# An introduction to pspatreg

#### Román Mínguez (UCLM), Roberto Basile (UNIVAQ), María Durbán (UC3M)

#### 2023-10-06

# 1 Introduction

pspatreg is a package that fits penalized spline (PS) semiparametric static and dynamic spatial autoregressive models via Restricted (or Residual) Maximum Likelihood (REML) and Maximum Likelihood (ML). This approach combines penalized regression spline methods (Paul HC Eilers, Marx, and Durbán 2015) with standard spatial autoregressive models (such as SAR, SEM and SDM). These types of models are thoroughly discussed in Mı́nguez, Basile, and Durbán (2020); see also Montero, Mı́nguez, and Durbán (2012), Basile et al. (2014), and Hoshino (2018).

These models are very flexible since they make it possible to include within the same specification: i) spatial autoregressive terms (i.e. spatial lags of dependent and independent variables as well as spatial error terms) to capture spatial interaction or network effects; ii) time lags of the dependent variable to capture persistence effects; iii) parametric and nonparametric (smooth) terms to identify nonlinear relationships between the response variable and the covariates; iv) a spatio-temporal trend, i.e. a smooth interaction between the spatial coordinates and the time trend, to capture site-specific nonlinear time trends.

The proposed method also allows the user to apply an ANOVA decomposition of the spatio-temporal trend into several components (spatial and temporal main effects, and second- and third-order interactions between them), which gives further insights into the dynamics of the data. Thus, we use the acronym PS-ANOVA-SAR (SEM, SDM, SLX) for the new data generating process (DGP) proposed. The use of nested B-spline bases for the interaction components of the spatio-temporal trend contributes to the efficiency of the fitting procedure without compromising the goodness of fit of the model. Finally, we also consider an extension of the PS-ANOVA-SAR (SEM, SDM, SLX) including the time lag of the dependent variable (dynamic spatial model) and/or a first-order time series autoregressive term process (AR1) in the noise to accommodate residual serial correlation.

## 1.1 The semiparametric spatial autoregressive model

In a very general form, the semiparametric spatial autoregressive model reads as:

\[ y_{it}=\alpha y_{it-1}+\rho \sum_{j=1}^N w_{ij,N} y_{jt} + \pi \sum_{j=1}^N w_{ij,N} y_{jt-1}+ \\ \sum_{k=1}^K \beta^*_k x^*_{k,it} + \sum_{k=1}^K \gamma^*_k \sum_{j=1}^N w_{ij,N} x^*_{k,jt}+ \sum_{\delta=1}^\Delta g_\delta(x_{\delta, it}) + \sum_{\delta=1}^\Delta h_\delta\left(\sum_{j=1}^N w_{ij,N} x_{\delta,jt}\right) + \\ \widetilde{ f}(s_{1i},s_{2i},\tau_t)+\epsilon_{it} \]

\[\epsilon_{it}=\theta \sum_{j=1}^N w_{ij,N}\epsilon_{jt}+\phi \epsilon_{it-1}+u_{it}\] \[u_{it} \sim i.i.d.(0,\sigma^2_u)\]

where \((y_{it},x^*_{1,it},...,x^*_{K,it},x_{1,it},...,,x_{\Delta,it})\) are data observed for a sample of spatial units \(i\) (\(i=1,...,N\)) in each time period \(t=1,...,T\). The terms \((s_{1i},s_{2i})\) indicate the spatial coordinates (latitude and longitude) of the spatial unit \(i\) (either a spatial point or the centroid of a spatial polygon), and \(W_{ij}\) is an element of a row-standardized spatial weights matrix. The terms included into the model are:

- \(y_{it-1}\) = the time lag of \(y_{it}\);

- \(\sum_{j=1}^N w_{ij,N} y_{it}\) = the contemporaneous spatial lag of \(y_{it}\);

- \(\sum_{j=1}^N w_{ij,N} y_{it-1}\) = the time lag of the spatial lag of \(y_{it}\);

