# Construction Sector Role in Gross Fixed Capital Formation: Empirical Data from Russia

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Empirical Methodology

_{t}is a dependent variable Y at period t, X is the independent variable, a and b are the parameters with lag indication, and ε

_{t}is the unexplained part (gap) of the actual data and fitted line by the regression equation, termed as the error. For a one-off unit change in x, there is an impact on y; this impact is captured by b

_{0}; b

_{1}is the impact on y after one period, b

_{2}is the impact after 2 periods, and so on. The final impact on y is b

_{k}. If all t coefficients are collected {b

_{0}, b

_{1}, b

_{2}, …, b

_{k}}, they are called the impulse response function of the mapping of x

_{t}to y

_{t}. The above model is the lagged model accounting for the changes in { b

_{0}, b

_{1}, b

_{2}, …, b

_{k}} on x for lagged period t. On the y-axis, the y-dependent may also respond to exogeneous factor; thus, the ARDL may accommodate for both x and y.

## 4. Results

#### 4.1. Data Issues

#### 4.2. Empirical Results

#### 4.2.1. Results of Parameter Evaluation

#### 4.2.2. Results of Unit Root Tests

_{var}(0.850–0.940) and ADF (4.490 at 5%) were not cointegrated, which why the SUP index was excluded from our research.

#### 4.2.3. Results of the ARDL Models

_{RC}(RC/GFCF,CI). If the F

_{RC}(RC/GFCF,CI) dynamics of the tendency are strong (F(RC)

_{t-1}= 0.6345 → 1), (F(RC)

_{t-4}= 0.7943 → 1), then independent of F

_{GFCF}(GFCF/RC,CI) and F

_{IC}(IC/GFCF,RC), the dynamics of dependence are not observed (F → 0). The results are provided in Table 5.

_{RC}(RC/GFCF, CI) remained. Periodic oscillations in F

_{GFCF}(GFCF/RC,CI) have a period of four.

_{GFCF}is the index of construction, X

_{i}is the gross construction output, ΔX

_{i}is the increase in the gross construction output, and ΔGFCF is the increase in the gross value added.

_{tabl.}= 4.49). Since the actual value of F > F

_{tabl}, the estimated autoregression was a time series found to be statistically reliable. The elasticity coefficient was 0.93 (93%), indicating that a percentage change will occur in the variable GFCF when the RC variable changes 1%, that is, small changes in RC do modify the GFCF. The mean approximation error was 7.35; the calculated values deviated from the actual by 7.35%. Thus, the diagnostic tests, the ARDL model F

_{GFCF}(GFCF/RC,CI)

_{t-k}, and its analysis using linear regression (ΔRC

_{GFCF}) confirm the choice of model.

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Construction in Russia from 2000 to 2016. (

**а**) Volume of work performed by the economic activity “Construction” (percentage of corresponding period of previous year); (

**b**) volume of work performed by the economic activity “Construction”, billion rubles; and (

**c**) number of active construction organizations, thousand pieces.

**Figure 2.**Gross fixed capital formation (GFCF), gross domestic product (GDP), and gross value construction (RC) in Russia, %.

lag | ΔGFCF = f(SUP) | ΔGFCF = f(RC) | ΔGFCF = f(IC) |
---|---|---|---|

7 | 0.985045812 | 0.932874266 | 0.499845848 |

6 | 0.987725055 | 0.905850114 | 0.569685548 |

5 | 0.965343381 | 0.85755161 | 0.727798006 |

4 | 0.881776951 | 0.840451859 | 0.804727944 |

3 | 0.832232471 | 0.821348508 | 0.817885899 |

2 | 0.859457287 | 0.874365681 | 0.765398018 |

1 | 0.905539034 | 0.893287842 | 0.814934908 |

lag | ΔSUP = f(GFCF) | ΔRC = f(GFCF) | ΔIC = f(GFCF) |
---|---|---|---|

7 | 0.913892076 | 0.448950278 | –0.616410269 |

6 | 0.966367446 | 0.679542729 | 0.14622655 |

5 | 0.962124147 | 0.738654516 | 0.454208722 |

4 | 0.915996188 | 0.769489618 | 0.412950781 |

3 | 0.908163404 | 0.792722071 | 0.466605836 |

2 | 0.918128928 | 0.721287279 | 0.556031222 |

1 | 0.933430426 | 0.787836355 | 0.711410163 |

Variable | Xvar | Level/-Distribution | ||
---|---|---|---|---|

1% | 5% | 1% | 5% | |

inGFCF | 0.960 | 0.960 | 3.906 | 2.700 |

inSUP | 0.850 | 0.940 | 3.620 | 4.490 |

inRC | 0.972 | 0.920 | 1.862 | 2.668 |

inIC | 0.902 | 0.902 | 3.760 | 3.239 |

**Table 4.**The autoregressive distributed-lagged (ARDL) estimation results. GFCF, RC, and IC responses (2000–2016), annual data.

