State Space Models

Outline

• State Space Models
• Linear State Space Models
  • Components of state space models
• Exponential Smoothing
  
• Applications
  • Inflation Forecasting
  • DSGE

State Space Models

• State Space Models are a particular class of hidden variable models
  • Structure makes them easier to use, but still flexible enough to describe wide variety of patterns
• State equation: \(x_{t}\sim f_t(x|x_{t-1})\)
  • Each period, latent process evolves randomly from conditional distribution \(f_t\) that only depends on current state
• Observation equation \(y_{t}\sim g_t(y|x_t)\)
  • Data you see drawn from conditional distribution \(g_t\) that depends only on current state
• Use state to describe all the things going on over time and how they change, observation to describe how what you observe is related to it
• Distributions \(\{f_t(),g_t()\}_{t=1}^{T}\) (and sometimes initial state \(x_0\)) are parameters of model
  • Different specifications allow for wide variety of dynamic behaviors
  • In discrete case, called a Hidden Markov Model, otherwise a state space model
• Sequence \(\mathcal{X}_T=\{x_t\}_{t=1}^{T}\) are random variables but aren’t seen
  • In Bayesian approach, can treat them like parameters, which are also random

Inference and Likelihood

• Likelihood of the data \(\ell(\mathcal{Y}_{T})\) given by \(\int\int\ldots\int\Pi_{t=1}^{T} g_{t}(y_{t}|x_{t})f_t(x_{t}|x_{t-1})\Pi_{t=1}^{T}dx_{t}\)
• So, likelihood based methods like MLE, MAP, or full Bayes require computing T-dimensional integral
• Fortunately, procedure can be split up into computing conditional likelihoods by process known as filtering

1. Begin with \(p_{t-1}(x_{t-1}|\mathcal{Y}_{t-1})\) (at \(t=1\), this is \(p(x_0)\))
2. Predict next period \(x\): \(p_t(x_{t}|\mathcal{Y}_{t-1})=\int f_{t}(x_t|x_{t-1})p_{t-1}(x_{t-1}|\mathcal{Y}_{t-1})dx_{t-1}\)
   
3. Construct conditional likelihood: \(\ell(y_{t}|\mathcal{Y}_{T-1})= \int g_t(y_t|x_t)p_t(x_{t}|\mathcal{Y}_{t-1})dx_t\)
4. Use Bayes rule to predict \(x_t\): \(p(x_t|\mathcal{Y}_t)=\frac{g_t(y_t|x_t)p_t(x_{t}|\mathcal{Y}_{t-1})}{\ell(y_{t}|\mathcal{Y}_{T-1})}\)
5. Go back to 1

• From this procedure, obtain conditional likelihood for inference and prediction
  • Also get sequence of filtered distributions \(p(x_{t}|\mathcal{Y}_{t})\) which give best guess of state as of time \(t\)
• Now only one integral per step: still a challenge, but more feasible: approximate integrals sequentially
• For discrete data case (HMM) steps have known closed form
• Linear gaussian case also has explicit steps: Kalman Filter

Linear State Space Model

• By far most common subclass of state space models assumes linear equations and normal shocks
  • Mostly for computational reasons, but can do a lot with just this
• Observation Equation
  • \(y_t=a_{t}x_t+b_{t}u_t\)
  • \(u_t\overset{iid}{\sim}N(0,\Sigma_u)\)
  • Describes how observed variables \(y_t\) are determined by latent variables \(x_t\)
• State Equation
  • \(x_{t}=c_{t}x_{t-1}+d_{t}e_t\)
  • \(e_t\overset{iid}{\sim}N(0,\Sigma_e)\)
    
  • Describes how latent variables change over time
• Invented to predict rocket trajectories, used for sending people to the moon in 1960s
• Example: local level model \(y_t=x_t+u_t\) \(x_{t}=x_{t-1}+e_t\)
  • Latent state is random walk, describing stochastic trend, observable contains both this trend and a noise component
  • Data behaves like random walk (has unit root), but noise data beyond most recent observation needed to uncover state

