# Testing the Ricardian Equivalence Theorem: Time Series Evidence from Turkey

## Abstract

**:**

## 1. Introduction

#### 1.1. The Impact of the Rise in Government Debt

#### 1.2. Ricardian Equivalence Theory

#### 1.3. Economic Indicators Affected by RET

## 2. Research Aim and Objectives

## 3. Literature Review

- Income life-cycle hypothesis: Consumers wish to smooth their consumption over the course of their lives. Thus, if consumers anticipate a rise in taxes in the future, they will save their current tax cuts to be able to pay future tax rises.
- Rational expectations on behalf of consumers. Consumers respond to tax cuts by realizing that they will probably mean future taxes have to rise.
- Perfect capital markets: Households can borrow to finance consumer spending if needed.
- Intergenerational altruism: Tax cuts for the present generation may imply tax rises for future generations. Therefore, it is assumed that an altruistic parent will respond to current tax cuts by trying to give more wealth to their children so they can pay the more increased tax in the future.

#### 3.1. The Inter-Temporal Government Budget Constraint

#### 3.2. Importance of Ricardian Equivalence

#### 3.3. Relevance of Ricardian Equivalence in Turkey

#### 3.4. Criticisms of Ricardian Equivalence

#### 3.5. Empirical Evidence

## 4. Methodological Framework

#### 4.1. Variables for the Study

**Domestic government borrowings (GD):**The term is used for a country’s governmental debt securities that are issued to raise money, both by the center and the state. These securities act as the main source for the government to finance their rising expenses (Adofu and Abula 2010). The data on the variable were collected in terms of government debt in Turkey, in percent of GDP terms.

**Gross domestic savings (GDS):**Gross domestic savings is used as a proxy for private savings which is defined as the amount of money saved by a given household. The level of private savings is impacted by the factors such as the demographics, financial status, political instability in the country affecting individual decisions, macroeconomic uncertainty, personal income, and internal government policies (Gök 2014). For the current study, the data for the variable were collected in the form of gross domestic savings in Turkey in USD.

**Governmental final consumption expenditure:**The total amount of national income which is used for individual consumption such as education, housing, and healthcare. Apart from this, it includes the consumption required for collective consumption, including defense, justice, and so on. This category of expenditure includes the social transfers which are undertaken from the government to the households (Eurostat and OECD 2012).

**Household final consumption expenditure:**This expenditure comprises of all the purchases which are made by the resident households and the everyday needs, including food, clothing, transport, and durable goods. Apart from this, it includes the all the imputed expenditures including the agricultural products and owner-occupied imputed rents. Thus, all the goods and services which are brought by households in order to meet the everyday needs. The household’s individual consumption comprises general government expenditure and expenditure which directly benefits all the households (Emeka and Kelvin 2016).

#### 4.2. Data Collection Procedure

#### 4.3. Data Analysis Procedure

_{0}+ B

_{1}GDS+ B

_{3}GD + B

_{4}GFCE

## 5. Empirical Analysis

#### 5.1. Augmented Dickey–Fuller Unit Root Test

#### 5.2. Johansen’s Cointegration Test

_{2}. Hence, the results of Johansen’s cointegration test point to the presence of a maximum of 1 cointegrating equation between variables.

#### 5.3. Specifying the Static Model

#### 5.4. Auto-Regressive Distributed Lag Model

#### 5.5. Granger Causality Test

## 6. Conclusions

## Funding

## Conflicts of Interest

## Appendix A

Test Statistic | 1% Critical Value | 5% Critical Value | 10% Critical Value | ||||
---|---|---|---|---|---|---|---|

Z(t) | −1.787 | −4.27 | −3.552 | −3.211 | |||

MacKinnon approximate p-value for Z(t) = 0.7108 | |||||||

D.GDS | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | ||

GDS | |||||||

L1. | −0.1334065 | 0.074635 | −1.79 | 0.083 | −0.285084 | 0.018271 | |

_trend | 1.15 × 10^{9} | 5.20 × 10^{8} | 2.2 | 0.034 | 8.94 × 10^{7} | 2.20 × 10^{9} | |