- \(\alpha\), \(\rho\), \(\pi\), \(\beta^*_k\), and \(\gamma^*_k\) = fixed parameters;

- \(\sum_{k=1}^K \beta^*_k x^*_{k,it}\) and \(\sum_{k=1}^K \gamma^*_k \sum_{j=1}^N w_{ij,N} x^*_{k,jt}\) = parametric linear terms of some covariates \(x_{k,it}\) and of their spatial lags;

- \(g_\delta(.)\) and \(h_\gamma(.)\) = nonparametric smooth functions of other covariates and of their spatial lags (they can also accommodate varying coefficient terms, smooth interaction between covariates, factor-by-curve intercations, and so on);

- \(\widetilde{ f}(s_{1i},s_{2i},\tau_t)\) = an unknown nonparametric spatio-temporal trend;

- \(\epsilon_{it}\) = an idiosyncratic error term that can follow a spatial autoregressive process \(\epsilon_{it}=\theta \sum_{j=1}^N w_{ij,N}\epsilon_{it}+u_{it}\) with \(u_{it}\sim N(0,\sigma^2)\) (SEM), a time series autoregressive AR(1) process, i.e., \(\epsilon_{it}=\phi \epsilon_{it-1}+u_{it}\) with \(u_{it}\sim N(0,\sigma^2)\), or both.

Obviously, this very general specification is hardly suitable in real data applications. However, it is worth noticing that it nests most of the spatial additive models which can be used in practice. For example, a more suitable semiparametric static model reads as:

\[y_{it}=\rho \sum_{j=1}^N w_{ij,N} y_{jt} + \sum_{k=1}^K \beta^*_k x^*_{k,it} + \sum_{\delta=1}^\Delta g_\delta(x_{\delta, it}) + \widetilde{ f}(s_{1i},s_{2i},\tau_t)+\epsilon_{it}\]

\[\epsilon_{it}=\phi \epsilon_{it-1}+u_{it}\] \[u_{it} \sim i.i.d.(0,\sigma^2_u)\] This semiparametric SAR model turns out to be extremely useful to capture interactive spatial and temporal unobserved heterogeneity when the last one is smoothly distributed over space and time (Mı́nguez, Basile, and Durbán 2020). The dynamic extension (including \(y_{it-1}\) and \(\sum_{j=1}^N w_{ij,N} y_{it-1}\)) is also very promising and merits further theoretical investigation. Finally, the following semiparametric SAR model is very useful for modeling cross-setional spatial data taking into account nonlinearities, spatial dependence and spatial heterogeneity:

\[y_{i}=\rho \sum_{j=1}^N w_{ij,N} y_{j} + \sum_{k=1}^K \beta^*_k x^*_{k,i} + \sum_{\delta=1}^\Delta g_\delta(x_{\delta, i}) + \widetilde{ f}(s_{1i},s_{2i})+\epsilon_{i}\]

\[\epsilon_{i}\sim i.i.d.(0,\sigma^2_\epsilon)\] In many situations the spatio-temporal trend to be estimated can be complex, and the use of a single multidimensional smooth function may not be flexible enough to capture the structure in the data. To solve this problem, an ANOVA-type decomposition of \(\widetilde{ f}(s_{1i},s_{2i},\tau_t)\) can be used, where spatial and temporal main effects, and second- and third-order interactions between them can be identified:

\[\widetilde{ f}(s_{1i},s_{2i},\tau_t)=f_1(s_{1i})+f_2(s_{2i})+f_{\tau}(\tau_t)+ \\ f_{1,2}(s_{1i},s_{2i})+f_{1,\tau}(s_{1i},\tau_t)+f_{2,\tau}+(s_{2i},\tau_t)+f_{1,2,\tau}(s_{1i},s_{2i},\tau_t)\]

