Skip to content

Commit

Permalink
add real data example in readme files
Browse files Browse the repository at this point in the history
  • Loading branch information
@clemasso
clemasso committed Jul 19, 2024
1 parent dd15866 commit 3fbd6e9
Show file tree
Hide file tree
Showing 6 changed files with 178 additions and 96 deletions.
101 changes: 51 additions & 50 deletions README.Rmd
View file
Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,7 @@ remotes::install_github("rjdverse/rjd3nowcasting")

## Usage

``` r
```{r loading-library}
library("rjd3nowcasting")
```

Expand All @@ -85,72 +85,73 @@ Detailed information concerning each step can be found in the vignette.

### 1. Input

``` r
set.seed(100)
data0 <- stats::ts(
data = matrix(rnorm(500), 100, 5),
frequency = 12,
start = c(2010, 1)
)
data0[100, 1] <- data0[99:100, 2] <- data0[(1:100)[-seq(3, 100, 3)], 5] <- NA

data1 <- stats::ts(
data = rbind(data0, c(NA, NA, 1, 1, NA)),
frequency = 12,
start = c(2010, 1)
)
data1[100,1] <- data1[99,2] <- 1
```{r input}
data("data0", "data1")
data0_ts<-ts(data0[,-1], start=c(2012,1), frequency = 12)
data1_ts<-ts(data1[,-1], start=c(2012,1), frequency = 12)
```

### 2. Create or update the model

``` r
### Create model from scratch
dfm0 <- create_model(nfactors=2,
nlags=2,
factors_type = c("M", "M", "YoY", "M", "Q"),
factors_loading = matrix(data = TRUE, 5, 2),
var_init = "Unconditional")

### Update model
est0 <- estimate_em(dfm0, data0) # cfr. next step

dfm1 <- est0$dfm # R object (list) to potentially save from one time to another
```{r model}
### Create a model from scratch
ns<-ncol(data0_ts)
types<-c("M","M","Q","YoY","YoY","M","M","YoY","YoY","YoY")
loadings1<-matrix(data=TRUE, ns, 2)
#loadings2<-matrix(data=c(rep(TRUE,ns+5),rep(FALSE,5)), ns, 2)
dfm0_init <- create_model(nfactors=2,
nlags=2,
factors_type = types,
factors_loading = loadings1,
var_init = "Unconditional")
### Update an existing model
est0 <- estimate_em(dfm0_init, data0_ts) # cfr. next step
dfm1_init <- est0$dfm # R object (list) to save from one time to another
# or, equivalently,
dfm1 <- create_model(nfactors=2,
nlags=2,
factors_type = c("M", "M", "YoY", "M", "Q"),
factors_loading = matrix(data = TRUE, 5, 2),
var_init = "Unconditional",
var_coefficients = est0$dfm$var_coefficients,
var_errors_variance = est0$dfm$var_errors_variance,
measurement_coefficients = est0$dfm$measurement_coefficients,
measurement_errors_variance = est0$dfm$measurement_errors_variance)
# dfm1_init <- create_model(nfactors=2,
# nlags=2,
# factors_type = types,
# factors_loading = loadings1,
# var_init = "Unconditional",
# var_coefficients = est0$dfm$var_coefficients,
# var_errors_variance = est0$dfm$var_errors_variance,
# measurement_coefficients = est0$dfm$measurement_coefficients,
# measurement_errors_variance = est0$dfm$measurement_errors_variance)
```

### 3. Estimate the model

``` r
est1 <- estimate_ml(dfm1, data1)
# or est1<-estimate_em(dfm1, data1)
# or est1<-estimate_pca(dfm1, data1)
```{r estimation}
est0 <- estimate_ml(dfm0_init, data0_ts)
# or est0<-estimate_em(dfm0_init, data0_ts)
# or est0<-estimate_pca(dfm0_init, data0_ts) # to be avoided unless you only have monthly data
```

### 4. Get results

``` r
rslt1 <- get_results(est1)
print(rslt1)
fcst1 <- get_forecasts(est1, n_fcst = 2)
print(fcst1)
plot(fcst1)
#### Model and forecasts

news1 <- get_news(est0, data1, target_series = "Series 1", n_fcst = 2)
print(news1)
```{r results}
rslt0 <- get_results(est0)
print(rslt0)
fcsts0 <- get_forecasts(est0, n_fcst = 3)
print(fcsts0)
plot(fcsts0, series_name = "FR_PVI")
# Note: impact of Covid-19 on the model estimate could be eliminated by treating affected indicators as missing data during this period. This has not been done in this example.
```

#### News analysis

```{r news}
news1 <- get_news(est0, data1_ts, target_series = "FR_PVI", n_fcst = 3)
news1$impacts
news1$forecasts
plot(news1)
# ...
```


## Package Maintenance and contributing

Any contribution is welcome and should be done through pull requests and/or issues.
Expand Down
173 changes: 127 additions & 46 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -79,71 +79,152 @@ Detailed information concerning each step can be found in the vignette.
### 1. Input