_cons | −4.92 × 10^{9} | 5.51 × 10^{9} | −0.89 | 0.378 | −1.61 × 10^{10} | 6.27 × 10^{9} | |

Test Statistic | 1% Critical Value | 5% Critical Value | 10% Critical Value | ||||

Z(t) | −0.862 | −4.27 | −3.552 | −3.211 | |||

MacKinnon approximate p-value for Z(t) = 0.9600 | |||||||

D.GD | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | ||

GD | |||||||

L1. | −0.0584846 | 0.067825 | −0.86 | 0.395 | −0.1963222 | 0.079353 | |

_trend | 4.84 × 10^{8} | 7.00 × 10^{8} | 0.69 | 0.494 | −9.38 × 10^{8} | 1.91 × 10^{9} | |

_cons | 4.71 × 10^{9} | 6.33 × 10^{9} | 0.74 | 0.462 | −8.16 × 10^{9} | 1.76 × 10^{10} | |

Test Statistic | 1% Critical Value | 5% Critical Value | 10% Critical Value | ||||

Z(t) | 1.946 | −4.27 | −3.552 | −3.211 | |||

MacKinnon approximate p-value for Z(t) = 0.6306 | |||||||

D.HFC | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | ||

HFC | |||||||

L1. | −0.1561244 | 0.080237 | −1.95 | 0.06 | −0.3191865 | 0.006938 | |

_trend | 2.89 × 10^{9} | 1.40 × 10^{9} | 2.07 | 0.047 | 4.62 × 10^{7} | 5.73 × 10^{9} | |

_cons | −7.07 × 10^{9} | 1.29 × 10^{10} | −0.55 | 0.587 | −3.33 × 10^{10} | 1.92 × 10^{10} | |

Test Statistic | 1% Critical Value | 5% Critical Value | 10% Critical Value | ||||

Z(t) | −1.797 | −4.27 | −3.552 | −3.211 | |||

MacKinnon approximate p-value for Z(t) = 0.7060 | |||||||

D.GFCE | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | ||

GFCE | |||||||

L1. | −0.1040432 | 0.057884 | −1.8 | 0.081 | −0.2216769 | 0.013591 | |

_trend | 5.47 × 10^{8} | 2.44 × 10^{8} | 2.24 | 0.031 | 5.15 × 10^{7} | 1.04 × 10^{9} | |

_cons | −2.31 × 10^{9} | 2.59 × 10^{9} | −0.89 | 0.379 | −7.57 × 10^{9} | 2.95 × 10^{9} |

Test Statistic | 1% Critical Value | 5% Critical Value | 10% Critical Value | |||
---|---|---|---|---|---|---|

Z(t) | −2.831 | −4.27 | −3.552 | −3.211 | ||

MacKinnon approximate p-value for Z(t) = 0.1857 | ||||||

D.logGDS | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | |

logGDS | ||||||

L1. | −0.35413 | 0.125075 | −2.83 | 0.008 | −0.60831 | −0.09994 |

_trend | 0.029548 | 0.010499 | 2.81 | 0.008 | 0.008212 | 0.050885 |

_cons | 8.273331 | 2.905278 | 2.85 | 0.007 | 2.369096 | 14.17757 |

Test Statistic | 1% Critical Value | 5% Critical Value | 10% Critical Value | |||

Z(t) | 0.314 | −4.27 | −3.552 | −3.211 | ||

MacKinnon approximate p-value for Z(t) = 0.9963 | ||||||

D.logGD | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | |

logGD | ||||||

L1. | 0.025154 | 0.0802 | 0.31 | 0.756 | −0.13783 | 0.188139 |

_trend | −0.00524 | 0.007668 | −0.68 | 0.499 | −0.02082 | 0.010343 |

_cons | −0.46224 | 1.877338 | −0.25 | 0.807 | −4.27745 | 3.352971 |

Test Statistic | 1% Critical Value | 5% Critical Value | 10% Critical Value | |||