First, the geoadditive terms given by \(f_1(s_{1i}),f_2(s_{2i}),f_{1,2}(s_{1i},s_{2i})\) work as control functions to filter the spatial trend out of the residuals, and transfer it to the mean response in a model specification. Thus, they make it possible to capture the shape of the spatial distribution of \(y_{it}\), conditional on the determinants included in the model. These control functions also isolate stochastic spatial dependence in the residuals, that is, spatially autocorrelated unobserved heterogeneity. Thus, they can be regarded as an alternative to the use of individual regional dummies to capture unobserved heterogeneity, as long as such heterogeneity is smoothly distributed over space. Regional dummies peak at significantly higher and lower levels of the mean response variable. If these peaks are smoothly distributed over a two-dimensional surface (i.e., if unobserved heterogeneity is spatially autocorrelated), the smooth spatial trend is able to capture them.

Second, the smooth time trend, \(f_{\tau}(\tau_t)\), and the smooth interactions between space and time - \(f_{1,\tau}(s_{1i},\tau_t),f_{2,\tau},(s_{2i},\tau_t),f_{1,2,\tau}(s_{1i},s_{2i},\tau_t)\) - work as control functions to capture the heterogeneous effect of common shocks. Thus, conditional on a smooth distribution of the spatio-temporal heterogeneity, the PS-ANOVA-SAR (SDM, SEM, SLX) model works as an alternative to the models proposed by Bai and Li (2013), Shi and Lee (2018), Pesaran and Tosetti (2011), Bailey, Holly, and Pesaran (2016) and Vega and Elhorst (2016) based on extensions of common factor models to accommodate both strong cross-sectional dependence (through the estimation of the spatio-temporal trend) and weak cross-sectional dependence (through the estimation of spatial autoregressive parameters).

Furthermore, this framework is also flexible enough to control for the linear and nonlinear functional relationships between the dependent variable and the covariates as well as the heterogeneous effects of these regressors across space. The model inherits all the good properties of penalized regression splines, such as coping with missing observations by appropriately weighting them, and straightforward interpolation of the smooth functions.

## 1.2 Computational approach to estimation

All the non-parametric terms are modeled using Penalized-splines (P-Splines, P. H. Eilers and Marx (1996)). This methodology assumes that the unknown function to be estimated is smooth, and can be represented as a linear combination of basis functions,

\[f(x)=\sum_j \theta_j B_j(x),\] a popular election is the use of cubic B-spline basis (De Boor 1977). In the case of multidimensional functions, the basis is calculated as tensor products of the marginal basis functions in each dimension. The smoohness of the curve/surface is controlled by adding to the likelihood a penalty tunned by a smoothing parameter, \(\lambda\) (the number of smoothing parameters used is equal to the dimension of the function to be estimated). The penalty can take many forms, depending of the prior knowledge on the curve/surface. The most common choice is to use second order differences on adjacent B-spline coefficients (see Mı́nguez, Basile, and Durbán 2020). In the case of a univariate function, \(f(x)\), the penalty is:

\[ Penalty= \lambda\sum_j \left ( \theta_j-2\theta_{j-1}+\theta_{j-2}\right )^2.\]

As we mentioned above, the model is estimated via REML or ML. In order to be able to estimate simultaneously all parameters in the model (including the smothing parameters) we make use of the fact that a P-spline can be reparameterized as a mixed model, and so, all the methodology debeloped in this context can be use for estimation and inference. Furthermore, the mixed model setting allows us to impose the necessary constraints so that all terms in the model are identifiable, as well as, to add spatial (or other) random effects if they are necessary.

To speed up computations, we use a modification of the SOP (Separation of Anisotropic Penalties) algorithm derived by Rodriguez-Alvarez et al. (2015) (for variance components estimation). Also, the use of nested B-spline bases (Lee, Durban, and Eilers 2013) for the interaction components of the spatio-temporal trend contributes to the efficiency of the fitting procedure without compromising the goodness of fit of the model. See Mı́nguez, Basile, and Durbán (2020) for more details.

