat{f}(\mathcal{Y}_{T})\) by OLS by minimizing square loss over AR(2) rules \[\widehat{\beta}=\underset{\beta_0,\beta_1,\beta_2}{\arg\min}\frac{1}{T-2}\sum_{t=2}^{T-1}(y_{t+1}-\beta_0-\beta_1y_{t}-\beta_2y_{t-1})^2\]
• Predict \(\widehat{y}_{T+1}=\widehat{\beta}_0+\widehat{\beta}_1y_{T}+\widehat{\beta}_2y_{T-1}\)
• Predict \(\widehat{y}_{T+2}=\widehat{\beta}_0+\widehat{\beta}_1\widehat{y}_{T+1}+\widehat{\beta}_2y_{T}\) \(=\widehat{\beta}_0+\widehat{\beta}_1(\widehat{\beta}_0+\widehat{\beta}_1y_{T}+\widehat{\beta}_2y_{T-1})+\widehat{\beta}_2y_{T}\) \[=(\widehat{\beta}_0+\widehat{\beta}_1\widehat{\beta}_0)+(\widehat{\beta}_1^2+\widehat{\beta}_2)y_{T}+\widehat{\beta}_1\widehat{\beta}_2y_{T-1}\]

• Predict \(\widehat{y}_{T+3}=\widehat{\beta}_0+\widehat{\beta}_1\widehat{y}_{T+2}+\widehat{\beta}_2\widehat{y}_{T+1}\) \[=\widehat{\beta}_0+\widehat{\beta}_1((\widehat{\beta}_0+\widehat{\beta}_1\widehat{\beta}_0)+(\widehat{\beta}_1^2+\widehat{\beta}_2)y_{T}+\widehat{\beta}_1\widehat{\beta}_2y_{T-1})+\widehat{\beta}_2(\widehat{\beta}_0+\widehat{\beta}_1y_{T}+\widehat{\beta}_2y_{T-1})\] \[=(\widehat{\beta}_0+\widehat{\beta}_1\widehat{\beta}_0+\widehat{\beta}_1^2\widehat{\beta}_0+\widehat{\beta}_2\widehat{\beta}_0)+(\widehat{\beta}_1^3+2\widehat{\beta}_1\widehat{\beta}_2)y_{T}+(\widehat{\beta}_1^2\widehat{\beta}_2+\widehat{\beta}_2^2)y_{T-1}\]

• At every horizon the forecast rule is an AR(2), with different coefficients
  • Acts like local projection rule, but sequence of coefficients fixed by h=1 estimate

More Applications of Recursive Substitution

• Exact same procedure can be performed for multivariate forecasts, like VARs
  • VAR command in R (and all other VAR software I know of) produces multi-period forecasts this way
• Recursive substitution can also be used to produce univariate forecasts when data \(\mathcal{Z}_t=(\mathcal{Y}_t,\mathcal{X}_t)\) used to produce forecast contains variables \(\mathcal{X}_t\) other than predictor of interest
  • Like regression forecasts from previous lecture
  • Want to predict GDP, but use multiple components to do so
• Suppose we have a forecast rule \(\widehat{y}_{t+1}=\widehat{f}(\mathcal{Y}_t,\mathcal{X}_t)\)
  • Cannot directly perform resursive update to obtain \(\widehat{y}_{t+2}\) because \(x_{t+1}\) not known
  • Solution: forecast \(x_{t+1}\)!
• If we extend a univariate forecast \(\widehat{y}_{t+1}=\widehat{f}(\mathcal{Z}_t)\) to a multivariate forecast \((\widehat{y}_{t+1},\widehat{x}_{t+1})=(\widehat{f}(\mathcal{Z}_t),\widehat{f}_X(\mathcal{Z}_t))\), can produce updated forecasts by substitution rule
  • Adds some complexity since \(x\) forecasts produced, but limits complexity of multi-period forecast
    
• If original forecast is linear regression and other forecasts are also, this produces a VAR
  • VAR is natural extension of regression forecast
  • In fact, GDP equation in VAR example is exactly GDP equation in linear regression example

Economic Motivation for VAR models

• Can use forecasts to replace realized values not just with a known forecast rule, but also with other equations
  • If \(y_t\) related to \(x_t\), replace \(x_t\) with forecast to predict \(y_t\)
• Can be hard to think through which variables might predict \(y_t\) in the future
  • Easier to think of variables that might be related right now
• Economic theory and intuition can suggest variables and relationships which may matter
  • Incentives, tastes and beliefs, constraints, accounting identies, all those things you learn about in other econ classes
• If you can predict \(x_t\), then through contemporaneous relationship you can predict \(y_t\) (or vice-versa)
• Consider a simple example
  • Let \(y_t=ax_t+u_t\) where \(u_t\) is independent of \(x_t\) and over time
    