Z(t) | −2.419 | −4.27 | −3.552 | −3.211 | ||

MacKinnon approximate p-value for Z(t) = 0.3697 | ||||||

D.logHFC | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | |

logHFC | ||||||

L1. | −0.33162 | 0.137099 | −2.42 | 0.021 | −0.61024 | −0.053 |

_trend | 0.027534 | 0.011921 | 2.31 | 0.027 | 0.003307 | 0.051761 |

_cons | 8.083918 | 3.309086 | 2.44 | 0.02 | 1.359047 | 14.80879 |

Test Statistic | 1% Critical Value | 5% Critical Value | 10% Critical Value | |||

Z(t) | −2.494 | −4.27 | −3.552 | −3.211 | ||

MacKinnon approximate p-value for Z(t) = 0.3309 | ||||||

D.logGFCE | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | |

logGFCE | ||||||

L1. | −0.26642 | 0.106823 | −2.49 | 0.018 | −0.48352 | −0.04933 |

_trend | 0.027595 | 0.011051 | 2.5 | 0.018 | 0.005137 | 0.050054 |

_cons | 5.956746 | 2.364802 | 2.52 | 0.017 | 1.150891 | 10.7626 |

Test Statistic | 1% Critical Value | 5% Critical Value | 10% Critical Value | |||
---|---|---|---|---|---|---|

Z(t) | −7.522 | −4.279 | −3.556 | −3.214 | ||

MacKinnon approximate p-value for Z(t) = 0.0000 | ||||||

D.dlogGDS | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | |

dlogGDS | ||||||

L1. | −1.2599 | 0.167506 | −7.52 | 0 | −1.6007 | −0.91911 |

_trend | 0.001751 | 0.003218 | 0.54 | 0.59 | −0.0048 | 0.008298 |

_cons | 0.050001 | 0.068583 | 0.73 | 0.471 | −0.08953 | 0.189534 |

Test Statistic | 1% Critical Value | 5% Critical Value | 10% Critical Value | |||

Z(t) | −5.721 | −4.279 | −3.556 | −3.214 | ||

MacKinnon approximate p-value Z(t) = 0.0000 | ||||||

D.dlogGD | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | |

dlogGD | ||||||

L1. | −0.99783 | 0.174411 | −5.72 | 0 | −1.35267 | −0.64299 |

_trend | −0.00315 | 0.001825 | −1.72 | 0.094 | −0.00686 | 0.000567 |

_cons | 0.129583 | 0.043177 | 3 | 0.005 | 0.041739 | 0.217428 |

Test Statistics | 1% Critical Value | 5% Critical Value | 10% Critical Value | |||

Z(t) | −6.321 | −4.279 | −3.556 | −3.214 | ||

MacKinnon approximate p-alue for Z(t) = 0.0000 | ||||||

D.dlogHFC | Coef. | Std. Err. | t | p > t | [95% Conf. Interval] | |

dlogHFC | ||||||

L1. | −1.09545 | 0.17329 | −6.32 | 0 | −1.44801 | −0.74288 |

_trend | −0.00114 | 0.002696 | −0.42 | 0.676 | −0.00662 | 0.004349 |

_cons | 0.09661 | 0.058684 | 1.65 | 0.109 | −0.02278 | 0.216003 |

lag | LL | LR | df | p | FPE | AIC | HQIC | SBIC |
---|---|---|---|---|---|---|---|---|

0 | 88.5908 | 7 × 10^{−8} | −5.12671 | −5.06568 | 4.94532 | |||

1 | 100.08 | 24.418 | 16 | 0.081 | 8.90 × 10^{−8} | −4.89695 | −4.59178 | −3.98997 |

2 | 113.553 | 25.507 | 16 | 0.061 | 1.10 × 10^{−7} | −4.70019 | −4.15088 | −3.06764 |