``` r
set.seed(100)
data0 <- stats::ts(
data = matrix(rnorm(500), 100, 5),
frequency = 12,
start = c(2010, 1)
)
data0[100, 1] <- data0[99:100, 2] <- data0[(1:100)[-seq(3, 100, 3)], 5] <- NA

data1 <- stats::ts(
data = rbind(data0, c(NA, NA, 1, 1, NA)),
frequency = 12,
start = c(2010, 1)
)
data1[100,1] <- data1[99,2] <- 1
data("data0", "data1")
data0_ts<-ts(data0[,-1], start=c(2012,1), frequency = 12)
data1_ts<-ts(data1[,-1], start=c(2012,1), frequency = 12)
```

### 2. Create or update the model

``` r
### Create model from scratch
dfm0 <- create_model(nfactors=2,
nlags=2,
factors_type = c("M", "M", "YoY", "M", "Q"),
factors_loading = matrix(data = TRUE, 5, 2),
var_init = "Unconditional")

### Update model
est0 <- estimate_em(dfm0, data0) # cfr. next step

dfm1 <- est0$dfm # R object (list) to potentially save from one time to another
### Create a model from scratch
ns<-ncol(data0_ts)
types<-c("M","M","Q","YoY","YoY","M","M","YoY","YoY","YoY")
loadings1<-matrix(data=TRUE, ns, 2)
#loadings2<-matrix(data=c(rep(TRUE,ns+5),rep(FALSE,5)), ns, 2)
dfm0_init <- create_model(nfactors=2,
nlags=2,
factors_type = types,
factors_loading = loadings1,
var_init = "Unconditional")

### Update an existing model
est0 <- estimate_em(dfm0_init, data0_ts) # cfr. next step
dfm1_init <- est0$dfm # R object (list) to save from one time to another
# or, equivalently,
dfm1 <- create_model(nfactors=2,
nlags=2,
factors_type = c("M", "M", "YoY", "M", "Q"),
factors_loading = matrix(data = TRUE, 5, 2),
var_init = "Unconditional",
var_coefficients = est0$dfm$var_coefficients,
var_errors_variance = est0$dfm$var_errors_variance,
measurement_coefficients = est0$dfm$measurement_coefficients,
measurement_errors_variance = est0$dfm$measurement_errors_variance)
# dfm1_init <- create_model(nfactors=2,
# nlags=2,
# factors_type = types,
# factors_loading = loadings1,
# var_init = "Unconditional",
# var_coefficients = est0$dfm$var_coefficients,
# var_errors_variance = est0$dfm$var_errors_variance,
# measurement_coefficients = est0$dfm$measurement_coefficients,
# measurement_errors_variance = est0$dfm$measurement_errors_variance)
```

### 3. Estimate the model

``` r
est1 <- estimate_ml(dfm1, data1)
# or est1<-estimate_em(dfm1, data1)
# or est1<-estimate_pca(dfm1, data1)
est0 <- estimate_ml(dfm0_init, data0_ts)
# or est0<-estimate_em(dfm0_init, data0_ts)
# or est0<-estimate_pca(dfm0_init, data0_ts) # to be avoided unless you only have monthly data
```

### 4. Get results

#### Model and forecasts

``` r
rslt0 <- get_results(est0)
print(rslt0)
#> $loadings
#> Sample mean Stdev Coeff. F1 Coeff. F2 Idiosyncratic variance
#> FR_PVI -0.018 3.347 0.651 0.630 0.150
#> FR_TURN 0.178 3.915 0.621 0.603 0.219
#> FR_B1g_BCDE 0.110 4.219 0.145 0.141 0.257
#> FR_BS -5.962 6.726 0.212 0.006 0.126
#> FR_BS_prdexp 7.933 9.487 0.183 0.031 0.419
#> EA_PVI 0.027 2.695 0.646 0.627 0.162
#> EA_TURN 0.190 3.122 0.653 0.638 0.131
#> EA_BS -2.056 8.037 0.236 0.045 0.048
#> EA_BS_prdexp 8.349 9.070 0.235 0.100 0.173
#> EA_PMI_manuf 51.313 5.111 0.249 0.170 0.109
#>
#> $VAR_model
#> F1[-1] F2[-1] F1[-2] F2[-2]
#> F1 0.710 -0.253 -0.690 0.260
#> F2 -0.508 0.489 0.333 -0.624
#>
#> $Innovative_variance
#> F1 F2
#> F1 1.000 -0.151
#> F2 -0.151 1.000