Dynamic Regression Model

• Many models can be written in state space form by clever choice of state and parameters
• Can include regression model by letting state be the coefficient and coefficient be the data
  • Observations \(\{y_t\}_{t=1}^{T}\) and predictor \(\{a_t\}_{t=1}^{T}=\{z_t\}_{t=1}^{T}\)
  • Model is \(y_t=z_t\beta+u_t\)
• Here, state \(x_t\) is coefficient \(\beta\)
  • Since constant over time \(\beta_{t}=\beta_{t-1}+0*e_t\), with intitial value \(\beta_0=\beta\)
• This leads to simple extension: dynamic regression model where coefficient varies over time
  • \(y_t=z_t\beta_t+u_t\)
  • \(\beta_{t}=\beta_{t-1}+e_t\)
  • Allows predictor \(z_t\) to have smoothly evolving effect over time
• Can also allow more general process for \(\beta_t\)
  • \((\beta_{t}-b)=\phi(\beta_{t-1}-b)+e_t\)
  • Allows coefficient to vary around long run mean \(b\)

Multivariate Linear State Space Model

• Let \(y_{t}\in\mathbb{R}^n\), \(x_t\in\mathbb{R}^m\) be multiple observations, multiple states
  • Use this to represent many components of series
• Observation Equation \(y_{i,t}=\sum_{j=1}^{n}a_{i,j,t}x_{j,t}+\sum_{k=1}^{m}b_{i,k,t}u_{i,t}\) for \(i=1\ldots m\)
  • \(\{u_{i,t}\}_{i=1}^{m}\) jointly \(N(0,\Sigma_u)\) with \(\Sigma_{u}\) the covariance across shocks
  • Vector representation \(y_t=Ax_t+Bu_t\)
• State Equation \(x_{j,t}=\sum_{k=1}^{n}c_{j,k,t}x_{k,t-1}+\sum_{l=1}^{m}d_{j,l,t}e_{l,t}\) for \(j=1\ldots n\)
  • \(\{e_{j,t}\}_{j=1}^{n}\) jointly \(N(0,\Sigma_e)\) with \(\Sigma_{e}\) the covariance across shocks
  • Vector representation \(x_{t}=Cx_{t-1}+De_t\)
• Any set of univariate state equations can be combined additively to make a multivariate state space model
  • State equations are the same, observation equation now \(y_t=\sum_{j=1}^n a_{i,j}x_{j,t}\)
  • Allows building model out of many components, each of which describes some aspect of model
  • Additive components model is special case, but now can have many random components as well

Building blocks of multivariate state space models

• The following components are possible options that can be included in such a model
• Deterministic trend, seasonality, and holiday components exactly as in additive model
  • Represent values parameters which depend on time
• Linear regression with any set of predictors \(z_t\)
  • Can extend to case where coefficients vary over time also
• ARIMA models
  • Any ARIMA model has a state space representation
  • This is in fact how likelihood for ARIMA models built in standard commands
• Time varying drift
  • \(\mu_{t}=\mu_{t-1}+\delta_{t-1}+e_{1t}\), \((\delta_t-D)=\phi (\delta_{t-1}-D)+e_{2t}\)
  • Allows local level model to drift upwards or downwards at rate “close to” D
• Seasonality
  • Can allow seasonal components to change over time

State Space Representation of ARMA models

• Standard representation has observation depending only on current state
  • And state depending on only most recent past state
• Can incorporate longer history by adding states which “store” previous values
• AR(p) model \(y_t=\sum_{j=1}^{p}b_jy_{t-j}+\epsilon_t\)
• Let state be \(x_{t}=[y_t,y_{t-1},\ldots,y_{t-p+1}]^\prime\), \(e_t=[\epsilon_t,0,\ldots,0]\)
• Observation equation is \(y_t=x_{1,t}\) 0 coefficient on all states except current
• State equations for \(x_t\)
  • First is \(x_{1,t}=\sum_{j=1}^{p}b_jx_{j,t-1}+\epsilon_t\)
  • Each subsequent one is just \(x_{j,t}=x_{j-1,t-1}\) for \(j=2\ldots p\)
• State stores lagged values, and update to next position ensures \(y_{t-j}\) today becomes \(y_{t-j-1}\) tomorrow
• ARMA(p,q) process representation uses similar trick, with more complicated formulas
• Likewise for seasonality: Assume seasonal components sum to 0 on average
  • \(s_{1,t}=-\sum_{j=2}^{m}s_{j,t}+e_t\) is first state equation
  • Add \(s_{j,t}=s_{j-1,t-1}\) for \(j=2\ldots m\) so seasons rotate on cycle of length \(m\)