3 | 125.121 | 23.135 | 16 | 0.11 | 1.70 × 10^{−7} | −4.43157 | −3.63813 | −2.07343 |

4 | 143.473 | 36.706 | 16 | 0.002 | 1.90 × 10^{−7} | −4.57419 | −3.53661 | −1.49047 |

dlogHFC | Coef. | Std. Err. | z | p > z | [95% Conf. Interval] | |
---|---|---|---|---|---|---|

dlogGFCE | 0.72022 | 0.08684 | 8.29 | 0 | 0.550017 | 0.890424 |

dlogGD | 0.168922 | 0.116688 | 1.45 | 0.148 | −0.05978 | 0.397626 |

dlogGDS | 0.140744 | 0.07342 | 1.92 | 0.055 | −0.00316 | 0.284645 |

_cons | −0.00785 | 0.015577 | −0.5 | 0.614 | −0.03838 | 0.022678 |

Coef. | Std. Err. | z | p > z | [95% Conf. Interval] | |||
---|---|---|---|---|---|---|---|

GDS | |||||||

GDS | |||||||

L1. | 0.527108 | 0.225943 | 2.33 | 0.02 | 0.084268 | 0.969948 | |

GD | |||||||

L1. | 0.166852 | 0.06545 | 2.55 | 0.011 | 0.038572 | 0.295132 | |

HFC | |||||||

L1. | −0.0451 | 0.145242 | −0.31 | 0.756 | −0.32977 | 0.239573 | |

GFCE | |||||||

L1. | 0.616991 | 0.508445 | 1.21 | 0.225 | −0.37954 | 1.613524 | |

_cons | 3.80 × 10^{9} | 4.55 × 10^{9} | 0.840 | 0.404 | 0.456789 | 1.27 × 10^{10} | |

GD | |||||||

GDS | |||||||

L1. | −0.31434 | 0.233063 | −1.35 | 0.177 | −0.77113 | 0.142459 | |

GD | |||||||

L1. | 1.190752 | 0.067513 | 17.64 | 0 | 1.05843 | 1.323074 | |

HFC | |||||||

L1. | 0.024916 | 0.149819 | 0.17 | 0.868 | −0.26872 | 0.318557 | |

GFCE | |||||||

L1. | −0.12473 | 0.524469 | −0.24 | 0.812 | −1.15267 | 0.903213 | |

_cons | 7.16 × 10^{9} | 4.70 × 10^{9} | 1.53 | 0.127 | 1.64 × 10^{10} | ||

HFC | |||||||

GDS | |||||||

L1. | −0.6244 | 0.481732 | −1.3 | 0.195 | −1.56857 | 0.319781 | |

GD | |||||||

L1. | 0.566603 | 0.139546 | 4.06 | 0 | 0.293098 | 0.840107 | |

HFC | |||||||

L1. | 0.563406 | 0.30967 | 1.82 | 0.069 | −0.04354 | 1.170348 | |

GFCE | |||||||

L1. | 1.503606 | 1.084054 | 1.39 | 0.165 | −0.6211 | 3.628313 | |

_cons | 1.59 × 10^{10} | 9.71 × 10^{9} | 1.64 | 0.102 | 0.89789 | 3.49 × 10^{10} | |