fcsts0 <- get_forecasts(est0, n_fcst = 3)
print(fcsts0)
#> $forecasts_only
#> FR_PVI FR_TURN FR_B1g_BCDE FR_BS FR_BS_prdexp EA_PVI
#> Apr 2024 NA -0.26520376 -0.9066921 NA NA NA
#> May 2024 -0.5557794 -0.41682622 -1.0911187 NA NA -0.3988934
#> Jun 2024 0.1699863 0.36989280 -0.5998834 -9.138949 3.915219 0.1656424
#> Jul 2024 0.1685482 0.36225137 -0.5747306 -6.960827 6.381705 0.1606429
#> Aug 2024 -0.2416552 -0.07873533 -0.1738075 -6.907477 6.470485 -0.1560932
#> Sep 2024 -0.2809204 -0.09964879 -0.0680737 -8.495403 4.882437 -0.1728226
#> EA_TURN EA_BS EA_BS_prdexp EA_PMI_manuf
#> Apr 2024 -0.111319199 NA NA NA
#> May 2024 -0.291450017 NA NA NA
#> Jun 2024 0.301963860 -6.471711 3.067721 47.94640
#> Jul 2024 0.278853788 -3.807055 5.688887 49.26765
#> Aug 2024 -0.043318670 -3.706038 5.851589 49.39729
#> Sep 2024 0.002645719 -5.383872 4.670237 49.15457
plot(fcsts0, series_name = "FR_PVI")
```

<img src="man/figures/README-results-1.png" width="100%" style="display: block; margin: auto;" />

``` r
# Note: impact of Covid-19 on the model estimate could be eliminated by treating affected indicators as missing data during this period. This has not been done in this example.
```

#### News analysis

``` r
rslt1 <- get_results(est1)
print(rslt1)
fcst1 <- get_forecasts(est1, n_fcst = 2)
print(fcst1)
plot(fcst1)

news1 <- get_news(est0, data1, target_series = "Series 1", n_fcst = 2)
print(news1)
news1 <- get_news(est0, data1_ts, target_series = "FR_PVI", n_fcst = 3)
news1$impacts
#> series period expected_value observed_value
#> 1 FR_PVI 5-2024 -0.551604008710279 -2.10323226057412
#> 2 FR_TURN 4-2024 -0.158827203273009 1.5012792389185
#> 3 FR_BS 6-2024 -9.16421530477352 -7.9
#> 4 FR_BS_prdexp 6-2024 3.89240182586572 2.3
#> 5 EA_PVI 5-2024 -0.395537116403581 -0.615386557437869
#> 6 EA_TURN 4-2024 -0.0213558345482549 0.606324941105818
#> 7 EA_BS 6-2024 -6.49531828333652 -10.1
#> 8 EA_BS_prdexp 6-2024 3.05743620889786 0.5
#> 9 EA_PMI_manuf 6-2024 47.9509402506111 45.8
#> 10 TOTAL <NA> <NA> <NA>
#> news impacts(6-2024) impacts(7-2024)
#> 1 -1.55162825186384 0.0625426516614255 0.25062588561052
#> 2 1.6601064421915 -0.102937609850668 -0.0213553608864313
#> 3 1.26421530477352 -0.0324518053679323 -0.00408631517117787
#> 4 -1.59240182586572 -0.013283314097744 -0.00280694611263804
#> 5 -0.219849441034288 0.00987846987975813 0.0404545554456015
#> 6 0.627680775654073 -0.0853868086082372 -0.0178364405879784
#> 7 -3.60468171666348 -0.496517141653302 -0.101958959531745
#> 8 -2.55743620889786 -0.267633977600985 -0.0504429432184094
#> 9 -2.1509402506111 -1.17139914720656 -0.216781814558934
#> 10 <NA> -2.09718868284424 -0.124188339011193
#> impacts(8-2024)
#> 1 0.0349315424038756
#> 2 0.0315210711205507
#> 3 0.00797192122461071
#> 4 0.00468621804722846
#> 5 0.00571578862082551
#> 6 0.0254483699135738
#> 7 0.171450179562596
#> 8 0.0867506968615175
#> 9 0.374677629475995
#> 10 0.743153417230773
news1$forecasts
#> 6-2024 7-2024 8-2024
#> old_forecasts 0.1699863 0.16854824 -0.2416552
#> revised_forecasts 0.1376619 0.15925989 -0.2335544
#> new_forecasts -1.9595268 0.03507155 0.5095990
plot(news1)
# ...
```

<img src="man/figures/README-news-1.png" width="100%" style="display: block; margin: auto;" />

## Package Maintenance and contributing

Any contribution is welcome and should be done through pull requests
Expand Down
Binary file added man/figures/README-news-1.png
View file
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Binary file added man/figures/README-results-1.png
View file
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Binary file removed man/figures/README-unnamed-chunk-7-1.png
View file
Binary file not shown.
Binary file removed man/figures/README-unnamed-chunk-8-1.png
View file
Binary file not shown.

0 comments on commit 3fbd6e9

Please sign in to comment.