## 1.3 Installation

This package is available in GitHub (https://github.com/rominsal/pspatreg) and can be installed in the usual way.1

# 2 Basic information on the package

The package is accompanied by other two vignettes B_Examples_pspatreg_CS_data.html and C_Examples_pspatreg_Panel_data.html, introducing the application of `pspatreg` to cross-sectional and panel data. Here, we introduce some basic general information about the package.

## 2.1 The function `pspatfit()`

The main function in the `pspatreg` package is `pspatfit()`, which estimates spatio-temporal pernalized spline spatial regression models using either the Restricted Maximum Likelihood (REML) method or the Maximum Likelihood (ML) method. In its generic form `pspatfit()` appears as:

`pspatfit(formula, data, na.action, listw = NULL,type = "sim", method = "eigen", Durbin = NULL, zero.policy = NULL,` `interval = NULL,trs = NULL, cor = "none", dynamic = FALSE, control = list())`

The function `pspatfit()` returns a list of quantities of class `pspat`, including coefficients of the parametric terms and their standard errors, estimated coefficients corresponding to random effects in mixed model and their standard errors, equivalent degrees of freedom, residuals, fitted values, etc. A wide range of standard methods is also available for the `pspat` objects, including `print()`, `summary()`, `coef()`, `vcov()`, `anova()`, `fitted()`, `residuals()`, and `plot()`.

### 2.1.1 The argument `formula`

The argument `formula` within the function `pspatfit()` is formula similar to GAM specification including parametric and non-parametric terms. Parametric covariates are included in the usual way and non-parametric p-spline smooth terms are specified using `pspl(.)` and `pspat(.)` for the non-parametric covariates and spatial or spatio-temporal trends, respectively. For example

    formula <- y ~ x1 + x2 + pspl(x3, nknots = 15) + pspl(x4, nknots = 20) +
                      pspt(long, lat, year, nknots = c(18,18,8),
                           psanova = TRUE,
                           nest_sp1 = c(1, 2, 3),
                           nest_sp2 = c(1, 2, 3),
                           nest_time = c(1, 2, 2))

In the example above, the model includes two parametric terms, two nonparametric terms, and a spatio-temporal trend (with long and lat as spatial coordinates, and year as temporal coordinate). The dimension of the basis function both in `pspl(.)` and `pspt(.)` is defined by `nknots`. This term should not be less than the dimension of the null space of the penalty for the term (see `null.space.dimension` and `choose.k` from package `mgcv` to know how to choose `nknots`). The default number of `nknots` in `pspl(.)` is 10, but in this example we have chosen 15 `nknots` for `g_1(x_3)` and 20 `nknots` for `g_2(x_4)`. The default number of `nknots` in `pspt(.)` is `c(10,10,5)`, but we have chosen `c(18,18,8)`.

In this example we also adopt an ANOVA decomposition of the spatio-temporal trend (choosing `psanova = TRUE`). Each effect has its own degree of smoothing allowing a greater flexibility for the spatio-temporal trend. Calculating up to third-order interactions can be computationally expensive. To address this problem, we can select subgroups of interaction effects for the second- and third-order effects. To define these subgroups, we use three parameters available in `pspt()`: `nest_sp1`, `nest_sp2`, and `nest_time`. These parameters indicate the divisors of the `nknots` parameters. For example, if we set `nest_sp1 = c(1,2,3)`, we will have all knots for the `s_1` effect, 18/2 for each second-order effects with `s_1`, and 18/3 nots for the third order effect with `s_1`.2

If we want to set any main effect to `0`, we must set the parameters `f1_main`, `f2_main` or `ft_main` to `FALSE`, The default is `TRUE`. We can also exclude second- or third-order effects setting `f12_int`, `f1t_int`, `f2t_int`, `f12t_int` to `FALSE`.

### 2.1.2 The argument `Type`

Using the argument `Type` we can choose different spatial model specifications: `"sar"`, `"sem"`, `"sdm"`, `"sdem"`, `"sarar"`, or `"slx"`. When creating a `"slx"`, `"sdem"` or `"sdm"` model, we need to include the formula of the durbin part in the Durbin parameter.