  • Suppose \(x_t\) depends on itself and \(z_{t-1}\), eg \(x_t=\rho x_{t-1}+\gamma z_{t-1}+e_t\) with \(e_t\) independent over time
  • Then \(y_t=(a\rho) x_{t-1}+(a\gamma) z_{t-1}+(u_{t}+ae_t)\): Lagged relationship depends on strength of persistence and on current relationship
• More elaborate versions of this example inspired Sims use of VAR models in macroeconomics
  • Existing macro theory c.1980 had a lot to say about contemporaneous relationships, very little about dynamics
  • Implied relationship over time a mix of both: predictive relationships for one variable can extend to others linked indirectly
  • For accurate predictions, many variables can be relevant

Classical Statistics Perspective

• Usual justification for autoregression forecasts is in terms of autoregressive probability model \[y_{t+1}=\beta_0+\sum_{j=1}^{p}\beta_{j}y_{t-j}+e_{t+1}\text{ for all t}\]
  • Where \(e_{t}\), the “residual” is independent or uncorrelated over time: stationary white noise
  • And \(E[e_{t+1}|\mathcal{Y}_t]=0\) for all \(t\): residual is unpredictable part
• If data is drawn from such a probability distribution, oracle forecast for square loss among any linear predictor based on past data would be autoregressive forecast rule \(\beta_0+\sum_{j=1}^{p}\beta_{j}y_{t-j}\)
  • Further, oracle forecast at horizon \(h\) would be the recursive update of this rule
  • If \(e_{t+1}\) iid Normal random variable, this is also the Bayes forecast: even nonlinear rules can’t do better
• Under some conditions on \(\beta\) so that the implied distribution is stationary
  • Rule selected by ERM for any AR(d) class with \(d\geq p\) gets close to oracle rule
• If data known to come from this model, recursive forecast not just reasonable, but optimal
• Also true for VARs and other regression forecasts with multivariate version of this model

Evaluating Autoregressive Forecasts

• Forecast residuals from optimal forecast \(\widehat{e}_{t+1}=y_{t+1}-\widehat{y}_{t+1}\) are white noise in autoregressive model
• This is in fact true when forecast rule is Bayes predictor in square loss for any stationary distribution
  • Holds for VARs, regression, etc
• So if you are using a rule which is approximately the Bayes rule, residuals should be close to white noise
  • ACF of \(\widehat{e}_{t+1}\) should be close to 0 everywhere
  • Use checkresiduals to inspect and compare various measures of distance
• Usually little reason to believe chosen family contains Bayes forecast rule
  • May not be exactly linear, or may depend on information further back than number of lags used
  • Or even if it did that approximation is close enough that all properties of Bayes forecast hold for approximate rule
• These two issues are in conflict
  • Can always increase complexity of hypothesis class to try to get closer to Bayes forecaster
  • But doing so can worsen overfitting, making rule actually chosen further away
• Residual checks measure closeness of fit, but are unrelated to degree of overfitting
  • Generally not a reliable tool for finding a good forecast, as opposed to checking ex post
  • Use genuine measures of loss instead!

Application to GDP Series in VAR(1) Example

checkresiduals(NIPAVAR$varresult$diff.log.gdp..) #GDP Growth forecast residual diagnostic

## 
##  Breusch-Godfrey test for serial correlation of order up to 10
## 
## data:  Residuals
## LM test = 35.798, df = 10, p-value = 9.119e-05

• Patterns in residual plot and ACF, and formal test of white noise, suggest VAR(1) is not super close to white noise
  • Suggests patterns in data may differ somewhat from VAR(1) model: in-sample fit could be improved
  • To decide whether this or another forecast should be used, should compare loss measures

Summary

• Multivariate forecasting relies on same principles as single variable case
  • Can improve predictive power by including other predictors and sharing information across forecasts
• Vector Autoregressions describe most natural extension of single outcome regression models
• Multi-period forecasts are a special case of multivariate forecasts
  • Recursive substitution method provides simple and parsimonious way to extend forecasts into the future
  • Suggests using multivariate forecasts to produce better predictions even if goal is longer horizon forecasts of just one item
• Autoregressive probability model shows properties of cases in which autoregressive forecasts are particularly desirable

Caveats

• While multivariate forecasts and models can improve quality of oracle predictions a lot
• But they can also massively increase problems with overfitting
  • VAR(p) in m variables has \(m^2(p+1)\) parameters: easily huge given large number of possibly relevant predictors
  • In practice, this seriously reduces performance of these methods, makes them less attractive in practice
• Suggests seeking out methods which do more to control overfitting than ERM over linear class
  • ERM looks explicitly only at empirical risk, not at all at generalization error
  • Popularity of VAR methods relies strongly on combination with techniques to control overfitting
• Next class: methods which take into account generalization error in a more explicit way
  • Ways to reduce complexity of class
  • Ways to choose element of class which generalizes better

References

• Christopher A. Sims, “Macroeconomics and reality.” Econometrica (1980): 1-48.
• Charles Stein, “Inadmissibility of the usual estimator for the mean of a multivariate distribution”, Proc. Third Berkeley Symp. Math. Statist. Prob. (1956) 1, pp. 197–206