Estimation of Linear State Space Models

• With linar gaussian models, each update step in filtering procedure has known formula
• Each conditional density \(p_t(x_{t}|\mathcal{Y}_{t})\) is Gaussian, so update formulas reduce to finding new means and covariances
  • Kalman filter formulas are several lines of computationally straightforward matrix algebra
• If observation and state equation known, filter produces conditional likelihood \(\ell(y_{t+1}|\mathcal{Y}_T)\) to use for forecasting
• If parameters \(\theta\) determining \(A(\theta)\), \(B(\theta)\), \(C(\theta)\), \(D(\theta)\) unknown, filter gives likelihood at each \(\theta\)
• Maximum likelihood: at each \(\theta\), run Kalman filter to produce likelihood, iterating over parameters to find maximizer
  • Under usual stationarity and weak dependence, usually chooses \(\widehat{\theta}\) near optimum if \(T\) large
  • Like with MA special case, identifiability issues may make solution non-unique, or approximation less precise
• Bayesian case is standard: Given prior \(\pi(\theta)\), posterior \(\propto\ell(\mathcal{Y}_T|\theta)\pi(\theta)\)
  • Run Kalman filter inside MCMC sampler to calculate likelihood at each point
  • Posterior predictive density \(p(y_{t+1}|\mathcal{Y}_T)\) generated by integrating conditional likelihood over posterior
  • Common to choose gaussian priors over coefficients as in other linear models for tractability
• In \(R\), library dlm executes Kalman filter and MLE for arbitrary model, many functions use special cases
  
• Library bsts from Google Data Science provides convenient interface for Bayesian case
  • Common components built in, reasonable default priors, and fast samplers

Exponential Smoothing

• Exponential smoothing models are particularly simple class of state space models
• State innovation \(e_t\) and observation innovation \(u_t\) are the same (equivalently, perfectly correlated)
  • Allows closed form forecast rule and simple likelihood formula
• Many varieties correspond to different components in rule
• Simple Exponential Smoothing \(y_t=\ell_{t-1}+\epsilon_t\) \(\ell_t=\ell_{t-1}+\alpha\epsilon_t\)
  • Like local level model, but dependent shocks give exact updates
• Additive (damped) trend method: \(y_t=\ell_{t-1}+\phi b_{t-1}+\epsilon_t\), \(\ell_t=\ell_{t-1}+\phi b_{t-1}+\alpha\epsilon_t\), \(b_{t}=\phi b_{t-1}+\beta\epsilon_t\)
  • Adds drift \(b_t\) to account for trends, with size moving according to AR(1) model
• Seasonal component: \(y_t=\ell_{t-1}+s_{t-m}+\epsilon_t\) \(s_t=s_{t-m}+\gamma\epsilon_t\), \(\ell_t=\ell_{t-1}+\alpha\epsilon_t\)
  • Adds seasonal component (with moving size) to account for frequency \(m\) repetitions
• Can also combine seasonal with trend, or change additive to multiplicative
• ets() command estimates by MLE, choosing components by AICc if not specified
• ets is default method in forecast() command
  • Flexible enough to capture persistence, seasonality, trends, with few parameters: reasonable choice for short series with not much data

Filtering vs Smoothing

• State space models particularly valuable for their interpretability
  • Allow decomposing data into multiple components with known structure
• While not needed for general forecasting, can be useful to recover values of \(x_t\)
  
• Due to presence of multiple components, exact value cannot be known, but can construct a posterior distribution
• Recovering \(p(\mathcal{X}_T|\mathcal{Y}_T)\) is called smoothing
• Contrasts with filtering, which produces \(p(x_t|\mathcal{Y}_t)\) using only past, and not also future data
  • Future observations provide additional information regarding current state
• Smoothing generally computationally complicated, but simple formulas in linear Gaussian case
• Common to present smoothed components as interpretable summary of different parts of series
  • Since only obtain density of \(x_t\), not exact value, present means and intervals