### 2.1.3 Data structure

The argument `data` must contain all the variables included in parametric and non-parametric terms of the model. If a `pspat(.)` term is included in `formula`, the data must contain the spatial and temporal coordinates specified in `pspat(.)`. In this case, the coordinates must be ordered choosing time as fast index and spatial coordinates as slow indexes.

Both `data.frame` and `sf` class objects can be used as `data` inputs.3 `sf` objects are recommended since they allow the user to map spatial trends. In our demos we use two datasets in `sf` version.

## 2.2 Plotting smooth terms

Plotting the estimated non-parametric smooth terms represents an important step in semiparametric regression analyses. First, the function `fit_terms()` computes estimated non-parametric smooth terms. Then, the functions `plot_sp2d()` and `plot_sp3d()` are used to plot and map spatial and spatio-temporal trends, respectively, while `plot_sptime()` is used to plot the time trend for PS-ANOVA models in 3d; finally, and `plot_terms()` is used to plot smooth non-parametric terms.

## 2.3 Computing marginal impacts

In the case of a semiparametric model without the spatial lag of the dependent variable (PS model), if all regressors are manipulated independently of the errors, \(\widehat{g}_\delta(x_{\delta, it})\) can be interpreted as the conditional expectation of \(y\) given \(x_{\delta}\) (net of the effect of the other regressors). @blu.pow.03 use the term Average Structural Function (ASF) with reference to these functions. Instead, in PS-SAR, PS-SDM or in PS-SARAR model, when \(\rho\) is different from zero, the estimated smooth functions cannot be interpreted as ASF. Taking advantage of the results obtained for parametric SAR, we can compute the total smooth effect (total–ASF) of \(x_{\delta}\) as:
\[\widehat{g}_{\delta}^{T}\left(x_{\delta}\right)=\Sigma_{q} \left[\textbf{I}_{n}-\widehat{\rho}\textbf{W}_{n}\right]^{-1}_{ij} b_{\delta q}(x_{\delta})\widehat{\beta}_{\delta q}\]

where \(b_{\delta q}(x_{\delta})\) are P-spline basis functions, and \(\widehat{\beta}_{\delta q}\) the corresponding estimated parameters.

We can also compute direct and indirect (or spillover) impacts of smooth terms in the PS-SAR case as:

\[\widehat{g}_{\delta}^{D}\left(x_{\delta}\right)=\Sigma_{q} \left[\textbf{I}_{n}-\widehat{\rho}\textbf{W}_{n}\right]^{-1}_{ii} b_{\delta q}(x_{k})\widehat{\beta}_{\delta q}\]

\[\widehat{g}_{\delta}^{I}\left(x_{\delta}\right)=\widehat{g}_{\delta}^{T}\left(x_{\delta}\right)-\widehat{g}_{\delta}^{D}\left(x_{\delta}\right)\]

Similar expressions can be provided for the direct, indirect and total impacts of the PS-SDM.

The function `impactspar()` computes direct, indirect and total impacts for continuous parametric covariates using the standard procedure for their computation (LeSage and Pace 2009).

The function `impactsnopar()` computes direct, indirect and total impacts functions for continuous non-parametric covariates, while the function `plot_impactsnopar()` is used to plot these impacts functions. It is worth noticing that total, direct and indirect impacts are never smooth over the domain of the variable \(x_\delta\) due to the presence of the spatial multiplier matrix in the algorithm for their computation. Indeed, a wiggly profile of direct, indirect and total impacts would appear even if the model were linear. Therefore, in the spirit of the semiparametric approach, we included the possibility of applying a spline smoother to obtain smooth curves (using the argument `smooth=TRUE` in the function `plot_impactsnopar()`).