GFCE | |||||||

GDS | |||||||

L1. | 0.030714 | 0.094602 | 0.32 | 0.745 | −0.1547 | 0.21613 | |

GD | |||||||

L1. | 0.108039 | 0.027404 | 3.94 | 0 | 0.054329 | 0.16175 | |

HFC | |||||||

L1. | 0.002123 | 0.060813 | 0.03 | 0.972 | −0.11707 | 0.121313 | |

GFCE | |||||||

L1. | 0.705917 | 0.212885 | 3.32 | 0.001 | 0.288671 | 1.123164 | |

_cons | 0.987896 | 1.91 × 10^{9} | −0.26 | 0.794 | 0.56459 | 3.24 × 10^{9} |

Equation | Excluded | chi2 | df | Prob > chi2 |
---|---|---|---|---|

GDS | GD | 6.499 | 1 | 0.011 |

GDS | HFC | 0.0964 | 1 | 0.756 |

GDS | GFCE | 1.4725 | 1 | 0.225 |

GDS | ALL | 16.259 | 3 | 0.001 |

GD | GDS | 1.819 | 1 | 0.177 |

GD | HFC | 0.02766 | 1 | 0.868 |

GD | GFCE | 0.05656 | 1 | 0.812 |

GD | ALL | 18.307 | 3 | 0.000 |

HFC | GDS | 1.68 | 1 | 0.195 |

HFC | GD | 16.486 | 1 | 0.000 |

HFC | GFCE | 1.9238 | 1 | 0.165 |

HFC | ALL | 23.705 | 3 | 0.000 |

GFCE | GDS | 0.10541 | 1 | 0.745 |

GFCE | GD | 15.543 | 1 | 0.000 |

GFCE | HFC | 0.00122 | 1 | 0.972 |

GFCE | ALL | 21.37 | 3 | 0.000 |

Johansen Tests for Cointegration | ||||||
---|---|---|---|---|---|---|

Trend: Constant | Number of obs = 36 | |||||

Sample: 1982–2017 | Lags = 2 | |||||

5% | ||||||

maximum | trace | critical | ||||

rank | parms | LL | eigenvalue statistic | value | ||

0 | 6 | −1785.9538 | 25.3775 | 15.41 | ||

1 | 9 | −1774.9352 | 0.45781 | 3.3404 | 3.76 | |

2 | 10 | −1773.265 | 0.08861 | |||

5% | ||||||

maximum | max | critical | ||||

rank | parms | LL | eigenvalue statistic | value | ||

0 | 6 | −1785.9538 | 22.0371 | 14.07 | ||

1 | 9 | −1774.9352 | 0.45781 | 3.3404 | 3.76 | |

2 | 10 | −1773.265 | 0.08861 | |||

Vector error-correction model | ||||||

Sample: 1983–2017 | No. of obs = 35 | |||||

AIC = −1.246467 | ||||||

Log likelihood = 30.81317 | HQIC = −1.108405 | |||||

Det (Sigma_ml) = 0.0005893 | SBIC = −0.8465201 | |||||

Equation | Parms | RMSE | R-sq | chi2 | p > chi2 | |

D_dlogGDS | 4 | 0.203618 | 0.6350 | 53.94156 | 0.0000 | |

D_dlogGD | 4 | 0.135205 | 0.2910 | 12.72565 | 0.0127 | |

Coef. | Std. Err. | z | p > |z| | [95% Conf. Interval] | ||

D_dlogGDS | ||||||

_ce1 | ||||||

L1. | −1.332819 | 0.2847097 | −4.68 | 0.000 | −1.89084 | −0.7747987 |

dlogGDS | ||||||

LD. | 0.0550099 | 0.1785852 | 0.31 | 0.758 | −0.2950107 | 0.4050306 |

dlogGD | ||||||

LD. | −0.1205058 | 0.2328727 | −0.52 | 0.605 | −0.5769279 | 0.3359164 |

_cons | 0.0003087 | 0.0344236 | 0.01 | 0.993 | −0.0671603 | 0.0677777 |

D_dlogGD | ||||||

_ce1 | ||||||

L1. | −0.1220723 | 0.1890521 | −0.65 | 0.518 | −0.4926075 | 0.248463 |

dlogGDS | ||||||

LD. | 0.0737549 | 0.1185836 | 0.62 | 0.534 | −0.1586648 | 0.3061745 |

dlogGD | ||||||

LD. | .5507026 | 0.1546314 | −3.56 | 0.000 | −0.8537746 | −0.2476305 |

_cons | −0.0033707 | 0.0228578 | −0.15 | 0.883 | −0.0481712 | 0.0414298 |

Cointegrating equations | ||||||

Equation | Parms | chi2 | p > chi2 | |||

_ce1 | 1 | 0.4366088 | 0.5088 | |||

Identification: beta is exactly identified | ||||||

Johansen normalization restriction imposed | ||||||

beta | Coef. | Std. Err. | z | p > |z| | [95% Conf. Interval] | |

_ce1 | ||||||

dlogGDS | 1 | |||||

dlogGD | −0.2133542 | 0.3228902 | −0.66 | 0.509 | −0.8462074 | 0.4194991 |

_cons | −0.0523882 |

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Author, Year | Aim of The Study | Methodology | Findings |
---|---|---|---|