Lag | Dependent Variable | Coefficient | t-Statistic | p-Value |
---|---|---|---|---|

1 | F_{GFCF}(GFCF/RC,CI)_{t-1} | 0.974 *** | 23.38 | 0.954 |

F_{RC}(RC/GFCF,CI)_{t-1} | 0.9858 *** | 32.01 | 0.986 | |

F_{IC}(IC/GFCF,RC)_{t-1} | 0.9236 *** | 12.94 | 0.924 | |

2 | F_{GFCF}(GFCF/RC,CI)_{t-2} | 0.947 *** | 15.43 | 0.947 |

F_{RC}(RC/GFCF,CI)_{t-2} | 0.9861 *** | 21.4 | 0.971 | |

F_{IC}(IC/GFCF,RC)_{t-2} | 0.8769 *** | 12.94 | 0.924 | |

3 | F_{GFCF}(GFCF/RC,CI)_{t-3} | 0.974 *** | 23.38 | 0.937 |

F_{RC}(RC/GFCF,CI)_{t-3} | 0.9627 *** | 17.97 | 0.963 | |

F_{IC}(IC/GFCF,RC)_{t-3} | 0.8383 *** | 7.52 | 0.868 | |

4 | F_{GFCF}(GFCF/RC,CI)_{t-4} | 0.947 *** | 13.39 | 0.941 |

F_{RC}(RC/GFCF,CI)_{t-4} | 0.9548 *** | 15.56 | 0.955 | |

F_{IC}(IC/GFCF,RC)_{t-4} | 0.7885 *** | 5.94 | 0.924 |

_{GFCF}(GFCF/RC,CI)

_{t-1}is the dependent parameter functions with lag indication (Pesaran et al. 2001); *, ** and *** denote the statistical significance at 10%, 5% and 1% significance levels, respectively (Dickey and Fuller 1981).

**Table 5.**The ARDL estimation results. GFCF, RC, IC responses to external shocks (2006–2010), quarterly data.

Dependent Variable | Coefficient | t-Statistic | p-Value | |
---|---|---|---|---|

F_{GFCF}(GFCF/RC,CI)_{t-1} | 0.3988 | ** | 0.62 | 0.222 |

F_{GFCF}(GFCF/RC,CI)_{t-2} | 0.3129 | ** | – | – |

F_{GFCF}(GFCF/RC,CI)_{t-3} | –0.3972 | ** | – | – |

F_{GFCF}(GFCF/RC,CI)_{t-4} | –0.2222 | ** | – | – |

F_{RC}(RC/GFCF,CI)_{t-1} | 0.6345 | *** | 4.29 | 0.794 |

F_{RC}(RC/GFCF,CI)_{t-2} | 0.3193 | ** | – | – |

F_{RC}(RC/GFCF,CI)_{t-3} | 0.5055 | *** | – | – |

F_{RC}(RC/GFCF,CI)_{t-4} | 0.7943 | *** | – | – |

F_{IC}(IC/GFCF,RC)_{t-1} | 0.3065 | ** | 1.03 | 0.341 |

F_{IC}(IC/GFCF,RC)_{t-2} | –0.1906 | ** | – | – |

F_{IC}(IC/GFCF,RC)_{t-3} | 0.06296 | * | – | – |

F_{IC}(IC/GFCF,RC)_{t-4} | 0.3408** | ** | – | – |

_{GFCF}(GFCF/RC,CI)

_{t-1}is the dependent parameter functions with lag indication (Pesaran et al. 2001); *, ** and *** denote the statistical significance at 10%, 5% and 1% significance levels, respectively (Dickey and Fuller 1981).

**Table 6.**The ARDL estimation results. GFCF, RC, and IC responses to external shocks (2013–2016), quarterly data.

Dependent Variable | Coefficient | t-Statistic | p-Value | |
---|---|---|---|---|

F_{GFCF}(GFCF/RC,CI)_{t-1} | 0.4817 | ** | 1.56 | 0.466 |

F_{GFCF}(GFCF/RC,CI)_{t-2} | –0.01816 | * | – | – |

F_{GFCF}(GFCF/RC,CI)_{t-3} | –0.354 | * | – | – |

F_{GFCF}(GFCF/RC,CI)_{t-4} | –0.4656 | * | – | – |

F_{RC}(RC/GFCF,CI)_{t-1} | 0.7546 | *** | 3.3 | 0.717 |

F_{RC}(RC/GFCF,CI)_{t-2} | 0.6643 | *** | – | – |

F_{RC}(RC/GFCF,CI)_{t-3} | 0.6594 | ** | – | – |

F_{RC}(RC/GFCF,CI)_{t-4} | 0.7169 | *** | – | – |

F_{IC}(IC/GFCF,RC)_{t-1} | 0.3065 | ** | 1.03 | 0.341 |

F_{IC}(IC/GFCF,RC)_{t-2} | –0.1906 | * | – | – |

F_{IC}(IC/GFCF,RC)_{t-3} | 0.06296 | * | – | – |

F_{IC}(IC/GFCF,RC)_{t-4} | 0.3408 | ** | – | – |

_{GFCF}(GFCF/RC,CI)

_{t-1}is the parameter-dependent functions with lag indication (Pesaran et al. 2001); *, ** and *** denote the statistical significance at 10%, 5% and 1% significance levels, respectively (Dickey and Fuller 1981).

Indicator | Value |
---|---|

Sum of squares | 0.363 |

Number of degrees of freedom | 18–1 |

F-criterion | 26.02 |

Goldfeld–Quandt test | 1.24 |

Coefficient of determination | 0.6192 |

Coefficient of elasticity | 0.93 |

Mean approximation error | 7.35 |

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**MDPI and ACS Style**

Stupnikova, E.; Sukhadolets, T.
Construction Sector Role in Gross Fixed Capital Formation: Empirical Data from Russia. *Economies* **2019**, *7*, 42.
https://doi.org/10.3390/economies7020042

**AMA Style**

Stupnikova E, Sukhadolets T.
Construction Sector Role in Gross Fixed Capital Formation: Empirical Data from Russia. *Economies*. 2019; 7(2):42.
https://doi.org/10.3390/economies7020042

**Chicago/Turabian Style**

Stupnikova, Elena, and Tatyana Sukhadolets.
2019. "Construction Sector Role in Gross Fixed Capital Formation: Empirical Data from Russia" *Economies* 7, no. 2: 42.
https://doi.org/10.3390/economies7020042