# References

Bai, J., and K. Li. 2013. “Spatial Panel Data Models with Common Shocks.” MPRA Paper 52786. University of Munich, Germany. https://ideas.repec.org/p/pra/mprapa/52786.html.
Bailey, Natalia, Sean Holly, and M. Hashem Pesaran. 2016. “A Two-Stage Approach to Spatio-Temporal Analysis with Strong and Weak Cross-Sectional Dependence.” Journal of Applied Econometrics 31 (1): 249–80.
Basile, Roberto, María Durbán, Román Mínguez, Jose María Montero, and Jesús Mur. 2014. “Modeling Regional Economic Dynamics: Spatial Dependence, Spatial Heterogeneity and Nonlinearities.” Journal of Economic Dynamics and Control 48: 229–45. https://doi.org/10.1016/j.jedc.2014.06.011.
De Boor, C. 1977. “Package for Calculating with B-Splines.” Journal of Numerical Analysis 14: 441–72.
Eilers, P. H., and B. D. Marx. 1996. “Flexible Smoothing with B-Splines and Penalties.” Statistical Science 11: 89–121.
Eilers, Paul HC, Brian D Marx, and Maria Durbán. 2015. “Twenty Years of p-Splines.” SORT-Statistics and Operations Research Transactions 39 (2): 149–86.
Hoshino, T. 2018. “Semiparametric Spatial Autoregressive Models with Endogenous Regressors: With an Application to Crime Data.” Journal of Business & Economic Statistics 36: 160–72.
Lee, D. J., M. Durban, and P. Eilers. 2013. “Efficient Two-Dimensional Smoothing with P-Spline ANOVA Mixed Models and Nested Bases.” Computational Statistics & Data Analysis 61: 22–37.
LeSage, J., and K. Pace. 2009. Introduction to Spatial Econometrics. Boca Raton: CRC Press.
Mı́nguez, Román, Roberto Basile, and Marı́a Durbán. 2020. “An Alternative Semiparametric Model for Spatial Panel Data.” Statistical Methods & Applications 29 (4): 669–708. https://doi.org/10.1007/s10260-019-00492-8.
Montero, J, R Mı́nguez, and M Durbán. 2012. “SAR Models with Nonparametric Spatial Trends. A P-Spline Approach.” Estadı́stica Española 54 (177): 89–111.
Pesaran, M Hashem, and Elisa Tosetti. 2011. “Large Panels with Common Factors and Spatial Correlation.” Journal of Econometrics 161 (2): 182–202.
Rodriguez-Alvarez, M. X., T. Kneib, M. Durban, D. J. Lee, and P. Eilers. 2015. “Fast Smoothing Parameter Separation in Multidimensional Generalized P-Splines: The SAP Algorithm.” Statistics and Computing 25 (5): 941–57.
Shi, Wei, and Lung-fei Lee. 2018. “A Spatial Panel Data Model with Time Varying Endogenous Weights Matrices and Common Factors.” Regional Science and Urban Economics 72: 6–34.
Vega, Solmaria Halleck, and J Paul Elhorst. 2016. “A Regional Unemployment Model Simultaneously Accounting for Serial Dynamics, Spatial Dependence and Common Factors.” Regional Science and Urban Economics 60: 85–95.

1. To install any R package from GitHub you need to have previously installed `devtools` package from CRAN. Then execute the commands `library(devtools)`, to load `devtools`, and install `github("rominsal/pspatreg")` to install `pspatreg` package: `devtools::install_github("rominsal/pspatreg")`.↩︎

2. In most empirical cases, the main effects are more flexible than interaction effects and therefore the number of knots in B-Spline bases for interaction effects do not need to be as large as the number of knots for the main effects (Lee, Durban, and Eilers 2013).↩︎

3. `sf` means simple features of spatial vector objects. The geographic vector data model is based on points located within a coordinate reference system (CRS). Points can represent self-standing features (e.g., the location of a house) or they can be linked together to form more complex geometries such as lines and polygons. Most point geometries contain only two dimensions `x` and `y` (3-dimensional CRSs contain an additional `z` value, typically representing height above sea level). `sf` objects provide both a geometry information, describing where on Earth the feature is located, and attributes information, describing other properties (like the population of the region, the unemployment rate, etc.). `data.frame` objects store only attributes information.↩︎