(Ayşe and Levent 2016) | Testing the validity of twin deficit hypothesis for 2001–2014 | Toda Yamamoto test is used to examine the validity of the twin deficit hypothesis. | Unidirectional causality is found in the study which does not support the Keynesian general Theory. On the other hand, bidirectional relationship is found between budget deficit and interest rates. This eventually impacts the level of consumer spending in the economy. |

Causality relation is derived between government’s budget deficit, foreign trade deficit, exchange rate and interest rate. | |||

(Varol Iyidogan 2013) | Examining the validity of twin deficit relation in Turkey. | Budget Account and current account balance relationship has been analysed using descriptive statistics of the time series, stationarity of the series has been analysed using Zivot- Andrews test and lastly Causality test has been applied. | Current account balance and budget account balance are negatively related. |

(Bilgili 1997) | To test the Ricardian Equivalence Theorem of income, taxes, debt (or deficit) on the level of private consumption. | Dickey–Fuller’s unit root tests is used first followed by VAR forecast method and finally the study uses co integration test by Johansen. | The variables are related to each other, but this relation is not constant. |

No long-term relationship could be found between the effects of income, taxes and deficit on the level of private consumption. | |||

(Ricciuti 2001) | Testing the role of stochastic models and intertemporal government budget deficit in proving Ricardian Equivalence theorem. | Permanent Income Hypothesis and Eulers equation test | The theorem is the most contested one and studies concentrating on debt neutrality may find the prevalence of the theorem |

(Darrat 2006) | Examining the intertemporal relationship between government spending and taxation in Turkey | Stationarity tests using ADF, PP AND WS unit root procedure | Long-run equilibrium relationship between government spending and taxation is found in Turkey which eventually impacted the govt spending and the taxes Increase in taxes would curb the deficit in current account. |

Johansen cointegration technique is applied to variables | |||

Granger Causality test is used on variables. | |||

(Odim et al. 2014) | To examine the relation between government deficits and interest rate, in Nigeria | VAR analysis and VECM test has been used | Government deficit and interest rate were found to be related in the short-run. |

Variables | p-Value |
---|---|

GDS | 0.6742 |

GD | 0.2723 |

HFC | 0.6306 |

GFCE | 0.7060 |

logGDS | 0.1857 |

logGD | 0.9963 |

logHFC | 0.3697 |

logGFCE | 0.3309 |

dlogGDS | 0.0000 |

dlogGD | 0.0000 |

dlogHFC | 0.0000 |

dlogHFC | 0.0000 |

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İkiz, A.S.
Testing the Ricardian Equivalence Theorem: Time Series Evidence from Turkey. *Economies* **2020**, *8*, 69.
https://doi.org/10.3390/economies8030069

**AMA Style**

İkiz AS.
Testing the Ricardian Equivalence Theorem: Time Series Evidence from Turkey. *Economies*. 2020; 8(3):69.
https://doi.org/10.3390/economies8030069

**Chicago/Turabian Style**

İkiz, Ahmet Salih.
2020. "Testing the Ricardian Equivalence Theorem: Time Series Evidence from Turkey" *Economies* 8, no. 3: 69.
https://doi.org/10.3390/economies8030069