Example: State Space Decomposition

• Take Consumer Price Index Inflation as an example for decomposition
  • Exhibits large persistent spikes in 1970s which might be hard to capture with stationary ARMA component
• Exponential smoothing can be used to proide simple decomposition
  • AICc suggests to choose model with additive damped trend (and no seasonality, as series deseasonalized)
  • Unlike generic state space models, because only one shock, can recover components exactly rather than perform smoothing
• Based on this, build less restricted linear state space model and apply Bayesian estimate using BSTS
  • Start with “semilocal linear trend” component: exactly ETS model but observation, trend, and drift shocks are independent normal
  • Normal prior on slope parameters, inverse gamma on variances
• Fed Funds Rate, primary interest rate set by monetary policy, exhibits similar patterns to inflation series
  • Add a dynamic regression component: \(\beta_tFFR_t\) with \(\beta_t=\beta_{t-1}+e_{t}\)
  • Reflects possibly changing relationship between policy and outcomes
  • With multiple random components, get time series of posterior distributions

library(fredr) #FRED Data
library(fpp2) # Forecasting tools
library(bsts) #Bayesian State Space Models
library(gridExtra) #Graph Display

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

#One Year Percent Change in Consumer Price Index for All Urban Consumers
INFLATION<-fredr(series_id = "CPIAUCSL",units="pc1") 

#Fed Funds Rate
FEDFUNDS<-fredr(series_id = "FEDFUNDS") 
fedfunds<-ts(FEDFUNDS$value,frequency=12,start=c(1954,7),names="FFR")

inflation1<-ts(INFLATION$value,frequency=12,start=c(1947,1),names="CPI")
#Truncate to same window as Fed Funds Rate
inflation<-window(inflation1,start=c(1954,7))

#monetarydata<-as.ts(data.frame(inflation,fedfunds),frequency=12,start=c(1954,7))

# Estimate Exponential Smoothing Model of Inflation
# Choose components by AICc, estimate parameters by maximum likelihood
inflETSmodel<-ets(inflation)

#Construct 6 month forecast

inflETSfcst<-forecast(inflETSmodel,h=6)

Exponential Smoothing Component Decomposition: \(y_t\), \(\ell_t\), \(b_t\)

#Plot components and variance decomposition
autoplot(inflETSmodel)

# Build a Bayesian State Space Model of Inflation

#Start by adding components to specification object

ss<-AddSemilocalLinearTrend(
     state.specification = list(), #Initialize with empty list
     y=inflation) #Use data to define scale of prior

ss<-AddDynamicRegression(state.specification=ss,
                         inflation~fedfunds)

# Set number of MCMC draws, and number to discard as "burn in" in analysis
niter<-1000
burnins<-500
  
#Construct model posterior by MCMC sampling
model <- bsts(inflation, state.specification = ss, niter = niter)

## =-=-=-=-= Iteration 0 Mon Oct 28 00:36:32 2019 =-=-=-=-=
## =-=-=-=-= Iteration 100 Mon Oct 28 00:36:33 2019 =-=-=-=-=
## =-=-=-=-= Iteration 200 Mon Oct 28 00:36:34 2019 =-=-=-=-=
## =-=-=-=-= Iteration 300 Mon Oct 28 00:36:35 2019 =-=-=-=-=
## =-=-=-=-= Iteration 400 Mon Oct 28 00:36:36 2019 =-=-=-=-=
## =-=-=-=-= Iteration 500 Mon Oct 28 00:36:37 2019 =-=-=-=-=
## =-=-=-=-= Iteration 600 Mon Oct 28 00:36:38 2019 =-=-=-=-=
## =-=-=-=-= Iteration 700 Mon Oct 28 00:36:39 2019 =-=-=-=-=
## =-=-=-=-= Iteration 800 Mon Oct 28 00:36:40 2019 =-=-=-=-=
## =-=-=-=-= Iteration 900 Mon Oct 28 00:36:41 2019 =-=-=-=-=

coefdraw<-matrix(nrow=niter,ncol=length(fedfunds))
for (i in 1:niter){
  coefdraw[i,]<-model$dynamic.regression.coefficients[i,1,]
}

#Remove first 500 draws as burnin
coefdraws<-coefdraw[(burnins+1):niter,]

# Make forecast under contingency that fed funds rate stays constant for next 6 months
cffr<-last(fedfunds)
newffr<-c(cffr,cffr,cffr,cffr,cffr,cffr)

newdata<-data.frame(newffr)
colnames(newdata)<-c("fedfunds")


#Construct posterior predictive distribution by sampling
pred <- predict(model, newdata=newdata,
                horizon = 6, burn = burnins)

BSTS Filtered Decomposition into Components

#Time series of posterior distributions for trend and dynamic regression components
PlotBstsComponents(model)

BSTS Filtered Coefficients

#Time series of Coefficient posterior distributions
PlotDynamicDistribution(coefdraws) 

## NULL

Results and Interpretation

• Huge uncertainty in trend component, with most driven by FFR until recently
• Then inflation more stable and link to FFR much weaker
  • Coincides with Zero Lower Bound period where interest rate constrained?
• Forecast with regression requires forecasting predictors
  • Either use lagged predictors or supplement with additional forecasts
    
• Here, construct conditional forecasts given Fed Funds Rate stays constant
  • Produces conditional posterior predictive distribution
• Point forecasts similar across models, but uncertainty intervals much wider for full state space model
  • Reflects uncertainty in multiple components added together

BSTS vs ETS Forecasts

#ETS
autoplot(inflETSfcst)

#BSTS
plot(pred)

Application: Macroeconomic Forecasting with DSGE Models

• Central banks value models which permit both forecasting and interpretation
• State space models with Bayesian priors over equations provide reasonable forecast performance
  • Due to flexibility to capture data features but substantial regularization from priors
• Also provide interpretability since components can be backed out by smoothing
• Most central banks now use version where parameterization of model based on economic theory
• “Dynamic Stochastic General Equilibrium” (DSGE) model solves system of economic equilibrium equations to form a state space model
  • Priors over economic parameters used to put priors over coefficients
  • Introduced at European Central Bank by Smets & Wouters (2003)
• Forecast performance similar to BVARs, but policymakers find it useful to see which shocks responsible for outcome
  • Answers entirely dependent on structure of model, so substantial economic intuition needed to interpret
• Following figure an example: NY Fed DSGE’s latest (Jan 2019) inflation forecasts
  • Along with estimate of a latent variable they call “natural rate of interest”, that policymakers care about but is not directly observed
    
  • Comes from multivariate stae space model with many components corresponding to macroeconomic relationships

Jan 2019 NY Fed DSGE Forecasts and Estimated State

Conclusions

• State Space Models are structured way to build model from many probabilistic and deterministic components
• Construction of likelihood requires filtering, which is easiest in Linear Gaussian case
• Kalman Filter approach encompasses models which can account for many structural components, additional predictors
• Can use smoothing to construct posterior distribution of components and interpret movements in series

References

• Michael Cai, Marco Del Negro, Ethan Matlin, and Reca Sarfati. “The New York Fed DSGE Model Forecast—January 2019” (2/8/2019)
  
• E. E. Holmes, M. D. Scheuerell, and E. J. Ward “Applied Time Series Analysis for Fisheries and Environmental Sciences”
  • Text on state space modeling in environmental science, with R libraries for MLE and Bayesian estimation
• Frank Smets and Raf Wouters. “An estimated dynamic stochastic general equilibrium model of the euro area.” Journal of the European economic association 1, no. 5 (2003): 1123-1175.
  • First Bayesian DSGE in use by a central bank
• Case studies of state space models for forecasting with BSTS library
  • Kim Larsen “Sorry ARIMA, but I’m Going Bayesian” (2016)
    • Stitchfix (online retailer)
  • Steven Scott “Fitting Bayesian structural time series with the bsts R package” (2017)
    • Google Data Science